# Pipelines

## Search Pipelines

**get** `/api/v1/pipelines`

Search for pipelines by name, type, or project.

### Query Parameters

- `organization_id: optional string`

- `pipeline_name: optional string`

- `pipeline_type: optional PipelineType`

  Enum for representing the type of a pipeline

  - `"PLAYGROUND"`

  - `"MANAGED"`

- `project_id: optional string`

- `project_name: optional string`

### Cookie Parameters

- `session: optional string`

### Returns

- `id: string`

  Unique identifier

- `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

  - `ManagedOpenAIEmbedding = object { component, type }`

    - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

      Configuration for the Managed OpenAI embedding model.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `model_name: optional "openai-text-embedding-3-small"`

        The name of the OpenAI embedding model.

        - `"openai-text-embedding-3-small"`

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

    - `type: optional "MANAGED_OPENAI_EMBEDDING"`

      Type of the embedding model.

      - `"MANAGED_OPENAI_EMBEDDING"`

  - `AzureOpenAIEmbeddingConfig = object { component, type }`

    - `component: optional AzureOpenAIEmbedding`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for Azure deployment.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for Azure OpenAI API.

      - `azure_deployment: optional string`

        The Azure deployment to use.

      - `azure_endpoint: optional string`

        The Azure endpoint to use.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "AZURE_EMBEDDING"`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `CohereEmbeddingConfig = object { component, type }`

    - `component: optional CohereEmbedding`

      Configuration for the Cohere embedding model.

      - `api_key: string`

        The Cohere API key.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embedding_type: optional string`

        Embedding type. If not provided float embedding_type is used when needed.

      - `input_type: optional string`

        Model Input type. If not provided, search_document and search_query are used when needed.

      - `model_name: optional string`

        The modelId of the Cohere model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `truncate: optional string`

        Truncation type - START/ END/ NONE

    - `type: optional "COHERE_EMBEDDING"`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `GeminiEmbeddingConfig = object { component, type }`

    - `component: optional GeminiEmbedding`

      Configuration for the Gemini embedding model.

      - `api_base: optional string`

        API base to access the model. Defaults to None.

      - `api_key: optional string`

        API key to access the model. Defaults to None.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `model_name: optional string`

        The modelId of the Gemini model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: optional number`

        Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

      - `task_type: optional string`

        The task for embedding model.

      - `title: optional string`

        Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

      - `transport: optional string`

        Transport to access the model. Defaults to None.

    - `type: optional "GEMINI_EMBEDDING"`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

    - `component: optional HuggingFaceInferenceAPIEmbedding`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: optional string or boolean`

        Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

        - `string`

        - `boolean`

      - `class_name: optional string`

      - `cookies: optional map[string]`

        Additional cookies to send to the server.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `headers: optional map[string]`

        Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

      - `model_name: optional string`

        Hugging Face model name. If None, the task will be used.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `pooling: optional "cls" or "mean" or "last"`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: optional string`

        Instruction to prepend during query embedding.

      - `task: optional string`

        Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

      - `text_instruction: optional string`

        Instruction to prepend during text embedding.

      - `timeout: optional number`

        The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

    - `type: optional "HUGGINGFACE_API_EMBEDDING"`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `OpenAIEmbeddingConfig = object { component, type }`

    - `component: optional OpenAIEmbedding`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for OpenAI API.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for OpenAI API.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "OPENAI_EMBEDDING"`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `VertexAIEmbeddingConfig = object { component, type }`

    - `component: optional VertexTextEmbedding`

      Configuration for the VertexAI embedding model.

      - `client_email: string`

        The client email for the VertexAI credentials.

      - `location: string`

        The default location to use when making API calls.

      - `private_key: string`

        The private key for the VertexAI credentials.

      - `private_key_id: string`

        The private key ID for the VertexAI credentials.

      - `project: string`

        The default GCP project to use when making Vertex API calls.

      - `token_uri: string`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the Vertex.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: optional string`

        The modelId of the VertexAI model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

    - `type: optional "VERTEXAI_EMBEDDING"`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `BedrockEmbeddingConfig = object { component, type }`

    - `component: optional BedrockEmbedding`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: optional string`

        AWS Access Key ID to use

      - `aws_secret_access_key: optional string`

        AWS Secret Access Key to use

      - `aws_session_token: optional string`

        AWS Session Token to use

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        The maximum number of API retries.

      - `model_name: optional string`

        The modelId of the Bedrock model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `profile_name: optional string`

        The name of aws profile to use. If not given, then the default profile is used.

      - `region_name: optional string`

        AWS region name to use. Uses region configured in AWS CLI if not passed

      - `timeout: optional number`

        The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

    - `type: optional "BEDROCK_EMBEDDING"`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `name: string`

- `project_id: string`

- `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

  Hashes for the configuration of a pipeline.

  - `embedding_config_hash: optional string`

    Hash of the embedding config.

  - `parsing_config_hash: optional string`

    Hash of the llama parse parameters.

  - `transform_config_hash: optional string`

    Hash of the transform config.

- `created_at: optional string`

  Creation datetime

- `data_sink: optional DataSink`

  Schema for a data sink.

  - `id: string`

    Unique identifier

  - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

    Component that implements the data sink

    - `map[unknown]`

    - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

      Cloud Pinecone Vector Store.

      This class is used to store the configuration for a Pinecone vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      api_key (str): API key for authenticating with Pinecone
      index_name (str): name of the Pinecone index
      namespace (optional[str]): namespace to use in the Pinecone index
      insert_kwargs (optional[dict]): additional kwargs to pass during insertion

      - `api_key: string`

        The API key for authenticating with Pinecone

      - `index_name: string`

      - `class_name: optional string`

      - `insert_kwargs: optional map[unknown]`

      - `namespace: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

      - `database: string`

      - `embed_dim: number`

      - `host: string`

      - `password: string`

      - `port: number`

      - `schema_name: string`

      - `table_name: string`

      - `user: string`

      - `class_name: optional string`

      - `hnsw_settings: optional PgVectorHnswSettings`

        HNSW settings for PGVector.

        - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: optional number`

          The number of edges to use during the construction phase.

        - `ef_search: optional number`

          The number of edges to use during the search phase.

        - `m: optional number`

          The number of bi-directional links created for each new element.

        - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: optional boolean`

      - `perform_setup: optional boolean`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

      Cloud Qdrant Vector Store.

      This class is used to store the configuration for a Qdrant vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      collection_name (str): name of the Qdrant collection
      url (str): url of the Qdrant instance
      api_key (str): API key for authenticating with Qdrant
      max_retries (int): maximum number of retries in case of a failure. Defaults to 3
      client_kwargs (dict): additional kwargs to pass to the Qdrant client

      - `api_key: string`

      - `collection_name: string`

      - `url: string`

      - `class_name: optional string`

      - `client_kwargs: optional map[unknown]`

      - `max_retries: optional number`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: string`

      - `search_service_endpoint: string`

      - `class_name: optional string`

      - `client_id: optional string`

      - `client_secret: optional string`

      - `embedding_dimension: optional number`

      - `filterable_metadata_field_keys: optional map[unknown]`

      - `index_name: optional string`

      - `search_service_api_version: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

      - `tenant_id: optional string`

    - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

      Cloud MongoDB Atlas Vector Store.

      This class is used to store the configuration for a MongoDB Atlas vector store,
      so that it can be created and used in LlamaCloud.

      Args:
      mongodb_uri (str): URI for connecting to MongoDB Atlas
      db_name (str): name of the MongoDB database
      collection_name (str): name of the MongoDB collection
      vector_index_name (str): name of the MongoDB Atlas vector index
      fulltext_index_name (str): name of the MongoDB Atlas full-text index

      - `collection_name: string`

      - `db_name: string`

      - `mongodb_uri: string`

      - `class_name: optional string`

      - `embedding_dimension: optional number`

      - `fulltext_index_name: optional string`

      - `supports_nested_metadata_filters: optional boolean`

      - `vector_index_name: optional string`

    - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

      Cloud Milvus Vector Store.

      - `uri: string`

      - `token: optional string`

      - `class_name: optional string`

      - `collection_name: optional string`

      - `embedding_dimension: optional number`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

      Cloud AstraDB Vector Store.

      This class is used to store the configuration for an AstraDB vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      token (str): The Astra DB Application Token to use.
      api_endpoint (str): The Astra DB JSON API endpoint for your database.
      collection_name (str): Collection name to use. If not existing, it will be created.
      embedding_dimension (int): Length of the embedding vectors in use.
      keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

      - `token: string`

        The Astra DB Application Token to use

      - `api_endpoint: string`

        The Astra DB JSON API endpoint for your database

      - `collection_name: string`

        Collection name to use. If not existing, it will be created

      - `embedding_dimension: number`

        Length of the embedding vectors in use

      - `class_name: optional string`

      - `keyspace: optional string`

        The keyspace to use. If not provided, 'default_keyspace'

      - `supports_nested_metadata_filters: optional true`

        - `true`

  - `name: string`

    The name of the data sink.

  - `project_id: string`

  - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

  - `created_at: optional string`

    Creation datetime

  - `updated_at: optional string`

    Update datetime

- `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

  Schema for an embedding model config.

  - `id: string`

    Unique identifier

  - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

    The embedding configuration for the embedding model config.

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

    - `CohereEmbeddingConfig = object { component, type }`

    - `GeminiEmbeddingConfig = object { component, type }`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

    - `OpenAIEmbeddingConfig = object { component, type }`

    - `VertexAIEmbeddingConfig = object { component, type }`

    - `BedrockEmbeddingConfig = object { component, type }`

  - `name: string`

    The name of the embedding model config.

  - `project_id: string`

  - `created_at: optional string`

    Creation datetime

  - `updated_at: optional string`

    Update datetime

- `embedding_model_config_id: optional string`

  The ID of the EmbeddingModelConfig this pipeline is using.

- `llama_parse_parameters: optional LlamaParseParameters`

  Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

  - `adaptive_long_table: optional boolean`

  - `aggressive_table_extraction: optional boolean`

  - `annotate_links: optional boolean`

  - `auto_mode: optional boolean`

  - `auto_mode_configuration_json: optional string`

  - `auto_mode_trigger_on_image_in_page: optional boolean`

  - `auto_mode_trigger_on_regexp_in_page: optional string`

  - `auto_mode_trigger_on_table_in_page: optional boolean`

  - `auto_mode_trigger_on_text_in_page: optional string`

  - `azure_openai_api_version: optional string`

  - `azure_openai_deployment_name: optional string`

  - `azure_openai_endpoint: optional string`

  - `azure_openai_key: optional string`

  - `bbox_bottom: optional number`

  - `bbox_left: optional number`

  - `bbox_right: optional number`

  - `bbox_top: optional number`

  - `bounding_box: optional string`

  - `compact_markdown_table: optional boolean`

  - `complemental_formatting_instruction: optional string`

  - `content_guideline_instruction: optional string`

  - `continuous_mode: optional boolean`

  - `disable_image_extraction: optional boolean`

  - `disable_ocr: optional boolean`

  - `disable_reconstruction: optional boolean`

  - `do_not_cache: optional boolean`

  - `do_not_unroll_columns: optional boolean`

  - `enable_cost_optimizer: optional boolean`

  - `extract_charts: optional boolean`

  - `extract_layout: optional boolean`

  - `extract_printed_page_number: optional boolean`

  - `fast_mode: optional boolean`

  - `formatting_instruction: optional string`

  - `gpt4o_api_key: optional string`

  - `gpt4o_mode: optional boolean`

  - `guess_xlsx_sheet_name: optional boolean`

  - `hide_footers: optional boolean`

  - `hide_headers: optional boolean`

  - `high_res_ocr: optional boolean`

  - `html_make_all_elements_visible: optional boolean`

  - `html_remove_fixed_elements: optional boolean`

  - `html_remove_navigation_elements: optional boolean`

  - `http_proxy: optional string`

  - `ignore_document_elements_for_layout_detection: optional boolean`

  - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: optional boolean`

  - `input_s3_path: optional string`

  - `input_s3_region: optional string`

  - `input_url: optional string`

  - `internal_is_screenshot_job: optional boolean`

  - `invalidate_cache: optional boolean`

  - `is_formatting_instruction: optional boolean`

  - `job_timeout_extra_time_per_page_in_seconds: optional number`

  - `job_timeout_in_seconds: optional number`

  - `keep_page_separator_when_merging_tables: optional boolean`

  - `languages: optional array of ParsingLanguages`

    - `"af"`

    - `"az"`

    - `"bs"`

    - `"cs"`

    - `"cy"`

    - `"da"`

    - `"de"`

    - `"en"`

    - `"es"`

    - `"et"`

    - `"fr"`

    - `"ga"`

    - `"hr"`

    - `"hu"`

    - `"id"`

    - `"is"`

    - `"it"`

    - `"ku"`

    - `"la"`

    - `"lt"`

    - `"lv"`

    - `"mi"`

    - `"ms"`

    - `"mt"`

    - `"nl"`

    - `"no"`

    - `"oc"`

    - `"pi"`

    - `"pl"`

    - `"pt"`

    - `"ro"`

    - `"rs_latin"`

    - `"sk"`

    - `"sl"`

    - `"sq"`

    - `"sv"`

    - `"sw"`

    - `"tl"`

    - `"tr"`

    - `"uz"`

    - `"vi"`

    - `"ar"`

    - `"fa"`

    - `"ug"`

    - `"ur"`

    - `"bn"`

    - `"as"`

    - `"mni"`

    - `"ru"`

    - `"rs_cyrillic"`

    - `"be"`

    - `"bg"`

    - `"uk"`

    - `"mn"`

    - `"abq"`

    - `"ady"`

    - `"kbd"`

    - `"ava"`

    - `"dar"`

    - `"inh"`

    - `"che"`

    - `"lbe"`

    - `"lez"`

    - `"tab"`

    - `"tjk"`

    - `"hi"`

    - `"mr"`

    - `"ne"`

    - `"bh"`

    - `"mai"`

    - `"ang"`

    - `"bho"`

    - `"mah"`

    - `"sck"`

    - `"new"`

    - `"gom"`

    - `"sa"`

    - `"bgc"`

    - `"th"`

    - `"ch_sim"`

    - `"ch_tra"`

    - `"ja"`

    - `"ko"`

    - `"ta"`

    - `"te"`

    - `"kn"`

  - `layout_aware: optional boolean`

  - `line_level_bounding_box: optional boolean`

  - `markdown_table_multiline_header_separator: optional string`

  - `max_pages: optional number`

  - `max_pages_enforced: optional number`

  - `merge_tables_across_pages_in_markdown: optional boolean`

  - `model: optional string`

  - `outlined_table_extraction: optional boolean`

  - `output_pdf_of_document: optional boolean`

  - `output_s3_path_prefix: optional string`

  - `output_s3_region: optional string`

  - `output_tables_as_HTML: optional boolean`

  - `page_error_tolerance: optional number`

  - `page_footer_prefix: optional string`

  - `page_footer_suffix: optional string`

  - `page_header_prefix: optional string`

  - `page_header_suffix: optional string`

  - `page_prefix: optional string`

  - `page_separator: optional string`

  - `page_suffix: optional string`

  - `parse_mode: optional ParsingMode`

    Enum for representing the mode of parsing to be used.

    - `"parse_page_without_llm"`

    - `"parse_page_with_llm"`

    - `"parse_page_with_lvm"`

    - `"parse_page_with_agent"`

    - `"parse_page_with_layout_agent"`

    - `"parse_document_with_llm"`

    - `"parse_document_with_lvm"`

    - `"parse_document_with_agent"`

  - `parsing_instruction: optional string`

  - `precise_bounding_box: optional boolean`

  - `premium_mode: optional boolean`

  - `presentation_out_of_bounds_content: optional boolean`

  - `presentation_skip_embedded_data: optional boolean`

  - `preserve_layout_alignment_across_pages: optional boolean`

  - `preserve_very_small_text: optional boolean`

  - `preset: optional string`

  - `priority: optional "low" or "medium" or "high" or "critical"`

    The priority for the request. This field may be ignored or overwritten depending on the organization tier.

    - `"low"`

    - `"medium"`

    - `"high"`

    - `"critical"`

  - `project_id: optional string`

  - `remove_hidden_text: optional boolean`

  - `replace_failed_page_mode: optional FailPageMode`

    Enum for representing the different available page error handling modes.

    - `"raw_text"`

    - `"blank_page"`

    - `"error_message"`

  - `replace_failed_page_with_error_message_prefix: optional string`

  - `replace_failed_page_with_error_message_suffix: optional string`

  - `save_images: optional boolean`

  - `skip_diagonal_text: optional boolean`

  - `specialized_chart_parsing_agentic: optional boolean`

  - `specialized_chart_parsing_efficient: optional boolean`

  - `specialized_chart_parsing_plus: optional boolean`

  - `specialized_image_parsing: optional boolean`

  - `spreadsheet_extract_sub_tables: optional boolean`

  - `spreadsheet_force_formula_computation: optional boolean`

  - `spreadsheet_include_hidden_sheets: optional boolean`

  - `strict_mode_buggy_font: optional boolean`

  - `strict_mode_image_extraction: optional boolean`

  - `strict_mode_image_ocr: optional boolean`

  - `strict_mode_reconstruction: optional boolean`

  - `structured_output: optional boolean`

  - `structured_output_json_schema: optional string`

  - `structured_output_json_schema_name: optional string`

  - `system_prompt: optional string`

  - `system_prompt_append: optional string`

  - `take_screenshot: optional boolean`

  - `target_pages: optional string`

  - `tier: optional string`

  - `use_vendor_multimodal_model: optional boolean`

  - `user_prompt: optional string`

  - `vendor_multimodal_api_key: optional string`

  - `vendor_multimodal_model_name: optional string`

  - `version: optional string`

  - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

      Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

      - `"extract.pending"`

      - `"extract.success"`

      - `"extract.error"`

      - `"extract.partial_success"`

      - `"extract.cancelled"`

      - `"parse.pending"`

      - `"parse.running"`

      - `"parse.success"`

      - `"parse.error"`

      - `"parse.partial_success"`

      - `"parse.cancelled"`

      - `"classify.pending"`

      - `"classify.success"`

      - `"classify.error"`

      - `"classify.partial_success"`

      - `"classify.cancelled"`

      - `"unmapped_event"`

    - `webhook_headers: optional map[string]`

      Custom HTTP headers sent with each webhook request (e.g. auth tokens)

    - `webhook_output_format: optional string`

      Response format sent to the webhook: 'string' (default) or 'json'

    - `webhook_url: optional string`

      URL to receive webhook POST notifications

  - `webhook_url: optional string`

- `managed_pipeline_id: optional string`

  The ID of the ManagedPipeline this playground pipeline is linked to.

- `metadata_config: optional PipelineMetadataConfig`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: optional array of string`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: optional array of string`

    List of metadata keys to exclude from LLM during retrieval

- `pipeline_type: optional PipelineType`

  Type of pipeline. Either PLAYGROUND or MANAGED.

  - `"PLAYGROUND"`

  - `"MANAGED"`

- `preset_retrieval_parameters: optional PresetRetrievalParams`

  Preset retrieval parameters for the pipeline.

  - `alpha: optional number`

    Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

  - `class_name: optional string`

  - `dense_similarity_cutoff: optional number`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: optional number`

    Number of nodes for dense retrieval.

  - `enable_reranking: optional boolean`

    Enable reranking for retrieval

  - `files_top_k: optional number`

    Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

  - `rerank_top_n: optional number`

    Number of reranked nodes for returning.

  - `retrieval_mode: optional RetrievalMode`

    The retrieval mode for the query.

    - `"chunks"`

    - `"files_via_metadata"`

    - `"files_via_content"`

    - `"auto_routed"`

  - `retrieve_image_nodes: optional boolean`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: optional boolean`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: optional boolean`

    Whether to retrieve page screenshot nodes.

  - `search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

    JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `sparse_similarity_top_k: optional number`

    Number of nodes for sparse retrieval.

- `sparse_model_config: optional SparseModelConfig`

  Configuration for sparse embedding models used in hybrid search.

  This allows users to choose between Splade and BM25 models for
  sparse retrieval in managed data sinks.

  - `class_name: optional string`

  - `model_type: optional "splade" or "bm25" or "auto"`

    The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

    - `"splade"`

    - `"bm25"`

    - `"auto"`

- `status: optional "CREATED" or "DELETING"`

  Status of the pipeline.

  - `"CREATED"`

  - `"DELETING"`

- `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

  Configuration for the transformation.

  - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

    - `chunk_overlap: optional number`

      Chunk overlap for the transformation.

    - `chunk_size: optional number`

      Chunk size for the transformation.

    - `mode: optional "auto"`

      - `"auto"`

  - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

    - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

      Configuration for the chunking.

      - `NoneChunkingConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "character"`

          - `"character"`

      - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "token"`

          - `"token"`

        - `separator: optional string`

      - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "sentence"`

          - `"sentence"`

        - `paragraph_separator: optional string`

        - `separator: optional string`

      - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

        - `breakpoint_percentile_threshold: optional number`

        - `buffer_size: optional number`

        - `mode: optional "semantic"`

          - `"semantic"`

    - `mode: optional "advanced"`

      - `"advanced"`

    - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

      Configuration for the segmentation.

      - `NoneSegmentationConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `PageSegmentationConfig = object { mode, page_separator }`

        - `mode: optional "page"`

          - `"page"`

        - `page_separator: optional string`

      - `ElementSegmentationConfig = object { mode }`

        - `mode: optional "element"`

          - `"element"`

- `updated_at: optional string`

  Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "class_name": "class_name",
        "embed_batch_size": 1,
        "model_name": "openai-text-embedding-3-small",
        "num_workers": 0
      },
      "type": "MANAGED_OPENAI_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "config_hash": {
      "embedding_config_hash": "embedding_config_hash",
      "parsing_config_hash": "parsing_config_hash",
      "transform_config_hash": "transform_config_hash"
    },
    "created_at": "2019-12-27T18:11:19.117Z",
    "data_sink": {
      "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "component": {
        "foo": "bar"
      },
      "name": "name",
      "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "sink_type": "PINECONE",
      "created_at": "2019-12-27T18:11:19.117Z",
      "updated_at": "2019-12-27T18:11:19.117Z"
    },
    "embedding_model_config": {
      "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "embedding_config": {
        "component": {
          "additional_kwargs": {
            "foo": "bar"
          },
          "api_base": "api_base",
          "api_key": "api_key",
          "api_version": "api_version",
          "azure_deployment": "azure_deployment",
          "azure_endpoint": "azure_endpoint",
          "class_name": "class_name",
          "default_headers": {
            "foo": "string"
          },
          "dimensions": 0,
          "embed_batch_size": 1,
          "max_retries": 0,
          "model_name": "model_name",
          "num_workers": 0,
          "reuse_client": true,
          "timeout": 0
        },
        "type": "AZURE_EMBEDDING"
      },
      "name": "name",
      "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "created_at": "2019-12-27T18:11:19.117Z",
      "updated_at": "2019-12-27T18:11:19.117Z"
    },
    "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "llama_parse_parameters": {
      "adaptive_long_table": true,
      "aggressive_table_extraction": true,
      "annotate_links": true,
      "auto_mode": true,
      "auto_mode_configuration_json": "auto_mode_configuration_json",
      "auto_mode_trigger_on_image_in_page": true,
      "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
      "auto_mode_trigger_on_table_in_page": true,
      "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
      "azure_openai_api_version": "azure_openai_api_version",
      "azure_openai_deployment_name": "azure_openai_deployment_name",
      "azure_openai_endpoint": "azure_openai_endpoint",
      "azure_openai_key": "azure_openai_key",
      "bbox_bottom": 0,
      "bbox_left": 0,
      "bbox_right": 0,
      "bbox_top": 0,
      "bounding_box": "bounding_box",
      "compact_markdown_table": true,
      "complemental_formatting_instruction": "complemental_formatting_instruction",
      "content_guideline_instruction": "content_guideline_instruction",
      "continuous_mode": true,
      "disable_image_extraction": true,
      "disable_ocr": true,
      "disable_reconstruction": true,
      "do_not_cache": true,
      "do_not_unroll_columns": true,
      "enable_cost_optimizer": true,
      "extract_charts": true,
      "extract_layout": true,
      "extract_printed_page_number": true,
      "fast_mode": true,
      "formatting_instruction": "formatting_instruction",
      "gpt4o_api_key": "gpt4o_api_key",
      "gpt4o_mode": true,
      "guess_xlsx_sheet_name": true,
      "hide_footers": true,
      "hide_headers": true,
      "high_res_ocr": true,
      "html_make_all_elements_visible": true,
      "html_remove_fixed_elements": true,
      "html_remove_navigation_elements": true,
      "http_proxy": "http_proxy",
      "ignore_document_elements_for_layout_detection": true,
      "images_to_save": [
        "screenshot"
      ],
      "inline_images_in_markdown": true,
      "input_s3_path": "input_s3_path",
      "input_s3_region": "input_s3_region",
      "input_url": "input_url",
      "internal_is_screenshot_job": true,
      "invalidate_cache": true,
      "is_formatting_instruction": true,
      "job_timeout_extra_time_per_page_in_seconds": 0,
      "job_timeout_in_seconds": 0,
      "keep_page_separator_when_merging_tables": true,
      "languages": [
        "af"
      ],
      "layout_aware": true,
      "line_level_bounding_box": true,
      "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
      "max_pages": 0,
      "max_pages_enforced": 0,
      "merge_tables_across_pages_in_markdown": true,
      "model": "model",
      "outlined_table_extraction": true,
      "output_pdf_of_document": true,
      "output_s3_path_prefix": "output_s3_path_prefix",
      "output_s3_region": "output_s3_region",
      "output_tables_as_HTML": true,
      "page_error_tolerance": 0,
      "page_footer_prefix": "page_footer_prefix",
      "page_footer_suffix": "page_footer_suffix",
      "page_header_prefix": "page_header_prefix",
      "page_header_suffix": "page_header_suffix",
      "page_prefix": "page_prefix",
      "page_separator": "page_separator",
      "page_suffix": "page_suffix",
      "parse_mode": "parse_page_without_llm",
      "parsing_instruction": "parsing_instruction",
      "precise_bounding_box": true,
      "premium_mode": true,
      "presentation_out_of_bounds_content": true,
      "presentation_skip_embedded_data": true,
      "preserve_layout_alignment_across_pages": true,
      "preserve_very_small_text": true,
      "preset": "preset",
      "priority": "low",
      "project_id": "project_id",
      "remove_hidden_text": true,
      "replace_failed_page_mode": "raw_text",
      "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
      "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
      "save_images": true,
      "skip_diagonal_text": true,
      "specialized_chart_parsing_agentic": true,
      "specialized_chart_parsing_efficient": true,
      "specialized_chart_parsing_plus": true,
      "specialized_image_parsing": true,
      "spreadsheet_extract_sub_tables": true,
      "spreadsheet_force_formula_computation": true,
      "spreadsheet_include_hidden_sheets": true,
      "strict_mode_buggy_font": true,
      "strict_mode_image_extraction": true,
      "strict_mode_image_ocr": true,
      "strict_mode_reconstruction": true,
      "structured_output": true,
      "structured_output_json_schema": "structured_output_json_schema",
      "structured_output_json_schema_name": "structured_output_json_schema_name",
      "system_prompt": "system_prompt",
      "system_prompt_append": "system_prompt_append",
      "take_screenshot": true,
      "target_pages": "target_pages",
      "tier": "tier",
      "use_vendor_multimodal_model": true,
      "user_prompt": "user_prompt",
      "vendor_multimodal_api_key": "vendor_multimodal_api_key",
      "vendor_multimodal_model_name": "vendor_multimodal_model_name",
      "version": "version",
      "webhook_configurations": [
        {
          "webhook_events": [
            "parse.success",
            "parse.error"
          ],
          "webhook_headers": {
            "Authorization": "Bearer sk-..."
          },
          "webhook_output_format": "json",
          "webhook_url": "https://example.com/webhooks/llamacloud"
        }
      ],
      "webhook_url": "webhook_url"
    },
    "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "metadata_config": {
      "excluded_embed_metadata_keys": [
        "string"
      ],
      "excluded_llm_metadata_keys": [
        "string"
      ]
    },
    "pipeline_type": "PLAYGROUND",
    "preset_retrieval_parameters": {
      "alpha": 0,
      "class_name": "class_name",
      "dense_similarity_cutoff": 0,
      "dense_similarity_top_k": 1,
      "enable_reranking": true,
      "files_top_k": 1,
      "rerank_top_n": 1,
      "retrieval_mode": "chunks",
      "retrieve_image_nodes": true,
      "retrieve_page_figure_nodes": true,
      "retrieve_page_screenshot_nodes": true,
      "search_filters": {
        "filters": [
          {
            "key": "key",
            "value": 0,
            "operator": "=="
          }
        ],
        "condition": "and"
      },
      "search_filters_inference_schema": {
        "foo": {
          "foo": "bar"
        }
      },
      "sparse_similarity_top_k": 1
    },
    "sparse_model_config": {
      "class_name": "class_name",
      "model_type": "splade"
    },
    "status": "CREATED",
    "transform_config": {
      "chunk_overlap": 0,
      "chunk_size": 1,
      "mode": "auto"
    },
    "updated_at": "2019-12-27T18:11:19.117Z"
  }
]
```

## Create Pipeline

**post** `/api/v1/pipelines`

Create a new managed ingestion pipeline.

A pipeline connects data sources to a vector store for RAG.
After creation, call `POST /pipelines/{id}/sync` to start
ingesting documents.

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `name: string`

- `data_sink: optional DataSinkCreate`

  Schema for creating a data sink.

  - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

    Component that implements the data sink

    - `map[unknown]`

    - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

      Cloud Pinecone Vector Store.

      This class is used to store the configuration for a Pinecone vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      api_key (str): API key for authenticating with Pinecone
      index_name (str): name of the Pinecone index
      namespace (optional[str]): namespace to use in the Pinecone index
      insert_kwargs (optional[dict]): additional kwargs to pass during insertion

      - `api_key: string`

        The API key for authenticating with Pinecone

      - `index_name: string`

      - `class_name: optional string`

      - `insert_kwargs: optional map[unknown]`

      - `namespace: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

      - `database: string`

      - `embed_dim: number`

      - `host: string`

      - `password: string`

      - `port: number`

      - `schema_name: string`

      - `table_name: string`

      - `user: string`

      - `class_name: optional string`

      - `hnsw_settings: optional PgVectorHnswSettings`

        HNSW settings for PGVector.

        - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: optional number`

          The number of edges to use during the construction phase.

        - `ef_search: optional number`

          The number of edges to use during the search phase.

        - `m: optional number`

          The number of bi-directional links created for each new element.

        - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: optional boolean`

      - `perform_setup: optional boolean`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

      Cloud Qdrant Vector Store.

      This class is used to store the configuration for a Qdrant vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      collection_name (str): name of the Qdrant collection
      url (str): url of the Qdrant instance
      api_key (str): API key for authenticating with Qdrant
      max_retries (int): maximum number of retries in case of a failure. Defaults to 3
      client_kwargs (dict): additional kwargs to pass to the Qdrant client

      - `api_key: string`

      - `collection_name: string`

      - `url: string`

      - `class_name: optional string`

      - `client_kwargs: optional map[unknown]`

      - `max_retries: optional number`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: string`

      - `search_service_endpoint: string`

      - `class_name: optional string`

      - `client_id: optional string`

      - `client_secret: optional string`

      - `embedding_dimension: optional number`

      - `filterable_metadata_field_keys: optional map[unknown]`

      - `index_name: optional string`

      - `search_service_api_version: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

      - `tenant_id: optional string`

    - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

      Cloud MongoDB Atlas Vector Store.

      This class is used to store the configuration for a MongoDB Atlas vector store,
      so that it can be created and used in LlamaCloud.

      Args:
      mongodb_uri (str): URI for connecting to MongoDB Atlas
      db_name (str): name of the MongoDB database
      collection_name (str): name of the MongoDB collection
      vector_index_name (str): name of the MongoDB Atlas vector index
      fulltext_index_name (str): name of the MongoDB Atlas full-text index

      - `collection_name: string`

      - `db_name: string`

      - `mongodb_uri: string`

      - `class_name: optional string`

      - `embedding_dimension: optional number`

      - `fulltext_index_name: optional string`

      - `supports_nested_metadata_filters: optional boolean`

      - `vector_index_name: optional string`

    - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

      Cloud Milvus Vector Store.

      - `uri: string`

      - `token: optional string`

      - `class_name: optional string`

      - `collection_name: optional string`

      - `embedding_dimension: optional number`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

      Cloud AstraDB Vector Store.

      This class is used to store the configuration for an AstraDB vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      token (str): The Astra DB Application Token to use.
      api_endpoint (str): The Astra DB JSON API endpoint for your database.
      collection_name (str): Collection name to use. If not existing, it will be created.
      embedding_dimension (int): Length of the embedding vectors in use.
      keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

      - `token: string`

        The Astra DB Application Token to use

      - `api_endpoint: string`

        The Astra DB JSON API endpoint for your database

      - `collection_name: string`

        Collection name to use. If not existing, it will be created

      - `embedding_dimension: number`

        Length of the embedding vectors in use

      - `class_name: optional string`

      - `keyspace: optional string`

        The keyspace to use. If not provided, 'default_keyspace'

      - `supports_nested_metadata_filters: optional true`

        - `true`

  - `name: string`

    The name of the data sink.

  - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: optional string`

  Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.

- `embedding_config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

  - `AzureOpenAIEmbeddingConfig = object { component, type }`

    - `component: optional AzureOpenAIEmbedding`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for Azure deployment.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for Azure OpenAI API.

      - `azure_deployment: optional string`

        The Azure deployment to use.

      - `azure_endpoint: optional string`

        The Azure endpoint to use.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "AZURE_EMBEDDING"`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `CohereEmbeddingConfig = object { component, type }`

    - `component: optional CohereEmbedding`

      Configuration for the Cohere embedding model.

      - `api_key: string`

        The Cohere API key.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embedding_type: optional string`

        Embedding type. If not provided float embedding_type is used when needed.

      - `input_type: optional string`

        Model Input type. If not provided, search_document and search_query are used when needed.

      - `model_name: optional string`

        The modelId of the Cohere model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `truncate: optional string`

        Truncation type - START/ END/ NONE

    - `type: optional "COHERE_EMBEDDING"`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `GeminiEmbeddingConfig = object { component, type }`

    - `component: optional GeminiEmbedding`

      Configuration for the Gemini embedding model.

      - `api_base: optional string`

        API base to access the model. Defaults to None.

      - `api_key: optional string`

        API key to access the model. Defaults to None.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `model_name: optional string`

        The modelId of the Gemini model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: optional number`

        Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

      - `task_type: optional string`

        The task for embedding model.

      - `title: optional string`

        Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

      - `transport: optional string`

        Transport to access the model. Defaults to None.

    - `type: optional "GEMINI_EMBEDDING"`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

    - `component: optional HuggingFaceInferenceAPIEmbedding`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: optional string or boolean`

        Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

        - `string`

        - `boolean`

      - `class_name: optional string`

      - `cookies: optional map[string]`

        Additional cookies to send to the server.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `headers: optional map[string]`

        Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

      - `model_name: optional string`

        Hugging Face model name. If None, the task will be used.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `pooling: optional "cls" or "mean" or "last"`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: optional string`

        Instruction to prepend during query embedding.

      - `task: optional string`

        Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

      - `text_instruction: optional string`

        Instruction to prepend during text embedding.

      - `timeout: optional number`

        The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

    - `type: optional "HUGGINGFACE_API_EMBEDDING"`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `OpenAIEmbeddingConfig = object { component, type }`

    - `component: optional OpenAIEmbedding`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for OpenAI API.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for OpenAI API.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "OPENAI_EMBEDDING"`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `VertexAIEmbeddingConfig = object { component, type }`

    - `component: optional VertexTextEmbedding`

      Configuration for the VertexAI embedding model.

      - `client_email: string`

        The client email for the VertexAI credentials.

      - `location: string`

        The default location to use when making API calls.

      - `private_key: string`

        The private key for the VertexAI credentials.

      - `private_key_id: string`

        The private key ID for the VertexAI credentials.

      - `project: string`

        The default GCP project to use when making Vertex API calls.

      - `token_uri: string`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the Vertex.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: optional string`

        The modelId of the VertexAI model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

    - `type: optional "VERTEXAI_EMBEDDING"`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `BedrockEmbeddingConfig = object { component, type }`

    - `component: optional BedrockEmbedding`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: optional string`

        AWS Access Key ID to use

      - `aws_secret_access_key: optional string`

        AWS Secret Access Key to use

      - `aws_session_token: optional string`

        AWS Session Token to use

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        The maximum number of API retries.

      - `model_name: optional string`

        The modelId of the Bedrock model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `profile_name: optional string`

        The name of aws profile to use. If not given, then the default profile is used.

      - `region_name: optional string`

        AWS region name to use. Uses region configured in AWS CLI if not passed

      - `timeout: optional number`

        The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

    - `type: optional "BEDROCK_EMBEDDING"`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: optional string`

  Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.

- `llama_parse_parameters: optional LlamaParseParameters`

  Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

  - `adaptive_long_table: optional boolean`

  - `aggressive_table_extraction: optional boolean`

  - `annotate_links: optional boolean`

  - `auto_mode: optional boolean`

  - `auto_mode_configuration_json: optional string`

  - `auto_mode_trigger_on_image_in_page: optional boolean`

  - `auto_mode_trigger_on_regexp_in_page: optional string`

  - `auto_mode_trigger_on_table_in_page: optional boolean`

  - `auto_mode_trigger_on_text_in_page: optional string`

  - `azure_openai_api_version: optional string`

  - `azure_openai_deployment_name: optional string`

  - `azure_openai_endpoint: optional string`

  - `azure_openai_key: optional string`

  - `bbox_bottom: optional number`

  - `bbox_left: optional number`

  - `bbox_right: optional number`

  - `bbox_top: optional number`

  - `bounding_box: optional string`

  - `compact_markdown_table: optional boolean`

  - `complemental_formatting_instruction: optional string`

  - `content_guideline_instruction: optional string`

  - `continuous_mode: optional boolean`

  - `disable_image_extraction: optional boolean`

  - `disable_ocr: optional boolean`

  - `disable_reconstruction: optional boolean`

  - `do_not_cache: optional boolean`

  - `do_not_unroll_columns: optional boolean`

  - `enable_cost_optimizer: optional boolean`

  - `extract_charts: optional boolean`

  - `extract_layout: optional boolean`

  - `extract_printed_page_number: optional boolean`

  - `fast_mode: optional boolean`

  - `formatting_instruction: optional string`

  - `gpt4o_api_key: optional string`

  - `gpt4o_mode: optional boolean`

  - `guess_xlsx_sheet_name: optional boolean`

  - `hide_footers: optional boolean`

  - `hide_headers: optional boolean`

  - `high_res_ocr: optional boolean`

  - `html_make_all_elements_visible: optional boolean`

  - `html_remove_fixed_elements: optional boolean`

  - `html_remove_navigation_elements: optional boolean`

  - `http_proxy: optional string`

  - `ignore_document_elements_for_layout_detection: optional boolean`

  - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: optional boolean`

  - `input_s3_path: optional string`

  - `input_s3_region: optional string`

  - `input_url: optional string`

  - `internal_is_screenshot_job: optional boolean`

  - `invalidate_cache: optional boolean`

  - `is_formatting_instruction: optional boolean`

  - `job_timeout_extra_time_per_page_in_seconds: optional number`

  - `job_timeout_in_seconds: optional number`

  - `keep_page_separator_when_merging_tables: optional boolean`

  - `languages: optional array of ParsingLanguages`

    - `"af"`

    - `"az"`

    - `"bs"`

    - `"cs"`

    - `"cy"`

    - `"da"`

    - `"de"`

    - `"en"`

    - `"es"`

    - `"et"`

    - `"fr"`

    - `"ga"`

    - `"hr"`

    - `"hu"`

    - `"id"`

    - `"is"`

    - `"it"`

    - `"ku"`

    - `"la"`

    - `"lt"`

    - `"lv"`

    - `"mi"`

    - `"ms"`

    - `"mt"`

    - `"nl"`

    - `"no"`

    - `"oc"`

    - `"pi"`

    - `"pl"`

    - `"pt"`

    - `"ro"`

    - `"rs_latin"`

    - `"sk"`

    - `"sl"`

    - `"sq"`

    - `"sv"`

    - `"sw"`

    - `"tl"`

    - `"tr"`

    - `"uz"`

    - `"vi"`

    - `"ar"`

    - `"fa"`

    - `"ug"`

    - `"ur"`

    - `"bn"`

    - `"as"`

    - `"mni"`

    - `"ru"`

    - `"rs_cyrillic"`

    - `"be"`

    - `"bg"`

    - `"uk"`

    - `"mn"`

    - `"abq"`

    - `"ady"`

    - `"kbd"`

    - `"ava"`

    - `"dar"`

    - `"inh"`

    - `"che"`

    - `"lbe"`

    - `"lez"`

    - `"tab"`

    - `"tjk"`

    - `"hi"`

    - `"mr"`

    - `"ne"`

    - `"bh"`

    - `"mai"`

    - `"ang"`

    - `"bho"`

    - `"mah"`

    - `"sck"`

    - `"new"`

    - `"gom"`

    - `"sa"`

    - `"bgc"`

    - `"th"`

    - `"ch_sim"`

    - `"ch_tra"`

    - `"ja"`

    - `"ko"`

    - `"ta"`

    - `"te"`

    - `"kn"`

  - `layout_aware: optional boolean`

  - `line_level_bounding_box: optional boolean`

  - `markdown_table_multiline_header_separator: optional string`

  - `max_pages: optional number`

  - `max_pages_enforced: optional number`

  - `merge_tables_across_pages_in_markdown: optional boolean`

  - `model: optional string`

  - `outlined_table_extraction: optional boolean`

  - `output_pdf_of_document: optional boolean`

  - `output_s3_path_prefix: optional string`

  - `output_s3_region: optional string`

  - `output_tables_as_HTML: optional boolean`

  - `page_error_tolerance: optional number`

  - `page_footer_prefix: optional string`

  - `page_footer_suffix: optional string`

  - `page_header_prefix: optional string`

  - `page_header_suffix: optional string`

  - `page_prefix: optional string`

  - `page_separator: optional string`

  - `page_suffix: optional string`

  - `parse_mode: optional ParsingMode`

    Enum for representing the mode of parsing to be used.

    - `"parse_page_without_llm"`

    - `"parse_page_with_llm"`

    - `"parse_page_with_lvm"`

    - `"parse_page_with_agent"`

    - `"parse_page_with_layout_agent"`

    - `"parse_document_with_llm"`

    - `"parse_document_with_lvm"`

    - `"parse_document_with_agent"`

  - `parsing_instruction: optional string`

  - `precise_bounding_box: optional boolean`

  - `premium_mode: optional boolean`

  - `presentation_out_of_bounds_content: optional boolean`

  - `presentation_skip_embedded_data: optional boolean`

  - `preserve_layout_alignment_across_pages: optional boolean`

  - `preserve_very_small_text: optional boolean`

  - `preset: optional string`

  - `priority: optional "low" or "medium" or "high" or "critical"`

    The priority for the request. This field may be ignored or overwritten depending on the organization tier.

    - `"low"`

    - `"medium"`

    - `"high"`

    - `"critical"`

  - `project_id: optional string`

  - `remove_hidden_text: optional boolean`

  - `replace_failed_page_mode: optional FailPageMode`

    Enum for representing the different available page error handling modes.

    - `"raw_text"`

    - `"blank_page"`

    - `"error_message"`

  - `replace_failed_page_with_error_message_prefix: optional string`

  - `replace_failed_page_with_error_message_suffix: optional string`

  - `save_images: optional boolean`

  - `skip_diagonal_text: optional boolean`

  - `specialized_chart_parsing_agentic: optional boolean`

  - `specialized_chart_parsing_efficient: optional boolean`

  - `specialized_chart_parsing_plus: optional boolean`

  - `specialized_image_parsing: optional boolean`

  - `spreadsheet_extract_sub_tables: optional boolean`

  - `spreadsheet_force_formula_computation: optional boolean`

  - `spreadsheet_include_hidden_sheets: optional boolean`

  - `strict_mode_buggy_font: optional boolean`

  - `strict_mode_image_extraction: optional boolean`

  - `strict_mode_image_ocr: optional boolean`

  - `strict_mode_reconstruction: optional boolean`

  - `structured_output: optional boolean`

  - `structured_output_json_schema: optional string`

  - `structured_output_json_schema_name: optional string`

  - `system_prompt: optional string`

  - `system_prompt_append: optional string`

  - `take_screenshot: optional boolean`

  - `target_pages: optional string`

  - `tier: optional string`

  - `use_vendor_multimodal_model: optional boolean`

  - `user_prompt: optional string`

  - `vendor_multimodal_api_key: optional string`

  - `vendor_multimodal_model_name: optional string`

  - `version: optional string`

  - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

      Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

      - `"extract.pending"`

      - `"extract.success"`

      - `"extract.error"`

      - `"extract.partial_success"`

      - `"extract.cancelled"`

      - `"parse.pending"`

      - `"parse.running"`

      - `"parse.success"`

      - `"parse.error"`

      - `"parse.partial_success"`

      - `"parse.cancelled"`

      - `"classify.pending"`

      - `"classify.success"`

      - `"classify.error"`

      - `"classify.partial_success"`

      - `"classify.cancelled"`

      - `"unmapped_event"`

    - `webhook_headers: optional map[string]`

      Custom HTTP headers sent with each webhook request (e.g. auth tokens)

    - `webhook_output_format: optional string`

      Response format sent to the webhook: 'string' (default) or 'json'

    - `webhook_url: optional string`

      URL to receive webhook POST notifications

  - `webhook_url: optional string`

- `managed_pipeline_id: optional string`

  The ID of the ManagedPipeline this playground pipeline is linked to.

- `metadata_config: optional PipelineMetadataConfig`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: optional array of string`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: optional array of string`

    List of metadata keys to exclude from LLM during retrieval

- `pipeline_type: optional PipelineType`

  Type of pipeline. Either PLAYGROUND or MANAGED.

  - `"PLAYGROUND"`

  - `"MANAGED"`

- `preset_retrieval_parameters: optional PresetRetrievalParams`

  Preset retrieval parameters for the pipeline.

  - `alpha: optional number`

    Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

  - `class_name: optional string`

  - `dense_similarity_cutoff: optional number`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: optional number`

    Number of nodes for dense retrieval.

  - `enable_reranking: optional boolean`

    Enable reranking for retrieval

  - `files_top_k: optional number`

    Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

  - `rerank_top_n: optional number`

    Number of reranked nodes for returning.

  - `retrieval_mode: optional RetrievalMode`

    The retrieval mode for the query.

    - `"chunks"`

    - `"files_via_metadata"`

    - `"files_via_content"`

    - `"auto_routed"`

  - `retrieve_image_nodes: optional boolean`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: optional boolean`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: optional boolean`

    Whether to retrieve page screenshot nodes.

  - `search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

    JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `sparse_similarity_top_k: optional number`

    Number of nodes for sparse retrieval.

- `sparse_model_config: optional SparseModelConfig`

  Configuration for sparse embedding models used in hybrid search.

  This allows users to choose between Splade and BM25 models for
  sparse retrieval in managed data sinks.

  - `class_name: optional string`

  - `model_type: optional "splade" or "bm25" or "auto"`

    The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

    - `"splade"`

    - `"bm25"`

    - `"auto"`

- `status: optional string`

  Status of the pipeline deployment.

- `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

  Configuration for the transformation.

  - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

    - `chunk_overlap: optional number`

      Chunk overlap for the transformation.

    - `chunk_size: optional number`

      Chunk size for the transformation.

    - `mode: optional "auto"`

      - `"auto"`

  - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

    - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

      Configuration for the chunking.

      - `NoneChunkingConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "character"`

          - `"character"`

      - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "token"`

          - `"token"`

        - `separator: optional string`

      - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "sentence"`

          - `"sentence"`

        - `paragraph_separator: optional string`

        - `separator: optional string`

      - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

        - `breakpoint_percentile_threshold: optional number`

        - `buffer_size: optional number`

        - `mode: optional "semantic"`

          - `"semantic"`

    - `mode: optional "advanced"`

      - `"advanced"`

    - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

      Configuration for the segmentation.

      - `NoneSegmentationConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `PageSegmentationConfig = object { mode, page_separator }`

        - `mode: optional "page"`

          - `"page"`

        - `page_separator: optional string`

      - `ElementSegmentationConfig = object { mode }`

        - `mode: optional "element"`

          - `"element"`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "name": "x"
        }'
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Get Pipeline

**get** `/api/v1/pipelines/{pipeline_id}`

Get a pipeline by ID.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Update Existing Pipeline

**put** `/api/v1/pipelines/{pipeline_id}`

Update an existing pipeline's configuration.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `data_sink: optional DataSinkCreate`

  Schema for creating a data sink.

  - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

    Component that implements the data sink

    - `map[unknown]`

    - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

      Cloud Pinecone Vector Store.

      This class is used to store the configuration for a Pinecone vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      api_key (str): API key for authenticating with Pinecone
      index_name (str): name of the Pinecone index
      namespace (optional[str]): namespace to use in the Pinecone index
      insert_kwargs (optional[dict]): additional kwargs to pass during insertion

      - `api_key: string`

        The API key for authenticating with Pinecone

      - `index_name: string`

      - `class_name: optional string`

      - `insert_kwargs: optional map[unknown]`

      - `namespace: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

      - `database: string`

      - `embed_dim: number`

      - `host: string`

      - `password: string`

      - `port: number`

      - `schema_name: string`

      - `table_name: string`

      - `user: string`

      - `class_name: optional string`

      - `hnsw_settings: optional PgVectorHnswSettings`

        HNSW settings for PGVector.

        - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: optional number`

          The number of edges to use during the construction phase.

        - `ef_search: optional number`

          The number of edges to use during the search phase.

        - `m: optional number`

          The number of bi-directional links created for each new element.

        - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: optional boolean`

      - `perform_setup: optional boolean`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

      Cloud Qdrant Vector Store.

      This class is used to store the configuration for a Qdrant vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      collection_name (str): name of the Qdrant collection
      url (str): url of the Qdrant instance
      api_key (str): API key for authenticating with Qdrant
      max_retries (int): maximum number of retries in case of a failure. Defaults to 3
      client_kwargs (dict): additional kwargs to pass to the Qdrant client

      - `api_key: string`

      - `collection_name: string`

      - `url: string`

      - `class_name: optional string`

      - `client_kwargs: optional map[unknown]`

      - `max_retries: optional number`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: string`

      - `search_service_endpoint: string`

      - `class_name: optional string`

      - `client_id: optional string`

      - `client_secret: optional string`

      - `embedding_dimension: optional number`

      - `filterable_metadata_field_keys: optional map[unknown]`

      - `index_name: optional string`

      - `search_service_api_version: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

      - `tenant_id: optional string`

    - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

      Cloud MongoDB Atlas Vector Store.

      This class is used to store the configuration for a MongoDB Atlas vector store,
      so that it can be created and used in LlamaCloud.

      Args:
      mongodb_uri (str): URI for connecting to MongoDB Atlas
      db_name (str): name of the MongoDB database
      collection_name (str): name of the MongoDB collection
      vector_index_name (str): name of the MongoDB Atlas vector index
      fulltext_index_name (str): name of the MongoDB Atlas full-text index

      - `collection_name: string`

      - `db_name: string`

      - `mongodb_uri: string`

      - `class_name: optional string`

      - `embedding_dimension: optional number`

      - `fulltext_index_name: optional string`

      - `supports_nested_metadata_filters: optional boolean`

      - `vector_index_name: optional string`

    - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

      Cloud Milvus Vector Store.

      - `uri: string`

      - `token: optional string`

      - `class_name: optional string`

      - `collection_name: optional string`

      - `embedding_dimension: optional number`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

      Cloud AstraDB Vector Store.

      This class is used to store the configuration for an AstraDB vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      token (str): The Astra DB Application Token to use.
      api_endpoint (str): The Astra DB JSON API endpoint for your database.
      collection_name (str): Collection name to use. If not existing, it will be created.
      embedding_dimension (int): Length of the embedding vectors in use.
      keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

      - `token: string`

        The Astra DB Application Token to use

      - `api_endpoint: string`

        The Astra DB JSON API endpoint for your database

      - `collection_name: string`

        Collection name to use. If not existing, it will be created

      - `embedding_dimension: number`

        Length of the embedding vectors in use

      - `class_name: optional string`

      - `keyspace: optional string`

        The keyspace to use. If not provided, 'default_keyspace'

      - `supports_nested_metadata_filters: optional true`

        - `true`

  - `name: string`

    The name of the data sink.

  - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: optional string`

  Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.

- `embedding_config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

  - `AzureOpenAIEmbeddingConfig = object { component, type }`

    - `component: optional AzureOpenAIEmbedding`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for Azure deployment.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for Azure OpenAI API.

      - `azure_deployment: optional string`

        The Azure deployment to use.

      - `azure_endpoint: optional string`

        The Azure endpoint to use.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "AZURE_EMBEDDING"`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `CohereEmbeddingConfig = object { component, type }`

    - `component: optional CohereEmbedding`

      Configuration for the Cohere embedding model.

      - `api_key: string`

        The Cohere API key.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embedding_type: optional string`

        Embedding type. If not provided float embedding_type is used when needed.

      - `input_type: optional string`

        Model Input type. If not provided, search_document and search_query are used when needed.

      - `model_name: optional string`

        The modelId of the Cohere model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `truncate: optional string`

        Truncation type - START/ END/ NONE

    - `type: optional "COHERE_EMBEDDING"`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `GeminiEmbeddingConfig = object { component, type }`

    - `component: optional GeminiEmbedding`

      Configuration for the Gemini embedding model.

      - `api_base: optional string`

        API base to access the model. Defaults to None.

      - `api_key: optional string`

        API key to access the model. Defaults to None.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `model_name: optional string`

        The modelId of the Gemini model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: optional number`

        Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

      - `task_type: optional string`

        The task for embedding model.

      - `title: optional string`

        Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

      - `transport: optional string`

        Transport to access the model. Defaults to None.

    - `type: optional "GEMINI_EMBEDDING"`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

    - `component: optional HuggingFaceInferenceAPIEmbedding`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: optional string or boolean`

        Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

        - `string`

        - `boolean`

      - `class_name: optional string`

      - `cookies: optional map[string]`

        Additional cookies to send to the server.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `headers: optional map[string]`

        Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

      - `model_name: optional string`

        Hugging Face model name. If None, the task will be used.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `pooling: optional "cls" or "mean" or "last"`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: optional string`

        Instruction to prepend during query embedding.

      - `task: optional string`

        Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

      - `text_instruction: optional string`

        Instruction to prepend during text embedding.

      - `timeout: optional number`

        The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

    - `type: optional "HUGGINGFACE_API_EMBEDDING"`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `OpenAIEmbeddingConfig = object { component, type }`

    - `component: optional OpenAIEmbedding`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for OpenAI API.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for OpenAI API.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "OPENAI_EMBEDDING"`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `VertexAIEmbeddingConfig = object { component, type }`

    - `component: optional VertexTextEmbedding`

      Configuration for the VertexAI embedding model.

      - `client_email: string`

        The client email for the VertexAI credentials.

      - `location: string`

        The default location to use when making API calls.

      - `private_key: string`

        The private key for the VertexAI credentials.

      - `private_key_id: string`

        The private key ID for the VertexAI credentials.

      - `project: string`

        The default GCP project to use when making Vertex API calls.

      - `token_uri: string`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the Vertex.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: optional string`

        The modelId of the VertexAI model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

    - `type: optional "VERTEXAI_EMBEDDING"`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `BedrockEmbeddingConfig = object { component, type }`

    - `component: optional BedrockEmbedding`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: optional string`

        AWS Access Key ID to use

      - `aws_secret_access_key: optional string`

        AWS Secret Access Key to use

      - `aws_session_token: optional string`

        AWS Session Token to use

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        The maximum number of API retries.

      - `model_name: optional string`

        The modelId of the Bedrock model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `profile_name: optional string`

        The name of aws profile to use. If not given, then the default profile is used.

      - `region_name: optional string`

        AWS region name to use. Uses region configured in AWS CLI if not passed

      - `timeout: optional number`

        The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

    - `type: optional "BEDROCK_EMBEDDING"`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: optional string`

  Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.

- `llama_parse_parameters: optional LlamaParseParameters`

  Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

  - `adaptive_long_table: optional boolean`

  - `aggressive_table_extraction: optional boolean`

  - `annotate_links: optional boolean`

  - `auto_mode: optional boolean`

  - `auto_mode_configuration_json: optional string`

  - `auto_mode_trigger_on_image_in_page: optional boolean`

  - `auto_mode_trigger_on_regexp_in_page: optional string`

  - `auto_mode_trigger_on_table_in_page: optional boolean`

  - `auto_mode_trigger_on_text_in_page: optional string`

  - `azure_openai_api_version: optional string`

  - `azure_openai_deployment_name: optional string`

  - `azure_openai_endpoint: optional string`

  - `azure_openai_key: optional string`

  - `bbox_bottom: optional number`

  - `bbox_left: optional number`

  - `bbox_right: optional number`

  - `bbox_top: optional number`

  - `bounding_box: optional string`

  - `compact_markdown_table: optional boolean`

  - `complemental_formatting_instruction: optional string`

  - `content_guideline_instruction: optional string`

  - `continuous_mode: optional boolean`

  - `disable_image_extraction: optional boolean`

  - `disable_ocr: optional boolean`

  - `disable_reconstruction: optional boolean`

  - `do_not_cache: optional boolean`

  - `do_not_unroll_columns: optional boolean`

  - `enable_cost_optimizer: optional boolean`

  - `extract_charts: optional boolean`

  - `extract_layout: optional boolean`

  - `extract_printed_page_number: optional boolean`

  - `fast_mode: optional boolean`

  - `formatting_instruction: optional string`

  - `gpt4o_api_key: optional string`

  - `gpt4o_mode: optional boolean`

  - `guess_xlsx_sheet_name: optional boolean`

  - `hide_footers: optional boolean`

  - `hide_headers: optional boolean`

  - `high_res_ocr: optional boolean`

  - `html_make_all_elements_visible: optional boolean`

  - `html_remove_fixed_elements: optional boolean`

  - `html_remove_navigation_elements: optional boolean`

  - `http_proxy: optional string`

  - `ignore_document_elements_for_layout_detection: optional boolean`

  - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: optional boolean`

  - `input_s3_path: optional string`

  - `input_s3_region: optional string`

  - `input_url: optional string`

  - `internal_is_screenshot_job: optional boolean`

  - `invalidate_cache: optional boolean`

  - `is_formatting_instruction: optional boolean`

  - `job_timeout_extra_time_per_page_in_seconds: optional number`

  - `job_timeout_in_seconds: optional number`

  - `keep_page_separator_when_merging_tables: optional boolean`

  - `languages: optional array of ParsingLanguages`

    - `"af"`

    - `"az"`

    - `"bs"`

    - `"cs"`

    - `"cy"`

    - `"da"`

    - `"de"`

    - `"en"`

    - `"es"`

    - `"et"`

    - `"fr"`

    - `"ga"`

    - `"hr"`

    - `"hu"`

    - `"id"`

    - `"is"`

    - `"it"`

    - `"ku"`

    - `"la"`

    - `"lt"`

    - `"lv"`

    - `"mi"`

    - `"ms"`

    - `"mt"`

    - `"nl"`

    - `"no"`

    - `"oc"`

    - `"pi"`

    - `"pl"`

    - `"pt"`

    - `"ro"`

    - `"rs_latin"`

    - `"sk"`

    - `"sl"`

    - `"sq"`

    - `"sv"`

    - `"sw"`

    - `"tl"`

    - `"tr"`

    - `"uz"`

    - `"vi"`

    - `"ar"`

    - `"fa"`

    - `"ug"`

    - `"ur"`

    - `"bn"`

    - `"as"`

    - `"mni"`

    - `"ru"`

    - `"rs_cyrillic"`

    - `"be"`

    - `"bg"`

    - `"uk"`

    - `"mn"`

    - `"abq"`

    - `"ady"`

    - `"kbd"`

    - `"ava"`

    - `"dar"`

    - `"inh"`

    - `"che"`

    - `"lbe"`

    - `"lez"`

    - `"tab"`

    - `"tjk"`

    - `"hi"`

    - `"mr"`

    - `"ne"`

    - `"bh"`

    - `"mai"`

    - `"ang"`

    - `"bho"`

    - `"mah"`

    - `"sck"`

    - `"new"`

    - `"gom"`

    - `"sa"`

    - `"bgc"`

    - `"th"`

    - `"ch_sim"`

    - `"ch_tra"`

    - `"ja"`

    - `"ko"`

    - `"ta"`

    - `"te"`

    - `"kn"`

  - `layout_aware: optional boolean`

  - `line_level_bounding_box: optional boolean`

  - `markdown_table_multiline_header_separator: optional string`

  - `max_pages: optional number`

  - `max_pages_enforced: optional number`

  - `merge_tables_across_pages_in_markdown: optional boolean`

  - `model: optional string`

  - `outlined_table_extraction: optional boolean`

  - `output_pdf_of_document: optional boolean`

  - `output_s3_path_prefix: optional string`

  - `output_s3_region: optional string`

  - `output_tables_as_HTML: optional boolean`

  - `page_error_tolerance: optional number`

  - `page_footer_prefix: optional string`

  - `page_footer_suffix: optional string`

  - `page_header_prefix: optional string`

  - `page_header_suffix: optional string`

  - `page_prefix: optional string`

  - `page_separator: optional string`

  - `page_suffix: optional string`

  - `parse_mode: optional ParsingMode`

    Enum for representing the mode of parsing to be used.

    - `"parse_page_without_llm"`

    - `"parse_page_with_llm"`

    - `"parse_page_with_lvm"`

    - `"parse_page_with_agent"`

    - `"parse_page_with_layout_agent"`

    - `"parse_document_with_llm"`

    - `"parse_document_with_lvm"`

    - `"parse_document_with_agent"`

  - `parsing_instruction: optional string`

  - `precise_bounding_box: optional boolean`

  - `premium_mode: optional boolean`

  - `presentation_out_of_bounds_content: optional boolean`

  - `presentation_skip_embedded_data: optional boolean`

  - `preserve_layout_alignment_across_pages: optional boolean`

  - `preserve_very_small_text: optional boolean`

  - `preset: optional string`

  - `priority: optional "low" or "medium" or "high" or "critical"`

    The priority for the request. This field may be ignored or overwritten depending on the organization tier.

    - `"low"`

    - `"medium"`

    - `"high"`

    - `"critical"`

  - `project_id: optional string`

  - `remove_hidden_text: optional boolean`

  - `replace_failed_page_mode: optional FailPageMode`

    Enum for representing the different available page error handling modes.

    - `"raw_text"`

    - `"blank_page"`

    - `"error_message"`

  - `replace_failed_page_with_error_message_prefix: optional string`

  - `replace_failed_page_with_error_message_suffix: optional string`

  - `save_images: optional boolean`

  - `skip_diagonal_text: optional boolean`

  - `specialized_chart_parsing_agentic: optional boolean`

  - `specialized_chart_parsing_efficient: optional boolean`

  - `specialized_chart_parsing_plus: optional boolean`

  - `specialized_image_parsing: optional boolean`

  - `spreadsheet_extract_sub_tables: optional boolean`

  - `spreadsheet_force_formula_computation: optional boolean`

  - `spreadsheet_include_hidden_sheets: optional boolean`

  - `strict_mode_buggy_font: optional boolean`

  - `strict_mode_image_extraction: optional boolean`

  - `strict_mode_image_ocr: optional boolean`

  - `strict_mode_reconstruction: optional boolean`

  - `structured_output: optional boolean`

  - `structured_output_json_schema: optional string`

  - `structured_output_json_schema_name: optional string`

  - `system_prompt: optional string`

  - `system_prompt_append: optional string`

  - `take_screenshot: optional boolean`

  - `target_pages: optional string`

  - `tier: optional string`

  - `use_vendor_multimodal_model: optional boolean`

  - `user_prompt: optional string`

  - `vendor_multimodal_api_key: optional string`

  - `vendor_multimodal_model_name: optional string`

  - `version: optional string`

  - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

      Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

      - `"extract.pending"`

      - `"extract.success"`

      - `"extract.error"`

      - `"extract.partial_success"`

      - `"extract.cancelled"`

      - `"parse.pending"`

      - `"parse.running"`

      - `"parse.success"`

      - `"parse.error"`

      - `"parse.partial_success"`

      - `"parse.cancelled"`

      - `"classify.pending"`

      - `"classify.success"`

      - `"classify.error"`

      - `"classify.partial_success"`

      - `"classify.cancelled"`

      - `"unmapped_event"`

    - `webhook_headers: optional map[string]`

      Custom HTTP headers sent with each webhook request (e.g. auth tokens)

    - `webhook_output_format: optional string`

      Response format sent to the webhook: 'string' (default) or 'json'

    - `webhook_url: optional string`

      URL to receive webhook POST notifications

  - `webhook_url: optional string`

- `managed_pipeline_id: optional string`

  The ID of the ManagedPipeline this playground pipeline is linked to.

- `metadata_config: optional PipelineMetadataConfig`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: optional array of string`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: optional array of string`

    List of metadata keys to exclude from LLM during retrieval

- `name: optional string`

- `preset_retrieval_parameters: optional PresetRetrievalParams`

  Schema for the search params for an retrieval execution that can be preset for a pipeline.

  - `alpha: optional number`

    Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

  - `class_name: optional string`

  - `dense_similarity_cutoff: optional number`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: optional number`

    Number of nodes for dense retrieval.

  - `enable_reranking: optional boolean`

    Enable reranking for retrieval

  - `files_top_k: optional number`

    Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

  - `rerank_top_n: optional number`

    Number of reranked nodes for returning.

  - `retrieval_mode: optional RetrievalMode`

    The retrieval mode for the query.

    - `"chunks"`

    - `"files_via_metadata"`

    - `"files_via_content"`

    - `"auto_routed"`

  - `retrieve_image_nodes: optional boolean`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: optional boolean`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: optional boolean`

    Whether to retrieve page screenshot nodes.

  - `search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

    JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `sparse_similarity_top_k: optional number`

    Number of nodes for sparse retrieval.

- `sparse_model_config: optional SparseModelConfig`

  Configuration for sparse embedding models used in hybrid search.

  This allows users to choose between Splade and BM25 models for
  sparse retrieval in managed data sinks.

  - `class_name: optional string`

  - `model_type: optional "splade" or "bm25" or "auto"`

    The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

    - `"splade"`

    - `"bm25"`

    - `"auto"`

- `status: optional string`

  Status of the pipeline deployment.

- `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

  Configuration for the transformation.

  - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

    - `chunk_overlap: optional number`

      Chunk overlap for the transformation.

    - `chunk_size: optional number`

      Chunk size for the transformation.

    - `mode: optional "auto"`

      - `"auto"`

  - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

    - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

      Configuration for the chunking.

      - `NoneChunkingConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "character"`

          - `"character"`

      - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "token"`

          - `"token"`

        - `separator: optional string`

      - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "sentence"`

          - `"sentence"`

        - `paragraph_separator: optional string`

        - `separator: optional string`

      - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

        - `breakpoint_percentile_threshold: optional number`

        - `buffer_size: optional number`

        - `mode: optional "semantic"`

          - `"semantic"`

    - `mode: optional "advanced"`

      - `"advanced"`

    - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

      Configuration for the segmentation.

      - `NoneSegmentationConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `PageSegmentationConfig = object { mode, page_separator }`

        - `mode: optional "page"`

          - `"page"`

        - `page_separator: optional string`

      - `ElementSegmentationConfig = object { mode }`

        - `mode: optional "element"`

          - `"element"`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{}'
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Delete Pipeline

**delete** `/api/v1/pipelines/{pipeline_id}`

Delete a pipeline and all associated resources.

Removes pipeline files, data sources, and vector store data.
This operation is irreversible.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID \
    -X DELETE \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

## Get Pipeline Status

**get** `/api/v1/pipelines/{pipeline_id}/status`

Get the ingestion status of a managed pipeline.

Returns document counts, sync progress, and the last
effective timestamp. Only available for managed pipelines.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `full_details: optional boolean`

### Cookie Parameters

- `session: optional string`

### Returns

- `ManagedIngestionStatusResponse = object { status, deployment_date, effective_at, 2 more }`

  - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: optional string`

    Date of the deployment.

  - `effective_at: optional string`

    When the status is effective

  - `error: optional array of object { job_id, message, step }`

    List of errors that occurred during ingestion.

    - `job_id: string`

      ID of the job that failed.

    - `message: string`

      List of errors that occurred during ingestion.

    - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: optional string`

    ID of the latest job.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/status \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "status": "NOT_STARTED",
  "deployment_date": "2019-12-27T18:11:19.117Z",
  "effective_at": "2019-12-27T18:11:19.117Z",
  "error": [
    {
      "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "message": "message",
      "step": "MANAGED_INGESTION"
    }
  ],
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
}
```

## Upsert Pipeline

**put** `/api/v1/pipelines`

Upsert a pipeline.

Updates the pipeline if one with the same name and project
already exists, otherwise creates a new one.

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `name: string`

- `data_sink: optional DataSinkCreate`

  Schema for creating a data sink.

  - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

    Component that implements the data sink

    - `map[unknown]`

    - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

      Cloud Pinecone Vector Store.

      This class is used to store the configuration for a Pinecone vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      api_key (str): API key for authenticating with Pinecone
      index_name (str): name of the Pinecone index
      namespace (optional[str]): namespace to use in the Pinecone index
      insert_kwargs (optional[dict]): additional kwargs to pass during insertion

      - `api_key: string`

        The API key for authenticating with Pinecone

      - `index_name: string`

      - `class_name: optional string`

      - `insert_kwargs: optional map[unknown]`

      - `namespace: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

      - `database: string`

      - `embed_dim: number`

      - `host: string`

      - `password: string`

      - `port: number`

      - `schema_name: string`

      - `table_name: string`

      - `user: string`

      - `class_name: optional string`

      - `hnsw_settings: optional PgVectorHnswSettings`

        HNSW settings for PGVector.

        - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: optional number`

          The number of edges to use during the construction phase.

        - `ef_search: optional number`

          The number of edges to use during the search phase.

        - `m: optional number`

          The number of bi-directional links created for each new element.

        - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: optional boolean`

      - `perform_setup: optional boolean`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

      Cloud Qdrant Vector Store.

      This class is used to store the configuration for a Qdrant vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      collection_name (str): name of the Qdrant collection
      url (str): url of the Qdrant instance
      api_key (str): API key for authenticating with Qdrant
      max_retries (int): maximum number of retries in case of a failure. Defaults to 3
      client_kwargs (dict): additional kwargs to pass to the Qdrant client

      - `api_key: string`

      - `collection_name: string`

      - `url: string`

      - `class_name: optional string`

      - `client_kwargs: optional map[unknown]`

      - `max_retries: optional number`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: string`

      - `search_service_endpoint: string`

      - `class_name: optional string`

      - `client_id: optional string`

      - `client_secret: optional string`

      - `embedding_dimension: optional number`

      - `filterable_metadata_field_keys: optional map[unknown]`

      - `index_name: optional string`

      - `search_service_api_version: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

      - `tenant_id: optional string`

    - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

      Cloud MongoDB Atlas Vector Store.

      This class is used to store the configuration for a MongoDB Atlas vector store,
      so that it can be created and used in LlamaCloud.

      Args:
      mongodb_uri (str): URI for connecting to MongoDB Atlas
      db_name (str): name of the MongoDB database
      collection_name (str): name of the MongoDB collection
      vector_index_name (str): name of the MongoDB Atlas vector index
      fulltext_index_name (str): name of the MongoDB Atlas full-text index

      - `collection_name: string`

      - `db_name: string`

      - `mongodb_uri: string`

      - `class_name: optional string`

      - `embedding_dimension: optional number`

      - `fulltext_index_name: optional string`

      - `supports_nested_metadata_filters: optional boolean`

      - `vector_index_name: optional string`

    - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

      Cloud Milvus Vector Store.

      - `uri: string`

      - `token: optional string`

      - `class_name: optional string`

      - `collection_name: optional string`

      - `embedding_dimension: optional number`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

      Cloud AstraDB Vector Store.

      This class is used to store the configuration for an AstraDB vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      token (str): The Astra DB Application Token to use.
      api_endpoint (str): The Astra DB JSON API endpoint for your database.
      collection_name (str): Collection name to use. If not existing, it will be created.
      embedding_dimension (int): Length of the embedding vectors in use.
      keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

      - `token: string`

        The Astra DB Application Token to use

      - `api_endpoint: string`

        The Astra DB JSON API endpoint for your database

      - `collection_name: string`

        Collection name to use. If not existing, it will be created

      - `embedding_dimension: number`

        Length of the embedding vectors in use

      - `class_name: optional string`

      - `keyspace: optional string`

        The keyspace to use. If not provided, 'default_keyspace'

      - `supports_nested_metadata_filters: optional true`

        - `true`

  - `name: string`

    The name of the data sink.

  - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: optional string`

  Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.

- `embedding_config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

  - `AzureOpenAIEmbeddingConfig = object { component, type }`

    - `component: optional AzureOpenAIEmbedding`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for Azure deployment.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for Azure OpenAI API.

      - `azure_deployment: optional string`

        The Azure deployment to use.

      - `azure_endpoint: optional string`

        The Azure endpoint to use.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "AZURE_EMBEDDING"`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `CohereEmbeddingConfig = object { component, type }`

    - `component: optional CohereEmbedding`

      Configuration for the Cohere embedding model.

      - `api_key: string`

        The Cohere API key.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embedding_type: optional string`

        Embedding type. If not provided float embedding_type is used when needed.

      - `input_type: optional string`

        Model Input type. If not provided, search_document and search_query are used when needed.

      - `model_name: optional string`

        The modelId of the Cohere model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `truncate: optional string`

        Truncation type - START/ END/ NONE

    - `type: optional "COHERE_EMBEDDING"`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `GeminiEmbeddingConfig = object { component, type }`

    - `component: optional GeminiEmbedding`

      Configuration for the Gemini embedding model.

      - `api_base: optional string`

        API base to access the model. Defaults to None.

      - `api_key: optional string`

        API key to access the model. Defaults to None.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `model_name: optional string`

        The modelId of the Gemini model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: optional number`

        Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

      - `task_type: optional string`

        The task for embedding model.

      - `title: optional string`

        Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

      - `transport: optional string`

        Transport to access the model. Defaults to None.

    - `type: optional "GEMINI_EMBEDDING"`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

    - `component: optional HuggingFaceInferenceAPIEmbedding`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: optional string or boolean`

        Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

        - `string`

        - `boolean`

      - `class_name: optional string`

      - `cookies: optional map[string]`

        Additional cookies to send to the server.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `headers: optional map[string]`

        Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

      - `model_name: optional string`

        Hugging Face model name. If None, the task will be used.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `pooling: optional "cls" or "mean" or "last"`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: optional string`

        Instruction to prepend during query embedding.

      - `task: optional string`

        Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

      - `text_instruction: optional string`

        Instruction to prepend during text embedding.

      - `timeout: optional number`

        The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

    - `type: optional "HUGGINGFACE_API_EMBEDDING"`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `OpenAIEmbeddingConfig = object { component, type }`

    - `component: optional OpenAIEmbedding`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the OpenAI API.

      - `api_base: optional string`

        The base URL for OpenAI API.

      - `api_key: optional string`

        The OpenAI API key.

      - `api_version: optional string`

        The version for OpenAI API.

      - `class_name: optional string`

      - `default_headers: optional map[string]`

        The default headers for API requests.

      - `dimensions: optional number`

        The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        Maximum number of retries.

      - `model_name: optional string`

        The name of the OpenAI embedding model.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `reuse_client: optional boolean`

        Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

      - `timeout: optional number`

        Timeout for each request.

    - `type: optional "OPENAI_EMBEDDING"`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `VertexAIEmbeddingConfig = object { component, type }`

    - `component: optional VertexTextEmbedding`

      Configuration for the VertexAI embedding model.

      - `client_email: string`

        The client email for the VertexAI credentials.

      - `location: string`

        The default location to use when making API calls.

      - `private_key: string`

        The private key for the VertexAI credentials.

      - `private_key_id: string`

        The private key ID for the VertexAI credentials.

      - `project: string`

        The default GCP project to use when making Vertex API calls.

      - `token_uri: string`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the Vertex.

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: optional string`

        The modelId of the VertexAI model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

    - `type: optional "VERTEXAI_EMBEDDING"`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `BedrockEmbeddingConfig = object { component, type }`

    - `component: optional BedrockEmbedding`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: optional map[unknown]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: optional string`

        AWS Access Key ID to use

      - `aws_secret_access_key: optional string`

        AWS Secret Access Key to use

      - `aws_session_token: optional string`

        AWS Session Token to use

      - `class_name: optional string`

      - `embed_batch_size: optional number`

        The batch size for embedding calls.

      - `max_retries: optional number`

        The maximum number of API retries.

      - `model_name: optional string`

        The modelId of the Bedrock model to use.

      - `num_workers: optional number`

        The number of workers to use for async embedding calls.

      - `profile_name: optional string`

        The name of aws profile to use. If not given, then the default profile is used.

      - `region_name: optional string`

        AWS region name to use. Uses region configured in AWS CLI if not passed

      - `timeout: optional number`

        The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

    - `type: optional "BEDROCK_EMBEDDING"`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: optional string`

  Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.

- `llama_parse_parameters: optional LlamaParseParameters`

  Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

  - `adaptive_long_table: optional boolean`

  - `aggressive_table_extraction: optional boolean`

  - `annotate_links: optional boolean`

  - `auto_mode: optional boolean`

  - `auto_mode_configuration_json: optional string`

  - `auto_mode_trigger_on_image_in_page: optional boolean`

  - `auto_mode_trigger_on_regexp_in_page: optional string`

  - `auto_mode_trigger_on_table_in_page: optional boolean`

  - `auto_mode_trigger_on_text_in_page: optional string`

  - `azure_openai_api_version: optional string`

  - `azure_openai_deployment_name: optional string`

  - `azure_openai_endpoint: optional string`

  - `azure_openai_key: optional string`

  - `bbox_bottom: optional number`

  - `bbox_left: optional number`

  - `bbox_right: optional number`

  - `bbox_top: optional number`

  - `bounding_box: optional string`

  - `compact_markdown_table: optional boolean`

  - `complemental_formatting_instruction: optional string`

  - `content_guideline_instruction: optional string`

  - `continuous_mode: optional boolean`

  - `disable_image_extraction: optional boolean`

  - `disable_ocr: optional boolean`

  - `disable_reconstruction: optional boolean`

  - `do_not_cache: optional boolean`

  - `do_not_unroll_columns: optional boolean`

  - `enable_cost_optimizer: optional boolean`

  - `extract_charts: optional boolean`

  - `extract_layout: optional boolean`

  - `extract_printed_page_number: optional boolean`

  - `fast_mode: optional boolean`

  - `formatting_instruction: optional string`

  - `gpt4o_api_key: optional string`

  - `gpt4o_mode: optional boolean`

  - `guess_xlsx_sheet_name: optional boolean`

  - `hide_footers: optional boolean`

  - `hide_headers: optional boolean`

  - `high_res_ocr: optional boolean`

  - `html_make_all_elements_visible: optional boolean`

  - `html_remove_fixed_elements: optional boolean`

  - `html_remove_navigation_elements: optional boolean`

  - `http_proxy: optional string`

  - `ignore_document_elements_for_layout_detection: optional boolean`

  - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: optional boolean`

  - `input_s3_path: optional string`

  - `input_s3_region: optional string`

  - `input_url: optional string`

  - `internal_is_screenshot_job: optional boolean`

  - `invalidate_cache: optional boolean`

  - `is_formatting_instruction: optional boolean`

  - `job_timeout_extra_time_per_page_in_seconds: optional number`

  - `job_timeout_in_seconds: optional number`

  - `keep_page_separator_when_merging_tables: optional boolean`

  - `languages: optional array of ParsingLanguages`

    - `"af"`

    - `"az"`

    - `"bs"`

    - `"cs"`

    - `"cy"`

    - `"da"`

    - `"de"`

    - `"en"`

    - `"es"`

    - `"et"`

    - `"fr"`

    - `"ga"`

    - `"hr"`

    - `"hu"`

    - `"id"`

    - `"is"`

    - `"it"`

    - `"ku"`

    - `"la"`

    - `"lt"`

    - `"lv"`

    - `"mi"`

    - `"ms"`

    - `"mt"`

    - `"nl"`

    - `"no"`

    - `"oc"`

    - `"pi"`

    - `"pl"`

    - `"pt"`

    - `"ro"`

    - `"rs_latin"`

    - `"sk"`

    - `"sl"`

    - `"sq"`

    - `"sv"`

    - `"sw"`

    - `"tl"`

    - `"tr"`

    - `"uz"`

    - `"vi"`

    - `"ar"`

    - `"fa"`

    - `"ug"`

    - `"ur"`

    - `"bn"`

    - `"as"`

    - `"mni"`

    - `"ru"`

    - `"rs_cyrillic"`

    - `"be"`

    - `"bg"`

    - `"uk"`

    - `"mn"`

    - `"abq"`

    - `"ady"`

    - `"kbd"`

    - `"ava"`

    - `"dar"`

    - `"inh"`

    - `"che"`

    - `"lbe"`

    - `"lez"`

    - `"tab"`

    - `"tjk"`

    - `"hi"`

    - `"mr"`

    - `"ne"`

    - `"bh"`

    - `"mai"`

    - `"ang"`

    - `"bho"`

    - `"mah"`

    - `"sck"`

    - `"new"`

    - `"gom"`

    - `"sa"`

    - `"bgc"`

    - `"th"`

    - `"ch_sim"`

    - `"ch_tra"`

    - `"ja"`

    - `"ko"`

    - `"ta"`

    - `"te"`

    - `"kn"`

  - `layout_aware: optional boolean`

  - `line_level_bounding_box: optional boolean`

  - `markdown_table_multiline_header_separator: optional string`

  - `max_pages: optional number`

  - `max_pages_enforced: optional number`

  - `merge_tables_across_pages_in_markdown: optional boolean`

  - `model: optional string`

  - `outlined_table_extraction: optional boolean`

  - `output_pdf_of_document: optional boolean`

  - `output_s3_path_prefix: optional string`

  - `output_s3_region: optional string`

  - `output_tables_as_HTML: optional boolean`

  - `page_error_tolerance: optional number`

  - `page_footer_prefix: optional string`

  - `page_footer_suffix: optional string`

  - `page_header_prefix: optional string`

  - `page_header_suffix: optional string`

  - `page_prefix: optional string`

  - `page_separator: optional string`

  - `page_suffix: optional string`

  - `parse_mode: optional ParsingMode`

    Enum for representing the mode of parsing to be used.

    - `"parse_page_without_llm"`

    - `"parse_page_with_llm"`

    - `"parse_page_with_lvm"`

    - `"parse_page_with_agent"`

    - `"parse_page_with_layout_agent"`

    - `"parse_document_with_llm"`

    - `"parse_document_with_lvm"`

    - `"parse_document_with_agent"`

  - `parsing_instruction: optional string`

  - `precise_bounding_box: optional boolean`

  - `premium_mode: optional boolean`

  - `presentation_out_of_bounds_content: optional boolean`

  - `presentation_skip_embedded_data: optional boolean`

  - `preserve_layout_alignment_across_pages: optional boolean`

  - `preserve_very_small_text: optional boolean`

  - `preset: optional string`

  - `priority: optional "low" or "medium" or "high" or "critical"`

    The priority for the request. This field may be ignored or overwritten depending on the organization tier.

    - `"low"`

    - `"medium"`

    - `"high"`

    - `"critical"`

  - `project_id: optional string`

  - `remove_hidden_text: optional boolean`

  - `replace_failed_page_mode: optional FailPageMode`

    Enum for representing the different available page error handling modes.

    - `"raw_text"`

    - `"blank_page"`

    - `"error_message"`

  - `replace_failed_page_with_error_message_prefix: optional string`

  - `replace_failed_page_with_error_message_suffix: optional string`

  - `save_images: optional boolean`

  - `skip_diagonal_text: optional boolean`

  - `specialized_chart_parsing_agentic: optional boolean`

  - `specialized_chart_parsing_efficient: optional boolean`

  - `specialized_chart_parsing_plus: optional boolean`

  - `specialized_image_parsing: optional boolean`

  - `spreadsheet_extract_sub_tables: optional boolean`

  - `spreadsheet_force_formula_computation: optional boolean`

  - `spreadsheet_include_hidden_sheets: optional boolean`

  - `strict_mode_buggy_font: optional boolean`

  - `strict_mode_image_extraction: optional boolean`

  - `strict_mode_image_ocr: optional boolean`

  - `strict_mode_reconstruction: optional boolean`

  - `structured_output: optional boolean`

  - `structured_output_json_schema: optional string`

  - `structured_output_json_schema_name: optional string`

  - `system_prompt: optional string`

  - `system_prompt_append: optional string`

  - `take_screenshot: optional boolean`

  - `target_pages: optional string`

  - `tier: optional string`

  - `use_vendor_multimodal_model: optional boolean`

  - `user_prompt: optional string`

  - `vendor_multimodal_api_key: optional string`

  - `vendor_multimodal_model_name: optional string`

  - `version: optional string`

  - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

      Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

      - `"extract.pending"`

      - `"extract.success"`

      - `"extract.error"`

      - `"extract.partial_success"`

      - `"extract.cancelled"`

      - `"parse.pending"`

      - `"parse.running"`

      - `"parse.success"`

      - `"parse.error"`

      - `"parse.partial_success"`

      - `"parse.cancelled"`

      - `"classify.pending"`

      - `"classify.success"`

      - `"classify.error"`

      - `"classify.partial_success"`

      - `"classify.cancelled"`

      - `"unmapped_event"`

    - `webhook_headers: optional map[string]`

      Custom HTTP headers sent with each webhook request (e.g. auth tokens)

    - `webhook_output_format: optional string`

      Response format sent to the webhook: 'string' (default) or 'json'

    - `webhook_url: optional string`

      URL to receive webhook POST notifications

  - `webhook_url: optional string`

- `managed_pipeline_id: optional string`

  The ID of the ManagedPipeline this playground pipeline is linked to.

- `metadata_config: optional PipelineMetadataConfig`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: optional array of string`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: optional array of string`

    List of metadata keys to exclude from LLM during retrieval

- `pipeline_type: optional PipelineType`

  Type of pipeline. Either PLAYGROUND or MANAGED.

  - `"PLAYGROUND"`

  - `"MANAGED"`

- `preset_retrieval_parameters: optional PresetRetrievalParams`

  Preset retrieval parameters for the pipeline.

  - `alpha: optional number`

    Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

  - `class_name: optional string`

  - `dense_similarity_cutoff: optional number`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: optional number`

    Number of nodes for dense retrieval.

  - `enable_reranking: optional boolean`

    Enable reranking for retrieval

  - `files_top_k: optional number`

    Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

  - `rerank_top_n: optional number`

    Number of reranked nodes for returning.

  - `retrieval_mode: optional RetrievalMode`

    The retrieval mode for the query.

    - `"chunks"`

    - `"files_via_metadata"`

    - `"files_via_content"`

    - `"auto_routed"`

  - `retrieve_image_nodes: optional boolean`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: optional boolean`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: optional boolean`

    Whether to retrieve page screenshot nodes.

  - `search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

    JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `sparse_similarity_top_k: optional number`

    Number of nodes for sparse retrieval.

- `sparse_model_config: optional SparseModelConfig`

  Configuration for sparse embedding models used in hybrid search.

  This allows users to choose between Splade and BM25 models for
  sparse retrieval in managed data sinks.

  - `class_name: optional string`

  - `model_type: optional "splade" or "bm25" or "auto"`

    The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

    - `"splade"`

    - `"bm25"`

    - `"auto"`

- `status: optional string`

  Status of the pipeline deployment.

- `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

  Configuration for the transformation.

  - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

    - `chunk_overlap: optional number`

      Chunk overlap for the transformation.

    - `chunk_size: optional number`

      Chunk size for the transformation.

    - `mode: optional "auto"`

      - `"auto"`

  - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

    - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

      Configuration for the chunking.

      - `NoneChunkingConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "character"`

          - `"character"`

      - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "token"`

          - `"token"`

        - `separator: optional string`

      - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

        - `chunk_overlap: optional number`

        - `chunk_size: optional number`

        - `mode: optional "sentence"`

          - `"sentence"`

        - `paragraph_separator: optional string`

        - `separator: optional string`

      - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

        - `breakpoint_percentile_threshold: optional number`

        - `buffer_size: optional number`

        - `mode: optional "semantic"`

          - `"semantic"`

    - `mode: optional "advanced"`

      - `"advanced"`

    - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

      Configuration for the segmentation.

      - `NoneSegmentationConfig = object { mode }`

        - `mode: optional "none"`

          - `"none"`

      - `PageSegmentationConfig = object { mode, page_separator }`

        - `mode: optional "page"`

          - `"page"`

        - `page_separator: optional string`

      - `ElementSegmentationConfig = object { mode }`

        - `mode: optional "element"`

          - `"element"`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "name": "x"
        }'
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Run Search

**post** `/api/v1/pipelines/{pipeline_id}/retrieve`

Run a retrieval query against a managed pipeline.

Searches the pipeline's vector store using the provided query
and retrieval parameters. Supports dense, sparse, and hybrid
search modes with configurable top-k and reranking.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `query: string`

  The query to retrieve against.

- `alpha: optional number`

  Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

- `class_name: optional string`

- `dense_similarity_cutoff: optional number`

  Minimum similarity score wrt query for retrieval

- `dense_similarity_top_k: optional number`

  Number of nodes for dense retrieval.

- `enable_reranking: optional boolean`

  Enable reranking for retrieval

- `files_top_k: optional number`

  Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

- `rerank_top_n: optional number`

  Number of reranked nodes for returning.

- `retrieval_mode: optional RetrievalMode`

  The retrieval mode for the query.

  - `"chunks"`

  - `"files_via_metadata"`

  - `"files_via_content"`

  - `"auto_routed"`

- `retrieve_image_nodes: optional boolean`

  Whether to retrieve image nodes.

- `retrieve_page_figure_nodes: optional boolean`

  Whether to retrieve page figure nodes.

- `retrieve_page_screenshot_nodes: optional boolean`

  Whether to retrieve page screenshot nodes.

- `search_filters: optional MetadataFilters`

  Metadata filters for vector stores.

  - `filters: array of object { key, value, operator }  or MetadataFilters`

    - `MetadataFilter = object { key, value, operator }`

      Comprehensive metadata filter for vector stores to support more operators.

      Value uses Strict types, as int, float and str are compatible types and were all
      converted to string before.

      See: https://docs.pydantic.dev/latest/usage/types/#strict-types

      - `key: string`

      - `value: number or string or array of string or 2 more`

        - `number`

        - `string`

        - `array of string`

        - `array of number`

        - `array of number`

      - `operator: optional "==" or ">" or "<" or 11 more`

        Vector store filter operator.

        - `"=="`

        - `">"`

        - `"<"`

        - `"!="`

        - `">="`

        - `"<="`

        - `"in"`

        - `"nin"`

        - `"any"`

        - `"all"`

        - `"text_match"`

        - `"text_match_insensitive"`

        - `"contains"`

        - `"is_empty"`

    - `MetadataFilters = object { filters, condition }`

      Metadata filters for vector stores.

  - `condition: optional "and" or "or" or "not"`

    Vector store filter conditions to combine different filters.

    - `"and"`

    - `"or"`

    - `"not"`

- `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

  JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `sparse_similarity_top_k: optional number`

  Number of nodes for sparse retrieval.

### Returns

- `pipeline_id: string`

  The ID of the pipeline that the query was retrieved against.

- `retrieval_nodes: array of object { node, class_name, score }`

  The nodes retrieved by the pipeline for the given query.

  - `node: TextNode`

    Provided for backward compatibility.

    - `class_name: optional string`

    - `embedding: optional array of number`

      Embedding of the node.

    - `end_char_idx: optional number`

      End char index of the node.

    - `excluded_embed_metadata_keys: optional array of string`

      Metadata keys that are excluded from text for the embed model.

    - `excluded_llm_metadata_keys: optional array of string`

      Metadata keys that are excluded from text for the LLM.

    - `extra_info: optional map[unknown]`

      A flat dictionary of metadata fields

    - `id_: optional string`

      Unique ID of the node.

    - `metadata_seperator: optional string`

      Separator between metadata fields when converting to string.

    - `metadata_template: optional string`

      Template for how metadata is formatted, with {key} and {value} placeholders.

    - `mimetype: optional string`

      MIME type of the node content.

    - `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

      A mapping of relationships to other node information.

      - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

        - `node_id: string`

        - `class_name: optional string`

        - `hash: optional string`

        - `metadata: optional map[unknown]`

        - `node_type: optional "1" or "2" or "3" or 2 more or string`

          - `ObjectType = "1" or "2" or "3" or 2 more`

            - `"1"`

            - `"2"`

            - `"3"`

            - `"4"`

            - `"5"`

          - `string`

      - `array of object { node_id, class_name, hash, 2 more }`

        - `node_id: string`

        - `class_name: optional string`

        - `hash: optional string`

        - `metadata: optional map[unknown]`

        - `node_type: optional "1" or "2" or "3" or 2 more or string`

          - `ObjectType = "1" or "2" or "3" or 2 more`

            - `"1"`

            - `"2"`

            - `"3"`

            - `"4"`

            - `"5"`

          - `string`

    - `start_char_idx: optional number`

      Start char index of the node.

    - `text: optional string`

      Text content of the node.

    - `text_template: optional string`

      Template for how text is formatted, with {content} and {metadata_str} placeholders.

  - `class_name: optional string`

  - `score: optional number`

- `class_name: optional string`

- `image_nodes: optional array of PageScreenshotNodeWithScore`

  The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'.

  - `node: object { file_id, image_size, page_index, metadata }`

    - `file_id: string`

      The ID of the file that the page screenshot was taken from

    - `image_size: number`

      The size of the image in bytes

    - `page_index: number`

      The index of the page for which the screenshot is taken (0-indexed)

    - `metadata: optional map[unknown]`

      Metadata for the screenshot

  - `score: number`

    The score of the screenshot node

  - `class_name: optional string`

- `inferred_search_filters: optional MetadataFilters`

  Metadata filters for vector stores.

  - `filters: array of object { key, value, operator }  or MetadataFilters`

    - `MetadataFilter = object { key, value, operator }`

      Comprehensive metadata filter for vector stores to support more operators.

      Value uses Strict types, as int, float and str are compatible types and were all
      converted to string before.

      See: https://docs.pydantic.dev/latest/usage/types/#strict-types

      - `key: string`

      - `value: number or string or array of string or 2 more`

        - `number`

        - `string`

        - `array of string`

        - `array of number`

        - `array of number`

      - `operator: optional "==" or ">" or "<" or 11 more`

        Vector store filter operator.

        - `"=="`

        - `">"`

        - `"<"`

        - `"!="`

        - `">="`

        - `"<="`

        - `"in"`

        - `"nin"`

        - `"any"`

        - `"all"`

        - `"text_match"`

        - `"text_match_insensitive"`

        - `"contains"`

        - `"is_empty"`

    - `MetadataFilters = object { filters, condition }`

      Metadata filters for vector stores.

  - `condition: optional "and" or "or" or "not"`

    Vector store filter conditions to combine different filters.

    - `"and"`

    - `"or"`

    - `"not"`

- `metadata: optional map[string]`

  Metadata associated with the retrieval execution

- `page_figure_nodes: optional array of PageFigureNodeWithScore`

  The page figure nodes retrieved by the pipeline for the given query.

  - `node: object { confidence, figure_name, figure_size, 4 more }`

    - `confidence: number`

      The confidence of the figure

    - `figure_name: string`

      The name of the figure

    - `figure_size: number`

      The size of the figure in bytes

    - `file_id: string`

      The ID of the file that the figure was taken from

    - `page_index: number`

      The index of the page for which the figure is taken (0-indexed)

    - `is_likely_noise: optional boolean`

      Whether the figure is likely to be noise

    - `metadata: optional map[unknown]`

      Metadata for the figure

  - `score: number`

    The score of the figure node

  - `class_name: optional string`

- `retrieval_latency: optional map[number]`

  The end-to-end latency for retrieval and reranking.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/retrieve \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "query": "x"
        }'
```

#### Response

```json
{
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "retrieval_nodes": [
    {
      "node": {
        "class_name": "class_name",
        "embedding": [
          0
        ],
        "end_char_idx": 0,
        "excluded_embed_metadata_keys": [
          "string"
        ],
        "excluded_llm_metadata_keys": [
          "string"
        ],
        "extra_info": {
          "foo": "bar"
        },
        "id_": "id_",
        "metadata_seperator": "metadata_seperator",
        "metadata_template": "metadata_template",
        "mimetype": "mimetype",
        "relationships": {
          "foo": {
            "node_id": "node_id",
            "class_name": "class_name",
            "hash": "hash",
            "metadata": {
              "foo": "bar"
            },
            "node_type": "1"
          }
        },
        "start_char_idx": 0,
        "text": "text",
        "text_template": "text_template"
      },
      "class_name": "class_name",
      "score": 0
    }
  ],
  "class_name": "class_name",
  "image_nodes": [
    {
      "node": {
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "image_size": 0,
        "page_index": 0,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "inferred_search_filters": {
    "filters": [
      {
        "key": "key",
        "value": 0,
        "operator": "=="
      }
    ],
    "condition": "and"
  },
  "metadata": {
    "foo": "string"
  },
  "page_figure_nodes": [
    {
      "node": {
        "confidence": 0,
        "figure_name": "figure_name",
        "figure_size": 0,
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "page_index": 0,
        "is_likely_noise": true,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "retrieval_latency": {
    "foo": 0
  }
}
```

## Domain Types

### Advanced Mode Transform Config

- `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

  - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

    Configuration for the chunking.

    - `NoneChunkingConfig = object { mode }`

      - `mode: optional "none"`

        - `"none"`

    - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

      - `chunk_size: optional number`

      - `mode: optional "character"`

        - `"character"`

    - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

      - `chunk_overlap: optional number`

      - `chunk_size: optional number`

      - `mode: optional "token"`

        - `"token"`

      - `separator: optional string`

    - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

      - `chunk_overlap: optional number`

      - `chunk_size: optional number`

      - `mode: optional "sentence"`

        - `"sentence"`

      - `paragraph_separator: optional string`

      - `separator: optional string`

    - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

      - `breakpoint_percentile_threshold: optional number`

      - `buffer_size: optional number`

      - `mode: optional "semantic"`

        - `"semantic"`

  - `mode: optional "advanced"`

    - `"advanced"`

  - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

    Configuration for the segmentation.

    - `NoneSegmentationConfig = object { mode }`

      - `mode: optional "none"`

        - `"none"`

    - `PageSegmentationConfig = object { mode, page_separator }`

      - `mode: optional "page"`

        - `"page"`

      - `page_separator: optional string`

    - `ElementSegmentationConfig = object { mode }`

      - `mode: optional "element"`

        - `"element"`

### Auto Transform Config

- `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

  - `chunk_overlap: optional number`

    Chunk overlap for the transformation.

  - `chunk_size: optional number`

    Chunk size for the transformation.

  - `mode: optional "auto"`

    - `"auto"`

### Azure OpenAI Embedding

- `AzureOpenAIEmbedding = object { additional_kwargs, api_base, api_key, 12 more }`

  - `additional_kwargs: optional map[unknown]`

    Additional kwargs for the OpenAI API.

  - `api_base: optional string`

    The base URL for Azure deployment.

  - `api_key: optional string`

    The OpenAI API key.

  - `api_version: optional string`

    The version for Azure OpenAI API.

  - `azure_deployment: optional string`

    The Azure deployment to use.

  - `azure_endpoint: optional string`

    The Azure endpoint to use.

  - `class_name: optional string`

  - `default_headers: optional map[string]`

    The default headers for API requests.

  - `dimensions: optional number`

    The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `max_retries: optional number`

    Maximum number of retries.

  - `model_name: optional string`

    The name of the OpenAI embedding model.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `reuse_client: optional boolean`

    Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

  - `timeout: optional number`

    Timeout for each request.

### Azure OpenAI Embedding Config

- `AzureOpenAIEmbeddingConfig = object { component, type }`

  - `component: optional AzureOpenAIEmbedding`

    Configuration for the Azure OpenAI embedding model.

    - `additional_kwargs: optional map[unknown]`

      Additional kwargs for the OpenAI API.

    - `api_base: optional string`

      The base URL for Azure deployment.

    - `api_key: optional string`

      The OpenAI API key.

    - `api_version: optional string`

      The version for Azure OpenAI API.

    - `azure_deployment: optional string`

      The Azure deployment to use.

    - `azure_endpoint: optional string`

      The Azure endpoint to use.

    - `class_name: optional string`

    - `default_headers: optional map[string]`

      The default headers for API requests.

    - `dimensions: optional number`

      The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `max_retries: optional number`

      Maximum number of retries.

    - `model_name: optional string`

      The name of the OpenAI embedding model.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `reuse_client: optional boolean`

      Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

    - `timeout: optional number`

      Timeout for each request.

  - `type: optional "AZURE_EMBEDDING"`

    Type of the embedding model.

    - `"AZURE_EMBEDDING"`

### Bedrock Embedding

- `BedrockEmbedding = object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }`

  - `additional_kwargs: optional map[unknown]`

    Additional kwargs for the bedrock client.

  - `aws_access_key_id: optional string`

    AWS Access Key ID to use

  - `aws_secret_access_key: optional string`

    AWS Secret Access Key to use

  - `aws_session_token: optional string`

    AWS Session Token to use

  - `class_name: optional string`

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `max_retries: optional number`

    The maximum number of API retries.

  - `model_name: optional string`

    The modelId of the Bedrock model to use.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `profile_name: optional string`

    The name of aws profile to use. If not given, then the default profile is used.

  - `region_name: optional string`

    AWS region name to use. Uses region configured in AWS CLI if not passed

  - `timeout: optional number`

    The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

### Bedrock Embedding Config

- `BedrockEmbeddingConfig = object { component, type }`

  - `component: optional BedrockEmbedding`

    Configuration for the Bedrock embedding model.

    - `additional_kwargs: optional map[unknown]`

      Additional kwargs for the bedrock client.

    - `aws_access_key_id: optional string`

      AWS Access Key ID to use

    - `aws_secret_access_key: optional string`

      AWS Secret Access Key to use

    - `aws_session_token: optional string`

      AWS Session Token to use

    - `class_name: optional string`

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `max_retries: optional number`

      The maximum number of API retries.

    - `model_name: optional string`

      The modelId of the Bedrock model to use.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `profile_name: optional string`

      The name of aws profile to use. If not given, then the default profile is used.

    - `region_name: optional string`

      AWS region name to use. Uses region configured in AWS CLI if not passed

    - `timeout: optional number`

      The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

  - `type: optional "BEDROCK_EMBEDDING"`

    Type of the embedding model.

    - `"BEDROCK_EMBEDDING"`

### Cohere Embedding

- `CohereEmbedding = object { api_key, class_name, embed_batch_size, 5 more }`

  - `api_key: string`

    The Cohere API key.

  - `class_name: optional string`

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `embedding_type: optional string`

    Embedding type. If not provided float embedding_type is used when needed.

  - `input_type: optional string`

    Model Input type. If not provided, search_document and search_query are used when needed.

  - `model_name: optional string`

    The modelId of the Cohere model to use.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `truncate: optional string`

    Truncation type - START/ END/ NONE

### Cohere Embedding Config

- `CohereEmbeddingConfig = object { component, type }`

  - `component: optional CohereEmbedding`

    Configuration for the Cohere embedding model.

    - `api_key: string`

      The Cohere API key.

    - `class_name: optional string`

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `embedding_type: optional string`

      Embedding type. If not provided float embedding_type is used when needed.

    - `input_type: optional string`

      Model Input type. If not provided, search_document and search_query are used when needed.

    - `model_name: optional string`

      The modelId of the Cohere model to use.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `truncate: optional string`

      Truncation type - START/ END/ NONE

  - `type: optional "COHERE_EMBEDDING"`

    Type of the embedding model.

    - `"COHERE_EMBEDDING"`

### Data Sink Create

- `DataSinkCreate = object { component, name, sink_type }`

  Schema for creating a data sink.

  - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

    Component that implements the data sink

    - `map[unknown]`

    - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

      Cloud Pinecone Vector Store.

      This class is used to store the configuration for a Pinecone vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      api_key (str): API key for authenticating with Pinecone
      index_name (str): name of the Pinecone index
      namespace (optional[str]): namespace to use in the Pinecone index
      insert_kwargs (optional[dict]): additional kwargs to pass during insertion

      - `api_key: string`

        The API key for authenticating with Pinecone

      - `index_name: string`

      - `class_name: optional string`

      - `insert_kwargs: optional map[unknown]`

      - `namespace: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

      - `database: string`

      - `embed_dim: number`

      - `host: string`

      - `password: string`

      - `port: number`

      - `schema_name: string`

      - `table_name: string`

      - `user: string`

      - `class_name: optional string`

      - `hnsw_settings: optional PgVectorHnswSettings`

        HNSW settings for PGVector.

        - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: optional number`

          The number of edges to use during the construction phase.

        - `ef_search: optional number`

          The number of edges to use during the search phase.

        - `m: optional number`

          The number of bi-directional links created for each new element.

        - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: optional boolean`

      - `perform_setup: optional boolean`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

      Cloud Qdrant Vector Store.

      This class is used to store the configuration for a Qdrant vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      collection_name (str): name of the Qdrant collection
      url (str): url of the Qdrant instance
      api_key (str): API key for authenticating with Qdrant
      max_retries (int): maximum number of retries in case of a failure. Defaults to 3
      client_kwargs (dict): additional kwargs to pass to the Qdrant client

      - `api_key: string`

      - `collection_name: string`

      - `url: string`

      - `class_name: optional string`

      - `client_kwargs: optional map[unknown]`

      - `max_retries: optional number`

      - `supports_nested_metadata_filters: optional true`

        - `true`

    - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: string`

      - `search_service_endpoint: string`

      - `class_name: optional string`

      - `client_id: optional string`

      - `client_secret: optional string`

      - `embedding_dimension: optional number`

      - `filterable_metadata_field_keys: optional map[unknown]`

      - `index_name: optional string`

      - `search_service_api_version: optional string`

      - `supports_nested_metadata_filters: optional true`

        - `true`

      - `tenant_id: optional string`

    - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

      Cloud MongoDB Atlas Vector Store.

      This class is used to store the configuration for a MongoDB Atlas vector store,
      so that it can be created and used in LlamaCloud.

      Args:
      mongodb_uri (str): URI for connecting to MongoDB Atlas
      db_name (str): name of the MongoDB database
      collection_name (str): name of the MongoDB collection
      vector_index_name (str): name of the MongoDB Atlas vector index
      fulltext_index_name (str): name of the MongoDB Atlas full-text index

      - `collection_name: string`

      - `db_name: string`

      - `mongodb_uri: string`

      - `class_name: optional string`

      - `embedding_dimension: optional number`

      - `fulltext_index_name: optional string`

      - `supports_nested_metadata_filters: optional boolean`

      - `vector_index_name: optional string`

    - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

      Cloud Milvus Vector Store.

      - `uri: string`

      - `token: optional string`

      - `class_name: optional string`

      - `collection_name: optional string`

      - `embedding_dimension: optional number`

      - `supports_nested_metadata_filters: optional boolean`

    - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

      Cloud AstraDB Vector Store.

      This class is used to store the configuration for an AstraDB vector store, so that it can be
      created and used in LlamaCloud.

      Args:
      token (str): The Astra DB Application Token to use.
      api_endpoint (str): The Astra DB JSON API endpoint for your database.
      collection_name (str): Collection name to use. If not existing, it will be created.
      embedding_dimension (int): Length of the embedding vectors in use.
      keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

      - `token: string`

        The Astra DB Application Token to use

      - `api_endpoint: string`

        The Astra DB JSON API endpoint for your database

      - `collection_name: string`

        Collection name to use. If not existing, it will be created

      - `embedding_dimension: number`

        Length of the embedding vectors in use

      - `class_name: optional string`

      - `keyspace: optional string`

        The keyspace to use. If not provided, 'default_keyspace'

      - `supports_nested_metadata_filters: optional true`

        - `true`

  - `name: string`

    The name of the data sink.

  - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

### Gemini Embedding

- `GeminiEmbedding = object { api_base, api_key, class_name, 7 more }`

  - `api_base: optional string`

    API base to access the model. Defaults to None.

  - `api_key: optional string`

    API key to access the model. Defaults to None.

  - `class_name: optional string`

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `model_name: optional string`

    The modelId of the Gemini model to use.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `output_dimensionality: optional number`

    Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

  - `task_type: optional string`

    The task for embedding model.

  - `title: optional string`

    Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

  - `transport: optional string`

    Transport to access the model. Defaults to None.

### Gemini Embedding Config

- `GeminiEmbeddingConfig = object { component, type }`

  - `component: optional GeminiEmbedding`

    Configuration for the Gemini embedding model.

    - `api_base: optional string`

      API base to access the model. Defaults to None.

    - `api_key: optional string`

      API key to access the model. Defaults to None.

    - `class_name: optional string`

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `model_name: optional string`

      The modelId of the Gemini model to use.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `output_dimensionality: optional number`

      Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

    - `task_type: optional string`

      The task for embedding model.

    - `title: optional string`

      Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

    - `transport: optional string`

      Transport to access the model. Defaults to None.

  - `type: optional "GEMINI_EMBEDDING"`

    Type of the embedding model.

    - `"GEMINI_EMBEDDING"`

### Hugging Face Inference API Embedding

- `HuggingFaceInferenceAPIEmbedding = object { token, class_name, cookies, 9 more }`

  - `token: optional string or boolean`

    Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

    - `string`

    - `boolean`

  - `class_name: optional string`

  - `cookies: optional map[string]`

    Additional cookies to send to the server.

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `headers: optional map[string]`

    Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

  - `model_name: optional string`

    Hugging Face model name. If None, the task will be used.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `pooling: optional "cls" or "mean" or "last"`

    Enum of possible pooling choices with pooling behaviors.

    - `"cls"`

    - `"mean"`

    - `"last"`

  - `query_instruction: optional string`

    Instruction to prepend during query embedding.

  - `task: optional string`

    Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

  - `text_instruction: optional string`

    Instruction to prepend during text embedding.

  - `timeout: optional number`

    The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

### Hugging Face Inference API Embedding Config

- `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

  - `component: optional HuggingFaceInferenceAPIEmbedding`

    Configuration for the HuggingFace Inference API embedding model.

    - `token: optional string or boolean`

      Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

      - `string`

      - `boolean`

    - `class_name: optional string`

    - `cookies: optional map[string]`

      Additional cookies to send to the server.

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `headers: optional map[string]`

      Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

    - `model_name: optional string`

      Hugging Face model name. If None, the task will be used.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `pooling: optional "cls" or "mean" or "last"`

      Enum of possible pooling choices with pooling behaviors.

      - `"cls"`

      - `"mean"`

      - `"last"`

    - `query_instruction: optional string`

      Instruction to prepend during query embedding.

    - `task: optional string`

      Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

    - `text_instruction: optional string`

      Instruction to prepend during text embedding.

    - `timeout: optional number`

      The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

  - `type: optional "HUGGINGFACE_API_EMBEDDING"`

    Type of the embedding model.

    - `"HUGGINGFACE_API_EMBEDDING"`

### Llama Parse Parameters

- `LlamaParseParameters = object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }`

  - `adaptive_long_table: optional boolean`

  - `aggressive_table_extraction: optional boolean`

  - `annotate_links: optional boolean`

  - `auto_mode: optional boolean`

  - `auto_mode_configuration_json: optional string`

  - `auto_mode_trigger_on_image_in_page: optional boolean`

  - `auto_mode_trigger_on_regexp_in_page: optional string`

  - `auto_mode_trigger_on_table_in_page: optional boolean`

  - `auto_mode_trigger_on_text_in_page: optional string`

  - `azure_openai_api_version: optional string`

  - `azure_openai_deployment_name: optional string`

  - `azure_openai_endpoint: optional string`

  - `azure_openai_key: optional string`

  - `bbox_bottom: optional number`

  - `bbox_left: optional number`

  - `bbox_right: optional number`

  - `bbox_top: optional number`

  - `bounding_box: optional string`

  - `compact_markdown_table: optional boolean`

  - `complemental_formatting_instruction: optional string`

  - `content_guideline_instruction: optional string`

  - `continuous_mode: optional boolean`

  - `disable_image_extraction: optional boolean`

  - `disable_ocr: optional boolean`

  - `disable_reconstruction: optional boolean`

  - `do_not_cache: optional boolean`

  - `do_not_unroll_columns: optional boolean`

  - `enable_cost_optimizer: optional boolean`

  - `extract_charts: optional boolean`

  - `extract_layout: optional boolean`

  - `extract_printed_page_number: optional boolean`

  - `fast_mode: optional boolean`

  - `formatting_instruction: optional string`

  - `gpt4o_api_key: optional string`

  - `gpt4o_mode: optional boolean`

  - `guess_xlsx_sheet_name: optional boolean`

  - `hide_footers: optional boolean`

  - `hide_headers: optional boolean`

  - `high_res_ocr: optional boolean`

  - `html_make_all_elements_visible: optional boolean`

  - `html_remove_fixed_elements: optional boolean`

  - `html_remove_navigation_elements: optional boolean`

  - `http_proxy: optional string`

  - `ignore_document_elements_for_layout_detection: optional boolean`

  - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: optional boolean`

  - `input_s3_path: optional string`

  - `input_s3_region: optional string`

  - `input_url: optional string`

  - `internal_is_screenshot_job: optional boolean`

  - `invalidate_cache: optional boolean`

  - `is_formatting_instruction: optional boolean`

  - `job_timeout_extra_time_per_page_in_seconds: optional number`

  - `job_timeout_in_seconds: optional number`

  - `keep_page_separator_when_merging_tables: optional boolean`

  - `languages: optional array of ParsingLanguages`

    - `"af"`

    - `"az"`

    - `"bs"`

    - `"cs"`

    - `"cy"`

    - `"da"`

    - `"de"`

    - `"en"`

    - `"es"`

    - `"et"`

    - `"fr"`

    - `"ga"`

    - `"hr"`

    - `"hu"`

    - `"id"`

    - `"is"`

    - `"it"`

    - `"ku"`

    - `"la"`

    - `"lt"`

    - `"lv"`

    - `"mi"`

    - `"ms"`

    - `"mt"`

    - `"nl"`

    - `"no"`

    - `"oc"`

    - `"pi"`

    - `"pl"`

    - `"pt"`

    - `"ro"`

    - `"rs_latin"`

    - `"sk"`

    - `"sl"`

    - `"sq"`

    - `"sv"`

    - `"sw"`

    - `"tl"`

    - `"tr"`

    - `"uz"`

    - `"vi"`

    - `"ar"`

    - `"fa"`

    - `"ug"`

    - `"ur"`

    - `"bn"`

    - `"as"`

    - `"mni"`

    - `"ru"`

    - `"rs_cyrillic"`

    - `"be"`

    - `"bg"`

    - `"uk"`

    - `"mn"`

    - `"abq"`

    - `"ady"`

    - `"kbd"`

    - `"ava"`

    - `"dar"`

    - `"inh"`

    - `"che"`

    - `"lbe"`

    - `"lez"`

    - `"tab"`

    - `"tjk"`

    - `"hi"`

    - `"mr"`

    - `"ne"`

    - `"bh"`

    - `"mai"`

    - `"ang"`

    - `"bho"`

    - `"mah"`

    - `"sck"`

    - `"new"`

    - `"gom"`

    - `"sa"`

    - `"bgc"`

    - `"th"`

    - `"ch_sim"`

    - `"ch_tra"`

    - `"ja"`

    - `"ko"`

    - `"ta"`

    - `"te"`

    - `"kn"`

  - `layout_aware: optional boolean`

  - `line_level_bounding_box: optional boolean`

  - `markdown_table_multiline_header_separator: optional string`

  - `max_pages: optional number`

  - `max_pages_enforced: optional number`

  - `merge_tables_across_pages_in_markdown: optional boolean`

  - `model: optional string`

  - `outlined_table_extraction: optional boolean`

  - `output_pdf_of_document: optional boolean`

  - `output_s3_path_prefix: optional string`

  - `output_s3_region: optional string`

  - `output_tables_as_HTML: optional boolean`

  - `page_error_tolerance: optional number`

  - `page_footer_prefix: optional string`

  - `page_footer_suffix: optional string`

  - `page_header_prefix: optional string`

  - `page_header_suffix: optional string`

  - `page_prefix: optional string`

  - `page_separator: optional string`

  - `page_suffix: optional string`

  - `parse_mode: optional ParsingMode`

    Enum for representing the mode of parsing to be used.

    - `"parse_page_without_llm"`

    - `"parse_page_with_llm"`

    - `"parse_page_with_lvm"`

    - `"parse_page_with_agent"`

    - `"parse_page_with_layout_agent"`

    - `"parse_document_with_llm"`

    - `"parse_document_with_lvm"`

    - `"parse_document_with_agent"`

  - `parsing_instruction: optional string`

  - `precise_bounding_box: optional boolean`

  - `premium_mode: optional boolean`

  - `presentation_out_of_bounds_content: optional boolean`

  - `presentation_skip_embedded_data: optional boolean`

  - `preserve_layout_alignment_across_pages: optional boolean`

  - `preserve_very_small_text: optional boolean`

  - `preset: optional string`

  - `priority: optional "low" or "medium" or "high" or "critical"`

    The priority for the request. This field may be ignored or overwritten depending on the organization tier.

    - `"low"`

    - `"medium"`

    - `"high"`

    - `"critical"`

  - `project_id: optional string`

  - `remove_hidden_text: optional boolean`

  - `replace_failed_page_mode: optional FailPageMode`

    Enum for representing the different available page error handling modes.

    - `"raw_text"`

    - `"blank_page"`

    - `"error_message"`

  - `replace_failed_page_with_error_message_prefix: optional string`

  - `replace_failed_page_with_error_message_suffix: optional string`

  - `save_images: optional boolean`

  - `skip_diagonal_text: optional boolean`

  - `specialized_chart_parsing_agentic: optional boolean`

  - `specialized_chart_parsing_efficient: optional boolean`

  - `specialized_chart_parsing_plus: optional boolean`

  - `specialized_image_parsing: optional boolean`

  - `spreadsheet_extract_sub_tables: optional boolean`

  - `spreadsheet_force_formula_computation: optional boolean`

  - `spreadsheet_include_hidden_sheets: optional boolean`

  - `strict_mode_buggy_font: optional boolean`

  - `strict_mode_image_extraction: optional boolean`

  - `strict_mode_image_ocr: optional boolean`

  - `strict_mode_reconstruction: optional boolean`

  - `structured_output: optional boolean`

  - `structured_output_json_schema: optional string`

  - `structured_output_json_schema_name: optional string`

  - `system_prompt: optional string`

  - `system_prompt_append: optional string`

  - `take_screenshot: optional boolean`

  - `target_pages: optional string`

  - `tier: optional string`

  - `use_vendor_multimodal_model: optional boolean`

  - `user_prompt: optional string`

  - `vendor_multimodal_api_key: optional string`

  - `vendor_multimodal_model_name: optional string`

  - `version: optional string`

  - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

      Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

      - `"extract.pending"`

      - `"extract.success"`

      - `"extract.error"`

      - `"extract.partial_success"`

      - `"extract.cancelled"`

      - `"parse.pending"`

      - `"parse.running"`

      - `"parse.success"`

      - `"parse.error"`

      - `"parse.partial_success"`

      - `"parse.cancelled"`

      - `"classify.pending"`

      - `"classify.success"`

      - `"classify.error"`

      - `"classify.partial_success"`

      - `"classify.cancelled"`

      - `"unmapped_event"`

    - `webhook_headers: optional map[string]`

      Custom HTTP headers sent with each webhook request (e.g. auth tokens)

    - `webhook_output_format: optional string`

      Response format sent to the webhook: 'string' (default) or 'json'

    - `webhook_url: optional string`

      URL to receive webhook POST notifications

  - `webhook_url: optional string`

### Llm Parameters

- `LlmParameters = object { class_name, model_name, system_prompt, 3 more }`

  - `class_name: optional string`

  - `model_name: optional "GPT_4O" or "GPT_4O_MINI" or "GPT_4_1" or 10 more`

    The name of the model to use for LLM completions.

    - `"GPT_4O"`

    - `"GPT_4O_MINI"`

    - `"GPT_4_1"`

    - `"GPT_4_1_NANO"`

    - `"GPT_4_1_MINI"`

    - `"AZURE_OPENAI_GPT_4O"`

    - `"AZURE_OPENAI_GPT_4O_MINI"`

    - `"AZURE_OPENAI_GPT_4_1"`

    - `"AZURE_OPENAI_GPT_4_1_MINI"`

    - `"AZURE_OPENAI_GPT_4_1_NANO"`

    - `"CLAUDE_4_5_SONNET"`

    - `"BEDROCK_CLAUDE_3_5_SONNET_V1"`

    - `"BEDROCK_CLAUDE_3_5_SONNET_V2"`

  - `system_prompt: optional string`

    The system prompt to use for the completion.

  - `temperature: optional number`

    The temperature value for the model.

  - `use_chain_of_thought_reasoning: optional boolean`

    Whether to use chain of thought reasoning.

  - `use_citation: optional boolean`

    Whether to show citations in the response.

### Managed Ingestion Status Response

- `ManagedIngestionStatusResponse = object { status, deployment_date, effective_at, 2 more }`

  - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: optional string`

    Date of the deployment.

  - `effective_at: optional string`

    When the status is effective

  - `error: optional array of object { job_id, message, step }`

    List of errors that occurred during ingestion.

    - `job_id: string`

      ID of the job that failed.

    - `message: string`

      List of errors that occurred during ingestion.

    - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: optional string`

    ID of the latest job.

### Message Role

- `MessageRole = "system" or "developer" or "user" or 5 more`

  Message role.

  - `"system"`

  - `"developer"`

  - `"user"`

  - `"assistant"`

  - `"function"`

  - `"tool"`

  - `"chatbot"`

  - `"model"`

### Metadata Filters

- `MetadataFilters = object { filters, condition }`

  Metadata filters for vector stores.

  - `filters: array of object { key, value, operator }  or MetadataFilters`

    - `MetadataFilter = object { key, value, operator }`

      Comprehensive metadata filter for vector stores to support more operators.

      Value uses Strict types, as int, float and str are compatible types and were all
      converted to string before.

      See: https://docs.pydantic.dev/latest/usage/types/#strict-types

      - `key: string`

      - `value: number or string or array of string or 2 more`

        - `number`

        - `string`

        - `array of string`

        - `array of number`

        - `array of number`

      - `operator: optional "==" or ">" or "<" or 11 more`

        Vector store filter operator.

        - `"=="`

        - `">"`

        - `"<"`

        - `"!="`

        - `">="`

        - `"<="`

        - `"in"`

        - `"nin"`

        - `"any"`

        - `"all"`

        - `"text_match"`

        - `"text_match_insensitive"`

        - `"contains"`

        - `"is_empty"`

    - `MetadataFilters = object { filters, condition }`

      Metadata filters for vector stores.

  - `condition: optional "and" or "or" or "not"`

    Vector store filter conditions to combine different filters.

    - `"and"`

    - `"or"`

    - `"not"`

### OpenAI Embedding

- `OpenAIEmbedding = object { additional_kwargs, api_base, api_key, 10 more }`

  - `additional_kwargs: optional map[unknown]`

    Additional kwargs for the OpenAI API.

  - `api_base: optional string`

    The base URL for OpenAI API.

  - `api_key: optional string`

    The OpenAI API key.

  - `api_version: optional string`

    The version for OpenAI API.

  - `class_name: optional string`

  - `default_headers: optional map[string]`

    The default headers for API requests.

  - `dimensions: optional number`

    The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `max_retries: optional number`

    Maximum number of retries.

  - `model_name: optional string`

    The name of the OpenAI embedding model.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

  - `reuse_client: optional boolean`

    Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

  - `timeout: optional number`

    Timeout for each request.

### OpenAI Embedding Config

- `OpenAIEmbeddingConfig = object { component, type }`

  - `component: optional OpenAIEmbedding`

    Configuration for the OpenAI embedding model.

    - `additional_kwargs: optional map[unknown]`

      Additional kwargs for the OpenAI API.

    - `api_base: optional string`

      The base URL for OpenAI API.

    - `api_key: optional string`

      The OpenAI API key.

    - `api_version: optional string`

      The version for OpenAI API.

    - `class_name: optional string`

    - `default_headers: optional map[string]`

      The default headers for API requests.

    - `dimensions: optional number`

      The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `max_retries: optional number`

      Maximum number of retries.

    - `model_name: optional string`

      The name of the OpenAI embedding model.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

    - `reuse_client: optional boolean`

      Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

    - `timeout: optional number`

      Timeout for each request.

  - `type: optional "OPENAI_EMBEDDING"`

    Type of the embedding model.

    - `"OPENAI_EMBEDDING"`

### Page Figure Node With Score

- `PageFigureNodeWithScore = object { node, score, class_name }`

  Page figure metadata with score

  - `node: object { confidence, figure_name, figure_size, 4 more }`

    - `confidence: number`

      The confidence of the figure

    - `figure_name: string`

      The name of the figure

    - `figure_size: number`

      The size of the figure in bytes

    - `file_id: string`

      The ID of the file that the figure was taken from

    - `page_index: number`

      The index of the page for which the figure is taken (0-indexed)

    - `is_likely_noise: optional boolean`

      Whether the figure is likely to be noise

    - `metadata: optional map[unknown]`

      Metadata for the figure

  - `score: number`

    The score of the figure node

  - `class_name: optional string`

### Page Screenshot Node With Score

- `PageScreenshotNodeWithScore = object { node, score, class_name }`

  Page screenshot metadata with score

  - `node: object { file_id, image_size, page_index, metadata }`

    - `file_id: string`

      The ID of the file that the page screenshot was taken from

    - `image_size: number`

      The size of the image in bytes

    - `page_index: number`

      The index of the page for which the screenshot is taken (0-indexed)

    - `metadata: optional map[unknown]`

      Metadata for the screenshot

  - `score: number`

    The score of the screenshot node

  - `class_name: optional string`

### Pipeline

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Pipeline Create

- `PipelineCreate = object { name, data_sink, data_sink_id, 10 more }`

  Schema for creating a pipeline.

  - `name: string`

  - `data_sink: optional DataSinkCreate`

    Schema for creating a data sink.

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

  - `data_sink_id: optional string`

    Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.

  - `embedding_config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `embedding_model_config_id: optional string`

    Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional string`

    Status of the pipeline deployment.

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

### Pipeline Metadata Config

- `PipelineMetadataConfig = object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }`

  - `excluded_embed_metadata_keys: optional array of string`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: optional array of string`

    List of metadata keys to exclude from LLM during retrieval

### Pipeline Type

- `PipelineType = "PLAYGROUND" or "MANAGED"`

  Enum for representing the type of a pipeline

  - `"PLAYGROUND"`

  - `"MANAGED"`

### Preset Retrieval Params

- `PresetRetrievalParams = object { alpha, class_name, dense_similarity_cutoff, 11 more }`

  Schema for the search params for an retrieval execution that can be preset for a pipeline.

  - `alpha: optional number`

    Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

  - `class_name: optional string`

  - `dense_similarity_cutoff: optional number`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: optional number`

    Number of nodes for dense retrieval.

  - `enable_reranking: optional boolean`

    Enable reranking for retrieval

  - `files_top_k: optional number`

    Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

  - `rerank_top_n: optional number`

    Number of reranked nodes for returning.

  - `retrieval_mode: optional RetrievalMode`

    The retrieval mode for the query.

    - `"chunks"`

    - `"files_via_metadata"`

    - `"files_via_content"`

    - `"auto_routed"`

  - `retrieve_image_nodes: optional boolean`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: optional boolean`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: optional boolean`

    Whether to retrieve page screenshot nodes.

  - `search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

    JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `sparse_similarity_top_k: optional number`

    Number of nodes for sparse retrieval.

### Retrieval Mode

- `RetrievalMode = "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"`

  - `"chunks"`

  - `"files_via_metadata"`

  - `"files_via_content"`

  - `"auto_routed"`

### Sparse Model Config

- `SparseModelConfig = object { class_name, model_type }`

  Configuration for sparse embedding models used in hybrid search.

  This allows users to choose between Splade and BM25 models for
  sparse retrieval in managed data sinks.

  - `class_name: optional string`

  - `model_type: optional "splade" or "bm25" or "auto"`

    The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

    - `"splade"`

    - `"bm25"`

    - `"auto"`

### Vertex AI Embedding Config

- `VertexAIEmbeddingConfig = object { component, type }`

  - `component: optional VertexTextEmbedding`

    Configuration for the VertexAI embedding model.

    - `client_email: string`

      The client email for the VertexAI credentials.

    - `location: string`

      The default location to use when making API calls.

    - `private_key: string`

      The private key for the VertexAI credentials.

    - `private_key_id: string`

      The private key ID for the VertexAI credentials.

    - `project: string`

      The default GCP project to use when making Vertex API calls.

    - `token_uri: string`

      The token URI for the VertexAI credentials.

    - `additional_kwargs: optional map[unknown]`

      Additional kwargs for the Vertex.

    - `class_name: optional string`

    - `embed_batch_size: optional number`

      The batch size for embedding calls.

    - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

      The embedding mode to use.

      - `"default"`

      - `"classification"`

      - `"clustering"`

      - `"similarity"`

      - `"retrieval"`

    - `model_name: optional string`

      The modelId of the VertexAI model to use.

    - `num_workers: optional number`

      The number of workers to use for async embedding calls.

  - `type: optional "VERTEXAI_EMBEDDING"`

    Type of the embedding model.

    - `"VERTEXAI_EMBEDDING"`

### Vertex Text Embedding

- `VertexTextEmbedding = object { client_email, location, private_key, 9 more }`

  - `client_email: string`

    The client email for the VertexAI credentials.

  - `location: string`

    The default location to use when making API calls.

  - `private_key: string`

    The private key for the VertexAI credentials.

  - `private_key_id: string`

    The private key ID for the VertexAI credentials.

  - `project: string`

    The default GCP project to use when making Vertex API calls.

  - `token_uri: string`

    The token URI for the VertexAI credentials.

  - `additional_kwargs: optional map[unknown]`

    Additional kwargs for the Vertex.

  - `class_name: optional string`

  - `embed_batch_size: optional number`

    The batch size for embedding calls.

  - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

    The embedding mode to use.

    - `"default"`

    - `"classification"`

    - `"clustering"`

    - `"similarity"`

    - `"retrieval"`

  - `model_name: optional string`

    The modelId of the VertexAI model to use.

  - `num_workers: optional number`

    The number of workers to use for async embedding calls.

### Pipeline List Response

- `PipelineListResponse = array of Pipeline`

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Pipeline Retrieve Response

- `PipelineRetrieveResponse = object { pipeline_id, retrieval_nodes, class_name, 5 more }`

  Schema for the result of an retrieval execution.

  - `pipeline_id: string`

    The ID of the pipeline that the query was retrieved against.

  - `retrieval_nodes: array of object { node, class_name, score }`

    The nodes retrieved by the pipeline for the given query.

    - `node: TextNode`

      Provided for backward compatibility.

      - `class_name: optional string`

      - `embedding: optional array of number`

        Embedding of the node.

      - `end_char_idx: optional number`

        End char index of the node.

      - `excluded_embed_metadata_keys: optional array of string`

        Metadata keys that are excluded from text for the embed model.

      - `excluded_llm_metadata_keys: optional array of string`

        Metadata keys that are excluded from text for the LLM.

      - `extra_info: optional map[unknown]`

        A flat dictionary of metadata fields

      - `id_: optional string`

        Unique ID of the node.

      - `metadata_seperator: optional string`

        Separator between metadata fields when converting to string.

      - `metadata_template: optional string`

        Template for how metadata is formatted, with {key} and {value} placeholders.

      - `mimetype: optional string`

        MIME type of the node content.

      - `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

        A mapping of relationships to other node information.

        - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

          - `node_id: string`

          - `class_name: optional string`

          - `hash: optional string`

          - `metadata: optional map[unknown]`

          - `node_type: optional "1" or "2" or "3" or 2 more or string`

            - `ObjectType = "1" or "2" or "3" or 2 more`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `string`

        - `array of object { node_id, class_name, hash, 2 more }`

          - `node_id: string`

          - `class_name: optional string`

          - `hash: optional string`

          - `metadata: optional map[unknown]`

          - `node_type: optional "1" or "2" or "3" or 2 more or string`

            - `ObjectType = "1" or "2" or "3" or 2 more`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `string`

      - `start_char_idx: optional number`

        Start char index of the node.

      - `text: optional string`

        Text content of the node.

      - `text_template: optional string`

        Template for how text is formatted, with {content} and {metadata_str} placeholders.

    - `class_name: optional string`

    - `score: optional number`

  - `class_name: optional string`

  - `image_nodes: optional array of PageScreenshotNodeWithScore`

    The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'.

    - `node: object { file_id, image_size, page_index, metadata }`

      - `file_id: string`

        The ID of the file that the page screenshot was taken from

      - `image_size: number`

        The size of the image in bytes

      - `page_index: number`

        The index of the page for which the screenshot is taken (0-indexed)

      - `metadata: optional map[unknown]`

        Metadata for the screenshot

    - `score: number`

      The score of the screenshot node

    - `class_name: optional string`

  - `inferred_search_filters: optional MetadataFilters`

    Metadata filters for vector stores.

    - `filters: array of object { key, value, operator }  or MetadataFilters`

      - `MetadataFilter = object { key, value, operator }`

        Comprehensive metadata filter for vector stores to support more operators.

        Value uses Strict types, as int, float and str are compatible types and were all
        converted to string before.

        See: https://docs.pydantic.dev/latest/usage/types/#strict-types

        - `key: string`

        - `value: number or string or array of string or 2 more`

          - `number`

          - `string`

          - `array of string`

          - `array of number`

          - `array of number`

        - `operator: optional "==" or ">" or "<" or 11 more`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `MetadataFilters = object { filters, condition }`

        Metadata filters for vector stores.

    - `condition: optional "and" or "or" or "not"`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `metadata: optional map[string]`

    Metadata associated with the retrieval execution

  - `page_figure_nodes: optional array of PageFigureNodeWithScore`

    The page figure nodes retrieved by the pipeline for the given query.

    - `node: object { confidence, figure_name, figure_size, 4 more }`

      - `confidence: number`

        The confidence of the figure

      - `figure_name: string`

        The name of the figure

      - `figure_size: number`

        The size of the figure in bytes

      - `file_id: string`

        The ID of the file that the figure was taken from

      - `page_index: number`

        The index of the page for which the figure is taken (0-indexed)

      - `is_likely_noise: optional boolean`

        Whether the figure is likely to be noise

      - `metadata: optional map[unknown]`

        Metadata for the figure

    - `score: number`

      The score of the figure node

    - `class_name: optional string`

  - `retrieval_latency: optional map[number]`

    The end-to-end latency for retrieval and reranking.

# Sync

## Sync Pipeline

**post** `/api/v1/pipelines/{pipeline_id}/sync`

Trigger an incremental sync for a managed pipeline.

Processes new and updated documents from data sources and
files, then updates the index for retrieval.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/sync \
    -X POST \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Cancel Pipeline Sync

**post** `/api/v1/pipelines/{pipeline_id}/sync/cancel`

Cancel all running sync jobs for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/sync/cancel \
    -X POST \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

# Data Sources

## List Pipeline Data Sources

**get** `/api/v1/pipelines/{pipeline_id}/data-sources`

Get data sources for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `id: string`

  Unique identifier

- `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

  Component that implements the data source

  - `map[unknown]`

  - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

    - `bucket: string`

      The name of the S3 bucket to read from.

    - `aws_access_id: optional string`

      The AWS access ID to use for authentication.

    - `aws_access_secret: optional string`

      The AWS access secret to use for authentication.

    - `class_name: optional string`

    - `prefix: optional string`

      The prefix of the S3 objects to read from.

    - `regex_pattern: optional string`

      The regex pattern to filter S3 objects. Must be a valid regex pattern.

    - `s3_endpoint_url: optional string`

      The S3 endpoint URL to use for authentication.

    - `supports_access_control: optional boolean`

  - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

    - `account_url: string`

      The Azure Storage Blob account URL to use for authentication.

    - `container_name: string`

      The name of the Azure Storage Blob container to read from.

    - `account_key: optional string`

      The Azure Storage Blob account key to use for authentication.

    - `account_name: optional string`

      The Azure Storage Blob account name to use for authentication.

    - `blob: optional string`

      The blob name to read from.

    - `class_name: optional string`

    - `client_id: optional string`

      The Azure AD client ID to use for authentication.

    - `client_secret: optional string`

      The Azure AD client secret to use for authentication.

    - `prefix: optional string`

      The prefix of the Azure Storage Blob objects to read from.

    - `supports_access_control: optional boolean`

    - `tenant_id: optional string`

      The Azure AD tenant ID to use for authentication.

  - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

    - `folder_id: string`

      The ID of the Google Drive folder to read from.

    - `class_name: optional string`

    - `service_account_key: optional map[string]`

      A dictionary containing secret values

    - `supports_access_control: optional boolean`

  - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

    - `client_id: string`

      The client ID to use for authentication.

    - `client_secret: string`

      The client secret to use for authentication.

    - `tenant_id: string`

      The tenant ID to use for authentication.

    - `user_principal_name: string`

      The user principal name to use for authentication.

    - `class_name: optional string`

    - `folder_id: optional string`

      The ID of the OneDrive folder to read from.

    - `folder_path: optional string`

      The path of the OneDrive folder to read from.

    - `required_exts: optional array of string`

      The list of required file extensions.

    - `supports_access_control: optional true`

      - `true`

  - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

    - `client_id: string`

      The client ID to use for authentication.

    - `client_secret: string`

      The client secret to use for authentication.

    - `tenant_id: string`

      The tenant ID to use for authentication.

    - `class_name: optional string`

    - `drive_name: optional string`

      The name of the Sharepoint drive to read from.

    - `exclude_path_patterns: optional array of string`

      List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

    - `folder_id: optional string`

      The ID of the Sharepoint folder to read from.

    - `folder_path: optional string`

      The path of the Sharepoint folder to read from.

    - `get_permissions: optional boolean`

      Whether to get permissions for the sharepoint site.

    - `include_path_patterns: optional array of string`

      List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

    - `required_exts: optional array of string`

      The list of required file extensions.

    - `site_id: optional string`

      The ID of the SharePoint site to download from.

    - `site_name: optional string`

      The name of the SharePoint site to download from.

    - `supports_access_control: optional true`

      - `true`

  - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

    - `slack_token: string`

      Slack Bot Token.

    - `channel_ids: optional string`

      Slack Channel.

    - `channel_patterns: optional string`

      Slack Channel name pattern.

    - `class_name: optional string`

    - `earliest_date: optional string`

      Earliest date.

    - `earliest_date_timestamp: optional number`

      Earliest date timestamp.

    - `latest_date: optional string`

      Latest date.

    - `latest_date_timestamp: optional number`

      Latest date timestamp.

    - `supports_access_control: optional boolean`

  - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

    - `integration_token: string`

      The integration token to use for authentication.

    - `class_name: optional string`

    - `database_ids: optional string`

      The Notion Database Id to read content from.

    - `page_ids: optional string`

      The Page ID's of the Notion to read from.

    - `supports_access_control: optional boolean`

  - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Confluence APIs.

    - `server_url: string`

      The server URL of the Confluence instance.

    - `api_token: optional string`

      The API token to use for authentication.

    - `class_name: optional string`

    - `cql: optional string`

      The CQL query to use for fetching pages.

    - `failure_handling: optional FailureHandlingConfig`

      Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

      Example:
      {
      "skip_list_failures": true
      }

      Currently supports:

      - skip_list_failures: Skip failed batches/lists and continue processing

      - `skip_list_failures: optional boolean`

        Whether to skip failed batches/lists and continue processing

    - `index_restricted_pages: optional boolean`

      Whether to index restricted pages.

    - `keep_markdown_format: optional boolean`

      Whether to keep the markdown format.

    - `label: optional string`

      The label to use for fetching pages.

    - `page_ids: optional string`

      The page IDs of the Confluence to read from.

    - `space_key: optional string`

      The space key to read from.

    - `supports_access_control: optional boolean`

    - `user_name: optional string`

      The username to use for authentication.

  - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

    Cloud Jira Data Source integrating JiraReader.

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Jira APIs.

    - `query: string`

      JQL (Jira Query Language) query to search.

    - `api_token: optional string`

      The API/ Access Token used for Basic, PAT and OAuth2 authentication.

    - `class_name: optional string`

    - `cloud_id: optional string`

      The cloud ID, used in case of OAuth2.

    - `email: optional string`

      The email address to use for authentication.

    - `server_url: optional string`

      The server url for Jira Cloud.

    - `supports_access_control: optional boolean`

  - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

    Cloud Jira Data Source integrating JiraReaderV2.

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Jira APIs.

    - `query: string`

      JQL (Jira Query Language) query to search.

    - `server_url: string`

      The server url for Jira Cloud.

    - `api_token: optional string`

      The API Access Token used for Basic, PAT and OAuth2 authentication.

    - `api_version: optional "2" or "3"`

      Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

      - `"2"`

      - `"3"`

    - `class_name: optional string`

    - `cloud_id: optional string`

      The cloud ID, used in case of OAuth2.

    - `email: optional string`

      The email address to use for authentication.

    - `expand: optional string`

      Fields to expand in the response.

    - `fields: optional array of string`

      List of fields to retrieve from Jira. If None, retrieves all fields.

    - `get_permissions: optional boolean`

      Whether to fetch project role permissions and issue-level security

    - `requests_per_minute: optional number`

      Rate limit for Jira API requests per minute.

    - `supports_access_control: optional boolean`

  - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

    - `authentication_mechanism: "developer_token" or "ccg"`

      The type of authentication to use (Developer Token or CCG)

      - `"developer_token"`

      - `"ccg"`

    - `class_name: optional string`

    - `client_id: optional string`

      Box API key used for identifying the application the user is authenticating with

    - `client_secret: optional string`

      Box API secret used for making auth requests.

    - `developer_token: optional string`

      Developer token for authentication if authentication_mechanism is 'developer_token'.

    - `enterprise_id: optional string`

      Box Enterprise ID, if provided authenticates as service.

    - `folder_id: optional string`

      The ID of the Box folder to read from.

    - `supports_access_control: optional boolean`

    - `user_id: optional string`

      Box User ID, if provided authenticates as user.

- `data_source_id: string`

  The ID of the data source.

- `last_synced_at: string`

  The last time the data source was automatically synced.

- `name: string`

  The name of the data source.

- `pipeline_id: string`

  The ID of the pipeline.

- `project_id: string`

- `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

  - `"S3"`

  - `"AZURE_STORAGE_BLOB"`

  - `"GOOGLE_DRIVE"`

  - `"MICROSOFT_ONEDRIVE"`

  - `"MICROSOFT_SHAREPOINT"`

  - `"SLACK"`

  - `"NOTION_PAGE"`

  - `"CONFLUENCE"`

  - `"JIRA"`

  - `"JIRA_V2"`

  - `"BOX"`

- `created_at: optional string`

  Creation datetime

- `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

  Custom metadata that will be present on all data loaded from the data source

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

  The status of the data source in the pipeline.

  - `"NOT_STARTED"`

  - `"IN_PROGRESS"`

  - `"SUCCESS"`

  - `"ERROR"`

  - `"CANCELLED"`

- `status_updated_at: optional string`

  The last time the status was updated.

- `sync_interval: optional number`

  The interval at which the data source should be synced.

- `sync_schedule_set_by: optional string`

  The id of the user who set the sync schedule.

- `updated_at: optional string`

  Update datetime

- `version_metadata: optional DataSourceReaderVersionMetadata`

  Version metadata for the data source

  - `reader_version: optional "1.0" or "2.0" or "2.1"`

    The version of the reader to use for this data source.

    - `"1.0"`

    - `"2.0"`

    - `"2.1"`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/data-sources \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "last_synced_at": "2019-12-27T18:11:19.117Z",
    "name": "name",
    "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "source_type": "S3",
    "created_at": "2019-12-27T18:11:19.117Z",
    "custom_metadata": {
      "foo": {
        "foo": "bar"
      }
    },
    "status": "NOT_STARTED",
    "status_updated_at": "2019-12-27T18:11:19.117Z",
    "sync_interval": 0,
    "sync_schedule_set_by": "sync_schedule_set_by",
    "updated_at": "2019-12-27T18:11:19.117Z",
    "version_metadata": {
      "reader_version": "1.0"
    }
  }
]
```

## Add Data Sources To Pipeline

**put** `/api/v1/pipelines/{pipeline_id}/data-sources`

Add data sources to a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `body: array of object { data_source_id, sync_interval }`

  - `data_source_id: string`

    The ID of the data source.

  - `sync_interval: optional number`

    The interval at which the data source should be synced. Valid values are: 21600, 43200, 86400

### Returns

- `id: string`

  Unique identifier

- `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

  Component that implements the data source

  - `map[unknown]`

  - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

    - `bucket: string`

      The name of the S3 bucket to read from.

    - `aws_access_id: optional string`

      The AWS access ID to use for authentication.

    - `aws_access_secret: optional string`

      The AWS access secret to use for authentication.

    - `class_name: optional string`

    - `prefix: optional string`

      The prefix of the S3 objects to read from.

    - `regex_pattern: optional string`

      The regex pattern to filter S3 objects. Must be a valid regex pattern.

    - `s3_endpoint_url: optional string`

      The S3 endpoint URL to use for authentication.

    - `supports_access_control: optional boolean`

  - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

    - `account_url: string`

      The Azure Storage Blob account URL to use for authentication.

    - `container_name: string`

      The name of the Azure Storage Blob container to read from.

    - `account_key: optional string`

      The Azure Storage Blob account key to use for authentication.

    - `account_name: optional string`

      The Azure Storage Blob account name to use for authentication.

    - `blob: optional string`

      The blob name to read from.

    - `class_name: optional string`

    - `client_id: optional string`

      The Azure AD client ID to use for authentication.

    - `client_secret: optional string`

      The Azure AD client secret to use for authentication.

    - `prefix: optional string`

      The prefix of the Azure Storage Blob objects to read from.

    - `supports_access_control: optional boolean`

    - `tenant_id: optional string`

      The Azure AD tenant ID to use for authentication.

  - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

    - `folder_id: string`

      The ID of the Google Drive folder to read from.

    - `class_name: optional string`

    - `service_account_key: optional map[string]`

      A dictionary containing secret values

    - `supports_access_control: optional boolean`

  - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

    - `client_id: string`

      The client ID to use for authentication.

    - `client_secret: string`

      The client secret to use for authentication.

    - `tenant_id: string`

      The tenant ID to use for authentication.

    - `user_principal_name: string`

      The user principal name to use for authentication.

    - `class_name: optional string`

    - `folder_id: optional string`

      The ID of the OneDrive folder to read from.

    - `folder_path: optional string`

      The path of the OneDrive folder to read from.

    - `required_exts: optional array of string`

      The list of required file extensions.

    - `supports_access_control: optional true`

      - `true`

  - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

    - `client_id: string`

      The client ID to use for authentication.

    - `client_secret: string`

      The client secret to use for authentication.

    - `tenant_id: string`

      The tenant ID to use for authentication.

    - `class_name: optional string`

    - `drive_name: optional string`

      The name of the Sharepoint drive to read from.

    - `exclude_path_patterns: optional array of string`

      List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

    - `folder_id: optional string`

      The ID of the Sharepoint folder to read from.

    - `folder_path: optional string`

      The path of the Sharepoint folder to read from.

    - `get_permissions: optional boolean`

      Whether to get permissions for the sharepoint site.

    - `include_path_patterns: optional array of string`

      List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

    - `required_exts: optional array of string`

      The list of required file extensions.

    - `site_id: optional string`

      The ID of the SharePoint site to download from.

    - `site_name: optional string`

      The name of the SharePoint site to download from.

    - `supports_access_control: optional true`

      - `true`

  - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

    - `slack_token: string`

      Slack Bot Token.

    - `channel_ids: optional string`

      Slack Channel.

    - `channel_patterns: optional string`

      Slack Channel name pattern.

    - `class_name: optional string`

    - `earliest_date: optional string`

      Earliest date.

    - `earliest_date_timestamp: optional number`

      Earliest date timestamp.

    - `latest_date: optional string`

      Latest date.

    - `latest_date_timestamp: optional number`

      Latest date timestamp.

    - `supports_access_control: optional boolean`

  - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

    - `integration_token: string`

      The integration token to use for authentication.

    - `class_name: optional string`

    - `database_ids: optional string`

      The Notion Database Id to read content from.

    - `page_ids: optional string`

      The Page ID's of the Notion to read from.

    - `supports_access_control: optional boolean`

  - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Confluence APIs.

    - `server_url: string`

      The server URL of the Confluence instance.

    - `api_token: optional string`

      The API token to use for authentication.

    - `class_name: optional string`

    - `cql: optional string`

      The CQL query to use for fetching pages.

    - `failure_handling: optional FailureHandlingConfig`

      Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

      Example:
      {
      "skip_list_failures": true
      }

      Currently supports:

      - skip_list_failures: Skip failed batches/lists and continue processing

      - `skip_list_failures: optional boolean`

        Whether to skip failed batches/lists and continue processing

    - `index_restricted_pages: optional boolean`

      Whether to index restricted pages.

    - `keep_markdown_format: optional boolean`

      Whether to keep the markdown format.

    - `label: optional string`

      The label to use for fetching pages.

    - `page_ids: optional string`

      The page IDs of the Confluence to read from.

    - `space_key: optional string`

      The space key to read from.

    - `supports_access_control: optional boolean`

    - `user_name: optional string`

      The username to use for authentication.

  - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

    Cloud Jira Data Source integrating JiraReader.

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Jira APIs.

    - `query: string`

      JQL (Jira Query Language) query to search.

    - `api_token: optional string`

      The API/ Access Token used for Basic, PAT and OAuth2 authentication.

    - `class_name: optional string`

    - `cloud_id: optional string`

      The cloud ID, used in case of OAuth2.

    - `email: optional string`

      The email address to use for authentication.

    - `server_url: optional string`

      The server url for Jira Cloud.

    - `supports_access_control: optional boolean`

  - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

    Cloud Jira Data Source integrating JiraReaderV2.

    - `authentication_mechanism: string`

      Type of Authentication for connecting to Jira APIs.

    - `query: string`

      JQL (Jira Query Language) query to search.

    - `server_url: string`

      The server url for Jira Cloud.

    - `api_token: optional string`

      The API Access Token used for Basic, PAT and OAuth2 authentication.

    - `api_version: optional "2" or "3"`

      Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

      - `"2"`

      - `"3"`

    - `class_name: optional string`

    - `cloud_id: optional string`

      The cloud ID, used in case of OAuth2.

    - `email: optional string`

      The email address to use for authentication.

    - `expand: optional string`

      Fields to expand in the response.

    - `fields: optional array of string`

      List of fields to retrieve from Jira. If None, retrieves all fields.

    - `get_permissions: optional boolean`

      Whether to fetch project role permissions and issue-level security

    - `requests_per_minute: optional number`

      Rate limit for Jira API requests per minute.

    - `supports_access_control: optional boolean`

  - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

    - `authentication_mechanism: "developer_token" or "ccg"`

      The type of authentication to use (Developer Token or CCG)

      - `"developer_token"`

      - `"ccg"`

    - `class_name: optional string`

    - `client_id: optional string`

      Box API key used for identifying the application the user is authenticating with

    - `client_secret: optional string`

      Box API secret used for making auth requests.

    - `developer_token: optional string`

      Developer token for authentication if authentication_mechanism is 'developer_token'.

    - `enterprise_id: optional string`

      Box Enterprise ID, if provided authenticates as service.

    - `folder_id: optional string`

      The ID of the Box folder to read from.

    - `supports_access_control: optional boolean`

    - `user_id: optional string`

      Box User ID, if provided authenticates as user.

- `data_source_id: string`

  The ID of the data source.

- `last_synced_at: string`

  The last time the data source was automatically synced.

- `name: string`

  The name of the data source.

- `pipeline_id: string`

  The ID of the pipeline.

- `project_id: string`

- `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

  - `"S3"`

  - `"AZURE_STORAGE_BLOB"`

  - `"GOOGLE_DRIVE"`

  - `"MICROSOFT_ONEDRIVE"`

  - `"MICROSOFT_SHAREPOINT"`

  - `"SLACK"`

  - `"NOTION_PAGE"`

  - `"CONFLUENCE"`

  - `"JIRA"`

  - `"JIRA_V2"`

  - `"BOX"`

- `created_at: optional string`

  Creation datetime

- `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

  Custom metadata that will be present on all data loaded from the data source

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

  The status of the data source in the pipeline.

  - `"NOT_STARTED"`

  - `"IN_PROGRESS"`

  - `"SUCCESS"`

  - `"ERROR"`

  - `"CANCELLED"`

- `status_updated_at: optional string`

  The last time the status was updated.

- `sync_interval: optional number`

  The interval at which the data source should be synced.

- `sync_schedule_set_by: optional string`

  The id of the user who set the sync schedule.

- `updated_at: optional string`

  Update datetime

- `version_metadata: optional DataSourceReaderVersionMetadata`

  Version metadata for the data source

  - `reader_version: optional "1.0" or "2.0" or "2.1"`

    The version of the reader to use for this data source.

    - `"1.0"`

    - `"2.0"`

    - `"2.1"`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/data-sources \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '[
          {
            "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
            "sync_interval": 0
          }
        ]'
```

#### Response

```json
[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "last_synced_at": "2019-12-27T18:11:19.117Z",
    "name": "name",
    "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "source_type": "S3",
    "created_at": "2019-12-27T18:11:19.117Z",
    "custom_metadata": {
      "foo": {
        "foo": "bar"
      }
    },
    "status": "NOT_STARTED",
    "status_updated_at": "2019-12-27T18:11:19.117Z",
    "sync_interval": 0,
    "sync_schedule_set_by": "sync_schedule_set_by",
    "updated_at": "2019-12-27T18:11:19.117Z",
    "version_metadata": {
      "reader_version": "1.0"
    }
  }
]
```

## Update Pipeline Data Source

**put** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}`

Update the configuration of a data source in a pipeline.

### Path Parameters

- `pipeline_id: string`

- `data_source_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `sync_interval: optional number`

  The interval at which the data source should be synced.

### Returns

- `PipelineDataSource = object { id, component, data_source_id, 13 more }`

  Schema for a data source in a pipeline.

  - `id: string`

    Unique identifier

  - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

    Component that implements the data source

    - `map[unknown]`

    - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

      - `bucket: string`

        The name of the S3 bucket to read from.

      - `aws_access_id: optional string`

        The AWS access ID to use for authentication.

      - `aws_access_secret: optional string`

        The AWS access secret to use for authentication.

      - `class_name: optional string`

      - `prefix: optional string`

        The prefix of the S3 objects to read from.

      - `regex_pattern: optional string`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: optional string`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: optional boolean`

    - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

      - `account_url: string`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: string`

        The name of the Azure Storage Blob container to read from.

      - `account_key: optional string`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: optional string`

        The Azure Storage Blob account name to use for authentication.

      - `blob: optional string`

        The blob name to read from.

      - `class_name: optional string`

      - `client_id: optional string`

        The Azure AD client ID to use for authentication.

      - `client_secret: optional string`

        The Azure AD client secret to use for authentication.

      - `prefix: optional string`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: optional boolean`

      - `tenant_id: optional string`

        The Azure AD tenant ID to use for authentication.

    - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

      - `folder_id: string`

        The ID of the Google Drive folder to read from.

      - `class_name: optional string`

      - `service_account_key: optional map[string]`

        A dictionary containing secret values

      - `supports_access_control: optional boolean`

    - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `user_principal_name: string`

        The user principal name to use for authentication.

      - `class_name: optional string`

      - `folder_id: optional string`

        The ID of the OneDrive folder to read from.

      - `folder_path: optional string`

        The path of the OneDrive folder to read from.

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `class_name: optional string`

      - `drive_name: optional string`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: optional array of string`

        List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

      - `folder_id: optional string`

        The ID of the Sharepoint folder to read from.

      - `folder_path: optional string`

        The path of the Sharepoint folder to read from.

      - `get_permissions: optional boolean`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: optional array of string`

        List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `site_id: optional string`

        The ID of the SharePoint site to download from.

      - `site_name: optional string`

        The name of the SharePoint site to download from.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

      - `slack_token: string`

        Slack Bot Token.

      - `channel_ids: optional string`

        Slack Channel.

      - `channel_patterns: optional string`

        Slack Channel name pattern.

      - `class_name: optional string`

      - `earliest_date: optional string`

        Earliest date.

      - `earliest_date_timestamp: optional number`

        Earliest date timestamp.

      - `latest_date: optional string`

        Latest date.

      - `latest_date_timestamp: optional number`

        Latest date timestamp.

      - `supports_access_control: optional boolean`

    - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

      - `integration_token: string`

        The integration token to use for authentication.

      - `class_name: optional string`

      - `database_ids: optional string`

        The Notion Database Id to read content from.

      - `page_ids: optional string`

        The Page ID's of the Notion to read from.

      - `supports_access_control: optional boolean`

    - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: string`

        The server URL of the Confluence instance.

      - `api_token: optional string`

        The API token to use for authentication.

      - `class_name: optional string`

      - `cql: optional string`

        The CQL query to use for fetching pages.

      - `failure_handling: optional FailureHandlingConfig`

        Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

        Example:
        {
        "skip_list_failures": true
        }

        Currently supports:

        - skip_list_failures: Skip failed batches/lists and continue processing

        - `skip_list_failures: optional boolean`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: optional boolean`

        Whether to index restricted pages.

      - `keep_markdown_format: optional boolean`

        Whether to keep the markdown format.

      - `label: optional string`

        The label to use for fetching pages.

      - `page_ids: optional string`

        The page IDs of the Confluence to read from.

      - `space_key: optional string`

        The space key to read from.

      - `supports_access_control: optional boolean`

      - `user_name: optional string`

        The username to use for authentication.

    - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `api_token: optional string`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `server_url: optional string`

        The server url for Jira Cloud.

      - `supports_access_control: optional boolean`

    - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `server_url: string`

        The server url for Jira Cloud.

      - `api_token: optional string`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: optional "2" or "3"`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `expand: optional string`

        Fields to expand in the response.

      - `fields: optional array of string`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: optional boolean`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: optional number`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: optional boolean`

    - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

      - `authentication_mechanism: "developer_token" or "ccg"`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: optional string`

      - `client_id: optional string`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: optional string`

        Box API secret used for making auth requests.

      - `developer_token: optional string`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: optional string`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: optional string`

        The ID of the Box folder to read from.

      - `supports_access_control: optional boolean`

      - `user_id: optional string`

        Box User ID, if provided authenticates as user.

  - `data_source_id: string`

    The ID of the data source.

  - `last_synced_at: string`

    The last time the data source was automatically synced.

  - `name: string`

    The name of the data source.

  - `pipeline_id: string`

    The ID of the pipeline.

  - `project_id: string`

  - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: optional string`

    Creation datetime

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata that will be present on all data loaded from the data source

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `sync_interval: optional number`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: optional string`

    The id of the user who set the sync schedule.

  - `updated_at: optional string`

    Update datetime

  - `version_metadata: optional DataSourceReaderVersionMetadata`

    Version metadata for the data source

    - `reader_version: optional "1.0" or "2.0" or "2.1"`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/data-sources/$DATA_SOURCE_ID \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{}'
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "component": {
    "foo": "bar"
  },
  "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "last_synced_at": "2019-12-27T18:11:19.117Z",
  "name": "name",
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "source_type": "S3",
  "created_at": "2019-12-27T18:11:19.117Z",
  "custom_metadata": {
    "foo": {
      "foo": "bar"
    }
  },
  "status": "NOT_STARTED",
  "status_updated_at": "2019-12-27T18:11:19.117Z",
  "sync_interval": 0,
  "sync_schedule_set_by": "sync_schedule_set_by",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "version_metadata": {
    "reader_version": "1.0"
  }
}
```

## Get Pipeline Data Source Status

**get** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/status`

Get the status of a data source for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `data_source_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `ManagedIngestionStatusResponse = object { status, deployment_date, effective_at, 2 more }`

  - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: optional string`

    Date of the deployment.

  - `effective_at: optional string`

    When the status is effective

  - `error: optional array of object { job_id, message, step }`

    List of errors that occurred during ingestion.

    - `job_id: string`

      ID of the job that failed.

    - `message: string`

      List of errors that occurred during ingestion.

    - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: optional string`

    ID of the latest job.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/data-sources/$DATA_SOURCE_ID/status \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "status": "NOT_STARTED",
  "deployment_date": "2019-12-27T18:11:19.117Z",
  "effective_at": "2019-12-27T18:11:19.117Z",
  "error": [
    {
      "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "message": "message",
      "step": "MANAGED_INGESTION"
    }
  ],
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
}
```

## Sync Pipeline Data Source

**post** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/sync`

Run ingestion for the pipeline data source by incrementally updating the data-sink with upstream changes from data-source.

### Path Parameters

- `pipeline_id: string`

- `data_source_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `pipeline_file_ids: optional array of string`

### Returns

- `Pipeline = object { id, embedding_config, name, 15 more }`

  Schema for a pipeline.

  - `id: string`

    Unique identifier

  - `embedding_config: object { component, type }  or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more`

    - `ManagedOpenAIEmbedding = object { component, type }`

      - `component: optional object { class_name, embed_batch_size, model_name, num_workers }`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional "openai-text-embedding-3-small"`

          The name of the OpenAI embedding model.

          - `"openai-text-embedding-3-small"`

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "MANAGED_OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `component: optional AzureOpenAIEmbedding`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for Azure deployment.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for Azure OpenAI API.

        - `azure_deployment: optional string`

          The Azure deployment to use.

        - `azure_endpoint: optional string`

          The Azure endpoint to use.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "AZURE_EMBEDDING"`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `CohereEmbeddingConfig = object { component, type }`

      - `component: optional CohereEmbedding`

        Configuration for the Cohere embedding model.

        - `api_key: string`

          The Cohere API key.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embedding_type: optional string`

          Embedding type. If not provided float embedding_type is used when needed.

        - `input_type: optional string`

          Model Input type. If not provided, search_document and search_query are used when needed.

        - `model_name: optional string`

          The modelId of the Cohere model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `truncate: optional string`

          Truncation type - START/ END/ NONE

      - `type: optional "COHERE_EMBEDDING"`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `GeminiEmbeddingConfig = object { component, type }`

      - `component: optional GeminiEmbedding`

        Configuration for the Gemini embedding model.

        - `api_base: optional string`

          API base to access the model. Defaults to None.

        - `api_key: optional string`

          API key to access the model. Defaults to None.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `model_name: optional string`

          The modelId of the Gemini model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: optional number`

          Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.

        - `task_type: optional string`

          The task for embedding model.

        - `title: optional string`

          Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

        - `transport: optional string`

          Transport to access the model. Defaults to None.

      - `type: optional "GEMINI_EMBEDDING"`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `component: optional HuggingFaceInferenceAPIEmbedding`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: optional string or boolean`

          Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

          - `string`

          - `boolean`

        - `class_name: optional string`

        - `cookies: optional map[string]`

          Additional cookies to send to the server.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `headers: optional map[string]`

          Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.

        - `model_name: optional string`

          Hugging Face model name. If None, the task will be used.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `pooling: optional "cls" or "mean" or "last"`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: optional string`

          Instruction to prepend during query embedding.

        - `task: optional string`

          Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

        - `text_instruction: optional string`

          Instruction to prepend during text embedding.

        - `timeout: optional number`

          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

      - `type: optional "HUGGINGFACE_API_EMBEDDING"`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `OpenAIEmbeddingConfig = object { component, type }`

      - `component: optional OpenAIEmbedding`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the OpenAI API.

        - `api_base: optional string`

          The base URL for OpenAI API.

        - `api_key: optional string`

          The OpenAI API key.

        - `api_version: optional string`

          The version for OpenAI API.

        - `class_name: optional string`

        - `default_headers: optional map[string]`

          The default headers for API requests.

        - `dimensions: optional number`

          The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          Maximum number of retries.

        - `model_name: optional string`

          The name of the OpenAI embedding model.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `reuse_client: optional boolean`

          Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

        - `timeout: optional number`

          Timeout for each request.

      - `type: optional "OPENAI_EMBEDDING"`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `VertexAIEmbeddingConfig = object { component, type }`

      - `component: optional VertexTextEmbedding`

        Configuration for the VertexAI embedding model.

        - `client_email: string`

          The client email for the VertexAI credentials.

        - `location: string`

          The default location to use when making API calls.

        - `private_key: string`

          The private key for the VertexAI credentials.

        - `private_key_id: string`

          The private key ID for the VertexAI credentials.

        - `project: string`

          The default GCP project to use when making Vertex API calls.

        - `token_uri: string`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the Vertex.

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `embed_mode: optional "default" or "classification" or "clustering" or 2 more`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: optional string`

          The modelId of the VertexAI model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

      - `type: optional "VERTEXAI_EMBEDDING"`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `BedrockEmbeddingConfig = object { component, type }`

      - `component: optional BedrockEmbedding`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: optional map[unknown]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: optional string`

          AWS Access Key ID to use

        - `aws_secret_access_key: optional string`

          AWS Secret Access Key to use

        - `aws_session_token: optional string`

          AWS Session Token to use

        - `class_name: optional string`

        - `embed_batch_size: optional number`

          The batch size for embedding calls.

        - `max_retries: optional number`

          The maximum number of API retries.

        - `model_name: optional string`

          The modelId of the Bedrock model to use.

        - `num_workers: optional number`

          The number of workers to use for async embedding calls.

        - `profile_name: optional string`

          The name of aws profile to use. If not given, then the default profile is used.

        - `region_name: optional string`

          AWS region name to use. Uses region configured in AWS CLI if not passed

        - `timeout: optional number`

          The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

      - `type: optional "BEDROCK_EMBEDDING"`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: string`

  - `project_id: string`

  - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: optional string`

      Hash of the embedding config.

    - `parsing_config_hash: optional string`

      Hash of the llama parse parameters.

    - `transform_config_hash: optional string`

      Hash of the transform config.

  - `created_at: optional string`

    Creation datetime

  - `data_sink: optional DataSink`

    Schema for a data sink.

    - `id: string`

      Unique identifier

    - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more`

      Component that implements the data sink

      - `map[unknown]`

      - `CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }`

        Cloud Pinecone Vector Store.

        This class is used to store the configuration for a Pinecone vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        api_key (str): API key for authenticating with Pinecone
        index_name (str): name of the Pinecone index
        namespace (optional[str]): namespace to use in the Pinecone index
        insert_kwargs (optional[dict]): additional kwargs to pass during insertion

        - `api_key: string`

          The API key for authenticating with Pinecone

        - `index_name: string`

        - `class_name: optional string`

        - `insert_kwargs: optional map[unknown]`

        - `namespace: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }`

        - `database: string`

        - `embed_dim: number`

        - `host: string`

        - `password: string`

        - `port: number`

        - `schema_name: string`

        - `table_name: string`

        - `user: string`

        - `class_name: optional string`

        - `hnsw_settings: optional PgVectorHnswSettings`

          HNSW settings for PGVector.

          - `distance_method: optional "l2" or "ip" or "cosine" or 3 more`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: optional number`

            The number of edges to use during the construction phase.

          - `ef_search: optional number`

            The number of edges to use during the search phase.

          - `m: optional number`

            The number of bi-directional links created for each new element.

          - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: optional boolean`

        - `perform_setup: optional boolean`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }`

        Cloud Qdrant Vector Store.

        This class is used to store the configuration for a Qdrant vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        collection_name (str): name of the Qdrant collection
        url (str): url of the Qdrant instance
        api_key (str): API key for authenticating with Qdrant
        max_retries (int): maximum number of retries in case of a failure. Defaults to 3
        client_kwargs (dict): additional kwargs to pass to the Qdrant client

        - `api_key: string`

        - `collection_name: string`

        - `url: string`

        - `class_name: optional string`

        - `client_kwargs: optional map[unknown]`

        - `max_retries: optional number`

        - `supports_nested_metadata_filters: optional true`

          - `true`

      - `CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: string`

        - `search_service_endpoint: string`

        - `class_name: optional string`

        - `client_id: optional string`

        - `client_secret: optional string`

        - `embedding_dimension: optional number`

        - `filterable_metadata_field_keys: optional map[unknown]`

        - `index_name: optional string`

        - `search_service_api_version: optional string`

        - `supports_nested_metadata_filters: optional true`

          - `true`

        - `tenant_id: optional string`

      - `CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }`

        Cloud MongoDB Atlas Vector Store.

        This class is used to store the configuration for a MongoDB Atlas vector store,
        so that it can be created and used in LlamaCloud.

        Args:
        mongodb_uri (str): URI for connecting to MongoDB Atlas
        db_name (str): name of the MongoDB database
        collection_name (str): name of the MongoDB collection
        vector_index_name (str): name of the MongoDB Atlas vector index
        fulltext_index_name (str): name of the MongoDB Atlas full-text index

        - `collection_name: string`

        - `db_name: string`

        - `mongodb_uri: string`

        - `class_name: optional string`

        - `embedding_dimension: optional number`

        - `fulltext_index_name: optional string`

        - `supports_nested_metadata_filters: optional boolean`

        - `vector_index_name: optional string`

      - `CloudMilvusVectorStore = object { uri, token, class_name, 3 more }`

        Cloud Milvus Vector Store.

        - `uri: string`

        - `token: optional string`

        - `class_name: optional string`

        - `collection_name: optional string`

        - `embedding_dimension: optional number`

        - `supports_nested_metadata_filters: optional boolean`

      - `CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }`

        Cloud AstraDB Vector Store.

        This class is used to store the configuration for an AstraDB vector store, so that it can be
        created and used in LlamaCloud.

        Args:
        token (str): The Astra DB Application Token to use.
        api_endpoint (str): The Astra DB JSON API endpoint for your database.
        collection_name (str): Collection name to use. If not existing, it will be created.
        embedding_dimension (int): Length of the embedding vectors in use.
        keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

        - `token: string`

          The Astra DB Application Token to use

        - `api_endpoint: string`

          The Astra DB JSON API endpoint for your database

        - `collection_name: string`

          Collection name to use. If not existing, it will be created

        - `embedding_dimension: number`

          Length of the embedding vectors in use

        - `class_name: optional string`

        - `keyspace: optional string`

          The keyspace to use. If not provided, 'default_keyspace'

        - `supports_nested_metadata_filters: optional true`

          - `true`

    - `name: string`

      The name of the data sink.

    - `project_id: string`

    - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config: optional object { id, embedding_config, name, 3 more }`

    Schema for an embedding model config.

    - `id: string`

      Unique identifier

    - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more`

      The embedding configuration for the embedding model config.

      - `AzureOpenAIEmbeddingConfig = object { component, type }`

      - `CohereEmbeddingConfig = object { component, type }`

      - `GeminiEmbeddingConfig = object { component, type }`

      - `HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }`

      - `OpenAIEmbeddingConfig = object { component, type }`

      - `VertexAIEmbeddingConfig = object { component, type }`

      - `BedrockEmbeddingConfig = object { component, type }`

    - `name: string`

      The name of the embedding model config.

    - `project_id: string`

    - `created_at: optional string`

      Creation datetime

    - `updated_at: optional string`

      Update datetime

  - `embedding_model_config_id: optional string`

    The ID of the EmbeddingModelConfig this pipeline is using.

  - `llama_parse_parameters: optional LlamaParseParameters`

    Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

    - `adaptive_long_table: optional boolean`

    - `aggressive_table_extraction: optional boolean`

    - `annotate_links: optional boolean`

    - `auto_mode: optional boolean`

    - `auto_mode_configuration_json: optional string`

    - `auto_mode_trigger_on_image_in_page: optional boolean`

    - `auto_mode_trigger_on_regexp_in_page: optional string`

    - `auto_mode_trigger_on_table_in_page: optional boolean`

    - `auto_mode_trigger_on_text_in_page: optional string`

    - `azure_openai_api_version: optional string`

    - `azure_openai_deployment_name: optional string`

    - `azure_openai_endpoint: optional string`

    - `azure_openai_key: optional string`

    - `bbox_bottom: optional number`

    - `bbox_left: optional number`

    - `bbox_right: optional number`

    - `bbox_top: optional number`

    - `bounding_box: optional string`

    - `compact_markdown_table: optional boolean`

    - `complemental_formatting_instruction: optional string`

    - `content_guideline_instruction: optional string`

    - `continuous_mode: optional boolean`

    - `disable_image_extraction: optional boolean`

    - `disable_ocr: optional boolean`

    - `disable_reconstruction: optional boolean`

    - `do_not_cache: optional boolean`

    - `do_not_unroll_columns: optional boolean`

    - `enable_cost_optimizer: optional boolean`

    - `extract_charts: optional boolean`

    - `extract_layout: optional boolean`

    - `extract_printed_page_number: optional boolean`

    - `fast_mode: optional boolean`

    - `formatting_instruction: optional string`

    - `gpt4o_api_key: optional string`

    - `gpt4o_mode: optional boolean`

    - `guess_xlsx_sheet_name: optional boolean`

    - `hide_footers: optional boolean`

    - `hide_headers: optional boolean`

    - `high_res_ocr: optional boolean`

    - `html_make_all_elements_visible: optional boolean`

    - `html_remove_fixed_elements: optional boolean`

    - `html_remove_navigation_elements: optional boolean`

    - `http_proxy: optional string`

    - `ignore_document_elements_for_layout_detection: optional boolean`

    - `images_to_save: optional array of "screenshot" or "embedded" or "layout"`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: optional boolean`

    - `input_s3_path: optional string`

    - `input_s3_region: optional string`

    - `input_url: optional string`

    - `internal_is_screenshot_job: optional boolean`

    - `invalidate_cache: optional boolean`

    - `is_formatting_instruction: optional boolean`

    - `job_timeout_extra_time_per_page_in_seconds: optional number`

    - `job_timeout_in_seconds: optional number`

    - `keep_page_separator_when_merging_tables: optional boolean`

    - `languages: optional array of ParsingLanguages`

      - `"af"`

      - `"az"`

      - `"bs"`

      - `"cs"`

      - `"cy"`

      - `"da"`

      - `"de"`

      - `"en"`

      - `"es"`

      - `"et"`

      - `"fr"`

      - `"ga"`

      - `"hr"`

      - `"hu"`

      - `"id"`

      - `"is"`

      - `"it"`

      - `"ku"`

      - `"la"`

      - `"lt"`

      - `"lv"`

      - `"mi"`

      - `"ms"`

      - `"mt"`

      - `"nl"`

      - `"no"`

      - `"oc"`

      - `"pi"`

      - `"pl"`

      - `"pt"`

      - `"ro"`

      - `"rs_latin"`

      - `"sk"`

      - `"sl"`

      - `"sq"`

      - `"sv"`

      - `"sw"`

      - `"tl"`

      - `"tr"`

      - `"uz"`

      - `"vi"`

      - `"ar"`

      - `"fa"`

      - `"ug"`

      - `"ur"`

      - `"bn"`

      - `"as"`

      - `"mni"`

      - `"ru"`

      - `"rs_cyrillic"`

      - `"be"`

      - `"bg"`

      - `"uk"`

      - `"mn"`

      - `"abq"`

      - `"ady"`

      - `"kbd"`

      - `"ava"`

      - `"dar"`

      - `"inh"`

      - `"che"`

      - `"lbe"`

      - `"lez"`

      - `"tab"`

      - `"tjk"`

      - `"hi"`

      - `"mr"`

      - `"ne"`

      - `"bh"`

      - `"mai"`

      - `"ang"`

      - `"bho"`

      - `"mah"`

      - `"sck"`

      - `"new"`

      - `"gom"`

      - `"sa"`

      - `"bgc"`

      - `"th"`

      - `"ch_sim"`

      - `"ch_tra"`

      - `"ja"`

      - `"ko"`

      - `"ta"`

      - `"te"`

      - `"kn"`

    - `layout_aware: optional boolean`

    - `line_level_bounding_box: optional boolean`

    - `markdown_table_multiline_header_separator: optional string`

    - `max_pages: optional number`

    - `max_pages_enforced: optional number`

    - `merge_tables_across_pages_in_markdown: optional boolean`

    - `model: optional string`

    - `outlined_table_extraction: optional boolean`

    - `output_pdf_of_document: optional boolean`

    - `output_s3_path_prefix: optional string`

    - `output_s3_region: optional string`

    - `output_tables_as_HTML: optional boolean`

    - `page_error_tolerance: optional number`

    - `page_footer_prefix: optional string`

    - `page_footer_suffix: optional string`

    - `page_header_prefix: optional string`

    - `page_header_suffix: optional string`

    - `page_prefix: optional string`

    - `page_separator: optional string`

    - `page_suffix: optional string`

    - `parse_mode: optional ParsingMode`

      Enum for representing the mode of parsing to be used.

      - `"parse_page_without_llm"`

      - `"parse_page_with_llm"`

      - `"parse_page_with_lvm"`

      - `"parse_page_with_agent"`

      - `"parse_page_with_layout_agent"`

      - `"parse_document_with_llm"`

      - `"parse_document_with_lvm"`

      - `"parse_document_with_agent"`

    - `parsing_instruction: optional string`

    - `precise_bounding_box: optional boolean`

    - `premium_mode: optional boolean`

    - `presentation_out_of_bounds_content: optional boolean`

    - `presentation_skip_embedded_data: optional boolean`

    - `preserve_layout_alignment_across_pages: optional boolean`

    - `preserve_very_small_text: optional boolean`

    - `preset: optional string`

    - `priority: optional "low" or "medium" or "high" or "critical"`

      The priority for the request. This field may be ignored or overwritten depending on the organization tier.

      - `"low"`

      - `"medium"`

      - `"high"`

      - `"critical"`

    - `project_id: optional string`

    - `remove_hidden_text: optional boolean`

    - `replace_failed_page_mode: optional FailPageMode`

      Enum for representing the different available page error handling modes.

      - `"raw_text"`

      - `"blank_page"`

      - `"error_message"`

    - `replace_failed_page_with_error_message_prefix: optional string`

    - `replace_failed_page_with_error_message_suffix: optional string`

    - `save_images: optional boolean`

    - `skip_diagonal_text: optional boolean`

    - `specialized_chart_parsing_agentic: optional boolean`

    - `specialized_chart_parsing_efficient: optional boolean`

    - `specialized_chart_parsing_plus: optional boolean`

    - `specialized_image_parsing: optional boolean`

    - `spreadsheet_extract_sub_tables: optional boolean`

    - `spreadsheet_force_formula_computation: optional boolean`

    - `spreadsheet_include_hidden_sheets: optional boolean`

    - `strict_mode_buggy_font: optional boolean`

    - `strict_mode_image_extraction: optional boolean`

    - `strict_mode_image_ocr: optional boolean`

    - `strict_mode_reconstruction: optional boolean`

    - `structured_output: optional boolean`

    - `structured_output_json_schema: optional string`

    - `structured_output_json_schema_name: optional string`

    - `system_prompt: optional string`

    - `system_prompt_append: optional string`

    - `take_screenshot: optional boolean`

    - `target_pages: optional string`

    - `tier: optional string`

    - `use_vendor_multimodal_model: optional boolean`

    - `user_prompt: optional string`

    - `vendor_multimodal_api_key: optional string`

    - `vendor_multimodal_model_name: optional string`

    - `version: optional string`

    - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more`

        Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

        - `"extract.pending"`

        - `"extract.success"`

        - `"extract.error"`

        - `"extract.partial_success"`

        - `"extract.cancelled"`

        - `"parse.pending"`

        - `"parse.running"`

        - `"parse.success"`

        - `"parse.error"`

        - `"parse.partial_success"`

        - `"parse.cancelled"`

        - `"classify.pending"`

        - `"classify.success"`

        - `"classify.error"`

        - `"classify.partial_success"`

        - `"classify.cancelled"`

        - `"unmapped_event"`

      - `webhook_headers: optional map[string]`

        Custom HTTP headers sent with each webhook request (e.g. auth tokens)

      - `webhook_output_format: optional string`

        Response format sent to the webhook: 'string' (default) or 'json'

      - `webhook_url: optional string`

        URL to receive webhook POST notifications

    - `webhook_url: optional string`

  - `managed_pipeline_id: optional string`

    The ID of the ManagedPipeline this playground pipeline is linked to.

  - `metadata_config: optional PipelineMetadataConfig`

    Metadata configuration for the pipeline.

    - `excluded_embed_metadata_keys: optional array of string`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: optional array of string`

      List of metadata keys to exclude from LLM during retrieval

  - `pipeline_type: optional PipelineType`

    Type of pipeline. Either PLAYGROUND or MANAGED.

    - `"PLAYGROUND"`

    - `"MANAGED"`

  - `preset_retrieval_parameters: optional PresetRetrievalParams`

    Preset retrieval parameters for the pipeline.

    - `alpha: optional number`

      Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

    - `class_name: optional string`

    - `dense_similarity_cutoff: optional number`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: optional number`

      Number of nodes for dense retrieval.

    - `enable_reranking: optional boolean`

      Enable reranking for retrieval

    - `files_top_k: optional number`

      Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

    - `rerank_top_n: optional number`

      Number of reranked nodes for returning.

    - `retrieval_mode: optional RetrievalMode`

      The retrieval mode for the query.

      - `"chunks"`

      - `"files_via_metadata"`

      - `"files_via_content"`

      - `"auto_routed"`

    - `retrieve_image_nodes: optional boolean`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: optional boolean`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: optional boolean`

      Whether to retrieve page screenshot nodes.

    - `search_filters: optional MetadataFilters`

      Metadata filters for vector stores.

      - `filters: array of object { key, value, operator }  or MetadataFilters`

        - `MetadataFilter = object { key, value, operator }`

          Comprehensive metadata filter for vector stores to support more operators.

          Value uses Strict types, as int, float and str are compatible types and were all
          converted to string before.

          See: https://docs.pydantic.dev/latest/usage/types/#strict-types

          - `key: string`

          - `value: number or string or array of string or 2 more`

            - `number`

            - `string`

            - `array of string`

            - `array of number`

            - `array of number`

          - `operator: optional "==" or ">" or "<" or 11 more`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `MetadataFilters = object { filters, condition }`

          Metadata filters for vector stores.

      - `condition: optional "and" or "or" or "not"`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]`

      JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

      - `map[unknown]`

      - `array of unknown`

      - `string`

      - `number`

      - `boolean`

    - `sparse_similarity_top_k: optional number`

      Number of nodes for sparse retrieval.

  - `sparse_model_config: optional SparseModelConfig`

    Configuration for sparse embedding models used in hybrid search.

    This allows users to choose between Splade and BM25 models for
    sparse retrieval in managed data sinks.

    - `class_name: optional string`

    - `model_type: optional "splade" or "bm25" or "auto"`

      The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

      - `"splade"`

      - `"bm25"`

      - `"auto"`

  - `status: optional "CREATED" or "DELETING"`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig`

    Configuration for the transformation.

    - `AutoTransformConfig = object { chunk_overlap, chunk_size, mode }`

      - `chunk_overlap: optional number`

        Chunk overlap for the transformation.

      - `chunk_size: optional number`

        Chunk size for the transformation.

      - `mode: optional "auto"`

        - `"auto"`

    - `AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }`

      - `chunking_config: optional object { mode }  or object { chunk_overlap, chunk_size, mode }  or object { chunk_overlap, chunk_size, mode, separator }  or 2 more`

        Configuration for the chunking.

        - `NoneChunkingConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "character"`

            - `"character"`

        - `TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "token"`

            - `"token"`

          - `separator: optional string`

        - `SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }`

          - `chunk_overlap: optional number`

          - `chunk_size: optional number`

          - `mode: optional "sentence"`

            - `"sentence"`

          - `paragraph_separator: optional string`

          - `separator: optional string`

        - `SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }`

          - `breakpoint_percentile_threshold: optional number`

          - `buffer_size: optional number`

          - `mode: optional "semantic"`

            - `"semantic"`

      - `mode: optional "advanced"`

        - `"advanced"`

      - `segmentation_config: optional object { mode }  or object { mode, page_separator }  or object { mode }`

        Configuration for the segmentation.

        - `NoneSegmentationConfig = object { mode }`

          - `mode: optional "none"`

            - `"none"`

        - `PageSegmentationConfig = object { mode, page_separator }`

          - `mode: optional "page"`

            - `"page"`

          - `page_separator: optional string`

        - `ElementSegmentationConfig = object { mode }`

          - `mode: optional "element"`

            - `"element"`

  - `updated_at: optional string`

    Update datetime

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/data-sources/$DATA_SOURCE_ID/sync \
    -X POST \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Domain Types

### Pipeline Data Source

- `PipelineDataSource = object { id, component, data_source_id, 13 more }`

  Schema for a data source in a pipeline.

  - `id: string`

    Unique identifier

  - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

    Component that implements the data source

    - `map[unknown]`

    - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

      - `bucket: string`

        The name of the S3 bucket to read from.

      - `aws_access_id: optional string`

        The AWS access ID to use for authentication.

      - `aws_access_secret: optional string`

        The AWS access secret to use for authentication.

      - `class_name: optional string`

      - `prefix: optional string`

        The prefix of the S3 objects to read from.

      - `regex_pattern: optional string`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: optional string`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: optional boolean`

    - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

      - `account_url: string`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: string`

        The name of the Azure Storage Blob container to read from.

      - `account_key: optional string`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: optional string`

        The Azure Storage Blob account name to use for authentication.

      - `blob: optional string`

        The blob name to read from.

      - `class_name: optional string`

      - `client_id: optional string`

        The Azure AD client ID to use for authentication.

      - `client_secret: optional string`

        The Azure AD client secret to use for authentication.

      - `prefix: optional string`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: optional boolean`

      - `tenant_id: optional string`

        The Azure AD tenant ID to use for authentication.

    - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

      - `folder_id: string`

        The ID of the Google Drive folder to read from.

      - `class_name: optional string`

      - `service_account_key: optional map[string]`

        A dictionary containing secret values

      - `supports_access_control: optional boolean`

    - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `user_principal_name: string`

        The user principal name to use for authentication.

      - `class_name: optional string`

      - `folder_id: optional string`

        The ID of the OneDrive folder to read from.

      - `folder_path: optional string`

        The path of the OneDrive folder to read from.

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `class_name: optional string`

      - `drive_name: optional string`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: optional array of string`

        List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

      - `folder_id: optional string`

        The ID of the Sharepoint folder to read from.

      - `folder_path: optional string`

        The path of the Sharepoint folder to read from.

      - `get_permissions: optional boolean`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: optional array of string`

        List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `site_id: optional string`

        The ID of the SharePoint site to download from.

      - `site_name: optional string`

        The name of the SharePoint site to download from.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

      - `slack_token: string`

        Slack Bot Token.

      - `channel_ids: optional string`

        Slack Channel.

      - `channel_patterns: optional string`

        Slack Channel name pattern.

      - `class_name: optional string`

      - `earliest_date: optional string`

        Earliest date.

      - `earliest_date_timestamp: optional number`

        Earliest date timestamp.

      - `latest_date: optional string`

        Latest date.

      - `latest_date_timestamp: optional number`

        Latest date timestamp.

      - `supports_access_control: optional boolean`

    - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

      - `integration_token: string`

        The integration token to use for authentication.

      - `class_name: optional string`

      - `database_ids: optional string`

        The Notion Database Id to read content from.

      - `page_ids: optional string`

        The Page ID's of the Notion to read from.

      - `supports_access_control: optional boolean`

    - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: string`

        The server URL of the Confluence instance.

      - `api_token: optional string`

        The API token to use for authentication.

      - `class_name: optional string`

      - `cql: optional string`

        The CQL query to use for fetching pages.

      - `failure_handling: optional FailureHandlingConfig`

        Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

        Example:
        {
        "skip_list_failures": true
        }

        Currently supports:

        - skip_list_failures: Skip failed batches/lists and continue processing

        - `skip_list_failures: optional boolean`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: optional boolean`

        Whether to index restricted pages.

      - `keep_markdown_format: optional boolean`

        Whether to keep the markdown format.

      - `label: optional string`

        The label to use for fetching pages.

      - `page_ids: optional string`

        The page IDs of the Confluence to read from.

      - `space_key: optional string`

        The space key to read from.

      - `supports_access_control: optional boolean`

      - `user_name: optional string`

        The username to use for authentication.

    - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `api_token: optional string`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `server_url: optional string`

        The server url for Jira Cloud.

      - `supports_access_control: optional boolean`

    - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `server_url: string`

        The server url for Jira Cloud.

      - `api_token: optional string`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: optional "2" or "3"`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `expand: optional string`

        Fields to expand in the response.

      - `fields: optional array of string`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: optional boolean`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: optional number`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: optional boolean`

    - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

      - `authentication_mechanism: "developer_token" or "ccg"`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: optional string`

      - `client_id: optional string`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: optional string`

        Box API secret used for making auth requests.

      - `developer_token: optional string`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: optional string`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: optional string`

        The ID of the Box folder to read from.

      - `supports_access_control: optional boolean`

      - `user_id: optional string`

        Box User ID, if provided authenticates as user.

  - `data_source_id: string`

    The ID of the data source.

  - `last_synced_at: string`

    The last time the data source was automatically synced.

  - `name: string`

    The name of the data source.

  - `pipeline_id: string`

    The ID of the pipeline.

  - `project_id: string`

  - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: optional string`

    Creation datetime

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata that will be present on all data loaded from the data source

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `sync_interval: optional number`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: optional string`

    The id of the user who set the sync schedule.

  - `updated_at: optional string`

    Update datetime

  - `version_metadata: optional DataSourceReaderVersionMetadata`

    Version metadata for the data source

    - `reader_version: optional "1.0" or "2.0" or "2.1"`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Data Source Get Data Sources Response

- `DataSourceGetDataSourcesResponse = array of PipelineDataSource`

  - `id: string`

    Unique identifier

  - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

    Component that implements the data source

    - `map[unknown]`

    - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

      - `bucket: string`

        The name of the S3 bucket to read from.

      - `aws_access_id: optional string`

        The AWS access ID to use for authentication.

      - `aws_access_secret: optional string`

        The AWS access secret to use for authentication.

      - `class_name: optional string`

      - `prefix: optional string`

        The prefix of the S3 objects to read from.

      - `regex_pattern: optional string`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: optional string`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: optional boolean`

    - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

      - `account_url: string`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: string`

        The name of the Azure Storage Blob container to read from.

      - `account_key: optional string`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: optional string`

        The Azure Storage Blob account name to use for authentication.

      - `blob: optional string`

        The blob name to read from.

      - `class_name: optional string`

      - `client_id: optional string`

        The Azure AD client ID to use for authentication.

      - `client_secret: optional string`

        The Azure AD client secret to use for authentication.

      - `prefix: optional string`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: optional boolean`

      - `tenant_id: optional string`

        The Azure AD tenant ID to use for authentication.

    - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

      - `folder_id: string`

        The ID of the Google Drive folder to read from.

      - `class_name: optional string`

      - `service_account_key: optional map[string]`

        A dictionary containing secret values

      - `supports_access_control: optional boolean`

    - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `user_principal_name: string`

        The user principal name to use for authentication.

      - `class_name: optional string`

      - `folder_id: optional string`

        The ID of the OneDrive folder to read from.

      - `folder_path: optional string`

        The path of the OneDrive folder to read from.

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `class_name: optional string`

      - `drive_name: optional string`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: optional array of string`

        List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

      - `folder_id: optional string`

        The ID of the Sharepoint folder to read from.

      - `folder_path: optional string`

        The path of the Sharepoint folder to read from.

      - `get_permissions: optional boolean`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: optional array of string`

        List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `site_id: optional string`

        The ID of the SharePoint site to download from.

      - `site_name: optional string`

        The name of the SharePoint site to download from.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

      - `slack_token: string`

        Slack Bot Token.

      - `channel_ids: optional string`

        Slack Channel.

      - `channel_patterns: optional string`

        Slack Channel name pattern.

      - `class_name: optional string`

      - `earliest_date: optional string`

        Earliest date.

      - `earliest_date_timestamp: optional number`

        Earliest date timestamp.

      - `latest_date: optional string`

        Latest date.

      - `latest_date_timestamp: optional number`

        Latest date timestamp.

      - `supports_access_control: optional boolean`

    - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

      - `integration_token: string`

        The integration token to use for authentication.

      - `class_name: optional string`

      - `database_ids: optional string`

        The Notion Database Id to read content from.

      - `page_ids: optional string`

        The Page ID's of the Notion to read from.

      - `supports_access_control: optional boolean`

    - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: string`

        The server URL of the Confluence instance.

      - `api_token: optional string`

        The API token to use for authentication.

      - `class_name: optional string`

      - `cql: optional string`

        The CQL query to use for fetching pages.

      - `failure_handling: optional FailureHandlingConfig`

        Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

        Example:
        {
        "skip_list_failures": true
        }

        Currently supports:

        - skip_list_failures: Skip failed batches/lists and continue processing

        - `skip_list_failures: optional boolean`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: optional boolean`

        Whether to index restricted pages.

      - `keep_markdown_format: optional boolean`

        Whether to keep the markdown format.

      - `label: optional string`

        The label to use for fetching pages.

      - `page_ids: optional string`

        The page IDs of the Confluence to read from.

      - `space_key: optional string`

        The space key to read from.

      - `supports_access_control: optional boolean`

      - `user_name: optional string`

        The username to use for authentication.

    - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `api_token: optional string`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `server_url: optional string`

        The server url for Jira Cloud.

      - `supports_access_control: optional boolean`

    - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `server_url: string`

        The server url for Jira Cloud.

      - `api_token: optional string`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: optional "2" or "3"`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `expand: optional string`

        Fields to expand in the response.

      - `fields: optional array of string`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: optional boolean`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: optional number`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: optional boolean`

    - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

      - `authentication_mechanism: "developer_token" or "ccg"`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: optional string`

      - `client_id: optional string`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: optional string`

        Box API secret used for making auth requests.

      - `developer_token: optional string`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: optional string`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: optional string`

        The ID of the Box folder to read from.

      - `supports_access_control: optional boolean`

      - `user_id: optional string`

        Box User ID, if provided authenticates as user.

  - `data_source_id: string`

    The ID of the data source.

  - `last_synced_at: string`

    The last time the data source was automatically synced.

  - `name: string`

    The name of the data source.

  - `pipeline_id: string`

    The ID of the pipeline.

  - `project_id: string`

  - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: optional string`

    Creation datetime

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata that will be present on all data loaded from the data source

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `sync_interval: optional number`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: optional string`

    The id of the user who set the sync schedule.

  - `updated_at: optional string`

    Update datetime

  - `version_metadata: optional DataSourceReaderVersionMetadata`

    Version metadata for the data source

    - `reader_version: optional "1.0" or "2.0" or "2.1"`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Data Source Update Data Sources Response

- `DataSourceUpdateDataSourcesResponse = array of PipelineDataSource`

  - `id: string`

    Unique identifier

  - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more`

    Component that implements the data source

    - `map[unknown]`

    - `CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }`

      - `bucket: string`

        The name of the S3 bucket to read from.

      - `aws_access_id: optional string`

        The AWS access ID to use for authentication.

      - `aws_access_secret: optional string`

        The AWS access secret to use for authentication.

      - `class_name: optional string`

      - `prefix: optional string`

        The prefix of the S3 objects to read from.

      - `regex_pattern: optional string`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: optional string`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: optional boolean`

    - `CloudAzStorageBlobDataSource = object { account_url, container_name, account_key, 8 more }`

      - `account_url: string`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: string`

        The name of the Azure Storage Blob container to read from.

      - `account_key: optional string`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: optional string`

        The Azure Storage Blob account name to use for authentication.

      - `blob: optional string`

        The blob name to read from.

      - `class_name: optional string`

      - `client_id: optional string`

        The Azure AD client ID to use for authentication.

      - `client_secret: optional string`

        The Azure AD client secret to use for authentication.

      - `prefix: optional string`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: optional boolean`

      - `tenant_id: optional string`

        The Azure AD tenant ID to use for authentication.

    - `CloudGoogleDriveDataSource = object { folder_id, class_name, service_account_key, supports_access_control }`

      - `folder_id: string`

        The ID of the Google Drive folder to read from.

      - `class_name: optional string`

      - `service_account_key: optional map[string]`

        A dictionary containing secret values

      - `supports_access_control: optional boolean`

    - `CloudOneDriveDataSource = object { client_id, client_secret, tenant_id, 6 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `user_principal_name: string`

        The user principal name to use for authentication.

      - `class_name: optional string`

      - `folder_id: optional string`

        The ID of the OneDrive folder to read from.

      - `folder_path: optional string`

        The path of the OneDrive folder to read from.

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSharepointDataSource = object { client_id, client_secret, tenant_id, 11 more }`

      - `client_id: string`

        The client ID to use for authentication.

      - `client_secret: string`

        The client secret to use for authentication.

      - `tenant_id: string`

        The tenant ID to use for authentication.

      - `class_name: optional string`

      - `drive_name: optional string`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: optional array of string`

        List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~']

      - `folder_id: optional string`

        The ID of the Sharepoint folder to read from.

      - `folder_path: optional string`

        The path of the Sharepoint folder to read from.

      - `get_permissions: optional boolean`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: optional array of string`

        List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$']

      - `required_exts: optional array of string`

        The list of required file extensions.

      - `site_id: optional string`

        The ID of the SharePoint site to download from.

      - `site_name: optional string`

        The name of the SharePoint site to download from.

      - `supports_access_control: optional true`

        - `true`

    - `CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }`

      - `slack_token: string`

        Slack Bot Token.

      - `channel_ids: optional string`

        Slack Channel.

      - `channel_patterns: optional string`

        Slack Channel name pattern.

      - `class_name: optional string`

      - `earliest_date: optional string`

        Earliest date.

      - `earliest_date_timestamp: optional number`

        Earliest date timestamp.

      - `latest_date: optional string`

        Latest date.

      - `latest_date_timestamp: optional number`

        Latest date timestamp.

      - `supports_access_control: optional boolean`

    - `CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }`

      - `integration_token: string`

        The integration token to use for authentication.

      - `class_name: optional string`

      - `database_ids: optional string`

        The Notion Database Id to read content from.

      - `page_ids: optional string`

        The Page ID's of the Notion to read from.

      - `supports_access_control: optional boolean`

    - `CloudConfluenceDataSource = object { authentication_mechanism, server_url, api_token, 10 more }`

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: string`

        The server URL of the Confluence instance.

      - `api_token: optional string`

        The API token to use for authentication.

      - `class_name: optional string`

      - `cql: optional string`

        The CQL query to use for fetching pages.

      - `failure_handling: optional FailureHandlingConfig`

        Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

        Example:
        {
        "skip_list_failures": true
        }

        Currently supports:

        - skip_list_failures: Skip failed batches/lists and continue processing

        - `skip_list_failures: optional boolean`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: optional boolean`

        Whether to index restricted pages.

      - `keep_markdown_format: optional boolean`

        Whether to keep the markdown format.

      - `label: optional string`

        The label to use for fetching pages.

      - `page_ids: optional string`

        The page IDs of the Confluence to read from.

      - `space_key: optional string`

        The space key to read from.

      - `supports_access_control: optional boolean`

      - `user_name: optional string`

        The username to use for authentication.

    - `CloudJiraDataSource = object { authentication_mechanism, query, api_token, 5 more }`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `api_token: optional string`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `server_url: optional string`

        The server url for Jira Cloud.

      - `supports_access_control: optional boolean`

    - `CloudJiraDataSourceV2 = object { authentication_mechanism, query, server_url, 10 more }`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: string`

        Type of Authentication for connecting to Jira APIs.

      - `query: string`

        JQL (Jira Query Language) query to search.

      - `server_url: string`

        The server url for Jira Cloud.

      - `api_token: optional string`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: optional "2" or "3"`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: optional string`

      - `cloud_id: optional string`

        The cloud ID, used in case of OAuth2.

      - `email: optional string`

        The email address to use for authentication.

      - `expand: optional string`

        Fields to expand in the response.

      - `fields: optional array of string`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: optional boolean`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: optional number`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: optional boolean`

    - `CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }`

      - `authentication_mechanism: "developer_token" or "ccg"`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: optional string`

      - `client_id: optional string`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: optional string`

        Box API secret used for making auth requests.

      - `developer_token: optional string`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: optional string`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: optional string`

        The ID of the Box folder to read from.

      - `supports_access_control: optional boolean`

      - `user_id: optional string`

        Box User ID, if provided authenticates as user.

  - `data_source_id: string`

    The ID of the data source.

  - `last_synced_at: string`

    The last time the data source was automatically synced.

  - `name: string`

    The name of the data source.

  - `pipeline_id: string`

    The ID of the pipeline.

  - `project_id: string`

  - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: optional string`

    Creation datetime

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata that will be present on all data loaded from the data source

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `sync_interval: optional number`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: optional string`

    The id of the user who set the sync schedule.

  - `updated_at: optional string`

    Update datetime

  - `version_metadata: optional DataSourceReaderVersionMetadata`

    Version metadata for the data source

    - `reader_version: optional "1.0" or "2.0" or "2.1"`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

# Images

## List File Page Screenshots

**get** `/api/v1/files/{id}/page_screenshots`

List metadata for all screenshots of pages from a file.

### Path Parameters

- `id: string`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Returns

- `file_id: string`

  The ID of the file that the page screenshot was taken from

- `image_size: number`

  The size of the image in bytes

- `page_index: number`

  The index of the page for which the screenshot is taken (0-indexed)

- `metadata: optional map[unknown]`

  Metadata for the screenshot

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/files/$ID/page_screenshots \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
[
  {
    "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "image_size": 0,
    "page_index": 0,
    "metadata": {
      "foo": "bar"
    }
  }
]
```

## Get File Page Screenshot

**get** `/api/v1/files/{id}/page_screenshots/{page_index}`

Get screenshot of a page from a file.

### Path Parameters

- `id: string`

- `page_index: number`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/files/$ID/page_screenshots/$PAGE_INDEX \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{}
```

## Get File Page Figure

**get** `/api/v1/files/{id}/page-figures/{page_index}/{figure_name}`

Get a specific figure from a page of a file.

### Path Parameters

- `id: string`

- `page_index: number`

- `figure_name: string`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/files/$ID/page-figures/$PAGE_INDEX/$FIGURE_NAME \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{}
```

## List File Pages Figures

**get** `/api/v1/files/{id}/page-figures`

List metadata for all figures from all pages of a file.

### Path Parameters

- `id: string`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Returns

- `confidence: number`

  The confidence of the figure

- `figure_name: string`

  The name of the figure

- `figure_size: number`

  The size of the figure in bytes

- `file_id: string`

  The ID of the file that the figure was taken from

- `page_index: number`

  The index of the page for which the figure is taken (0-indexed)

- `is_likely_noise: optional boolean`

  Whether the figure is likely to be noise

- `metadata: optional map[unknown]`

  Metadata for the figure

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/files/$ID/page-figures \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
[
  {
    "confidence": 0,
    "figure_name": "figure_name",
    "figure_size": 0,
    "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "page_index": 0,
    "is_likely_noise": true,
    "metadata": {
      "foo": "bar"
    }
  }
]
```

## Domain Types

### Image List Page Screenshots Response

- `ImageListPageScreenshotsResponse = array of object { file_id, image_size, page_index, metadata }`

  - `file_id: string`

    The ID of the file that the page screenshot was taken from

  - `image_size: number`

    The size of the image in bytes

  - `page_index: number`

    The index of the page for which the screenshot is taken (0-indexed)

  - `metadata: optional map[unknown]`

    Metadata for the screenshot

### Image Get Page Screenshot Response

- `ImageGetPageScreenshotResponse = unknown`

### Image Get Page Figure Response

- `ImageGetPageFigureResponse = unknown`

### Image List Page Figures Response

- `ImageListPageFiguresResponse = array of object { confidence, figure_name, figure_size, 4 more }`

  - `confidence: number`

    The confidence of the figure

  - `figure_name: string`

    The name of the figure

  - `figure_size: number`

    The size of the figure in bytes

  - `file_id: string`

    The ID of the file that the figure was taken from

  - `page_index: number`

    The index of the page for which the figure is taken (0-indexed)

  - `is_likely_noise: optional boolean`

    Whether the figure is likely to be noise

  - `metadata: optional map[unknown]`

    Metadata for the figure

# Files

## Get Pipeline File Status Counts

**get** `/api/v1/pipelines/{pipeline_id}/files/status-counts`

Get files for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `data_source_id: optional string`

- `only_manually_uploaded: optional boolean`

### Cookie Parameters

- `session: optional string`

### Returns

- `counts: map[number]`

  The counts of files by status

- `total_count: number`

  The total number of files

- `data_source_id: optional string`

  The ID of the data source that the files belong to

- `only_manually_uploaded: optional boolean`

  Whether to only count manually uploaded files

- `pipeline_id: optional string`

  The ID of the pipeline that the files belong to

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files/status-counts \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "counts": {
    "foo": 0
  },
  "total_count": 0,
  "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "only_manually_uploaded": true,
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
}
```

## Get Pipeline File Status

**get** `/api/v1/pipelines/{pipeline_id}/files/{file_id}/status`

Get status of a file for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `file_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `ManagedIngestionStatusResponse = object { status, deployment_date, effective_at, 2 more }`

  - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: optional string`

    Date of the deployment.

  - `effective_at: optional string`

    When the status is effective

  - `error: optional array of object { job_id, message, step }`

    List of errors that occurred during ingestion.

    - `job_id: string`

      ID of the job that failed.

    - `message: string`

      List of errors that occurred during ingestion.

    - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: optional string`

    ID of the latest job.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files/$FILE_ID/status \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "status": "NOT_STARTED",
  "deployment_date": "2019-12-27T18:11:19.117Z",
  "effective_at": "2019-12-27T18:11:19.117Z",
  "error": [
    {
      "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "message": "message",
      "step": "MANAGED_INGESTION"
    }
  ],
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
}
```

## Add Files To Pipeline Api

**put** `/api/v1/pipelines/{pipeline_id}/files`

Add files to a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `body: array of object { file_id, custom_metadata }`

  - `file_id: string`

    The ID of the file

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata for the file

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

### Returns

- `id: string`

  Unique identifier for the pipeline file.

- `pipeline_id: string`

  The ID of the pipeline that the file is associated with.

- `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]`

  Hashes for the configuration of the pipeline.

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `created_at: optional string`

  When the pipeline file was created.

- `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

  Custom metadata for the file.

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `data_source_id: optional string`

  The ID of the data source that the file belongs to.

- `external_file_id: optional string`

  The ID of the file in the external system.

- `file_id: optional string`

  The ID of the file.

- `file_size: optional number`

  Size of the file in bytes.

- `file_type: optional string`

  File type (e.g. pdf, docx, etc.).

- `indexed_page_count: optional number`

  The number of pages that have been indexed for this file.

- `last_modified_at: optional string`

  The last modified time of the file.

- `name: optional string`

  Name of the file.

- `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]`

  Permission information for the file.

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `project_id: optional string`

  The ID of the project that the file belongs to.

- `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]`

  Resource information for the file.

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

- `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

  Status of the pipeline file.

  - `"NOT_STARTED"`

  - `"IN_PROGRESS"`

  - `"SUCCESS"`

  - `"ERROR"`

  - `"CANCELLED"`

- `status_updated_at: optional string`

  The last time the status was updated.

- `updated_at: optional string`

  When the pipeline file was last updated.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '[
          {
            "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
            "custom_metadata": {
              "foo": {
                "foo": "bar"
              }
            }
          }
        ]'
```

#### Response

```json
[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "config_hash": {
      "foo": {
        "foo": "bar"
      }
    },
    "created_at": "2019-12-27T18:11:19.117Z",
    "custom_metadata": {
      "foo": {
        "foo": "bar"
      }
    },
    "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "external_file_id": "external_file_id",
    "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "file_size": 0,
    "file_type": "file_type",
    "indexed_page_count": 0,
    "last_modified_at": "2019-12-27T18:11:19.117Z",
    "name": "name",
    "permission_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "resource_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "status": "NOT_STARTED",
    "status_updated_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  }
]
```

## Update Pipeline File

**put** `/api/v1/pipelines/{pipeline_id}/files/{file_id}`

Update a file for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `file_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

  Custom metadata for the file

  - `map[unknown]`

  - `array of unknown`

  - `string`

  - `number`

  - `boolean`

### Returns

- `PipelineFile = object { id, pipeline_id, config_hash, 16 more }`

  A file associated with a pipeline.

  - `id: string`

    Unique identifier for the pipeline file.

  - `pipeline_id: string`

    The ID of the pipeline that the file is associated with.

  - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]`

    Hashes for the configuration of the pipeline.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `created_at: optional string`

    When the pipeline file was created.

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `data_source_id: optional string`

    The ID of the data source that the file belongs to.

  - `external_file_id: optional string`

    The ID of the file in the external system.

  - `file_id: optional string`

    The ID of the file.

  - `file_size: optional number`

    Size of the file in bytes.

  - `file_type: optional string`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: optional number`

    The number of pages that have been indexed for this file.

  - `last_modified_at: optional string`

    The last modified time of the file.

  - `name: optional string`

    Name of the file.

  - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Permission information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `project_id: optional string`

    The ID of the project that the file belongs to.

  - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Resource information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `updated_at: optional string`

    When the pipeline file was last updated.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files/$FILE_ID \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{}'
```

#### Response

```json
{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "foo": {
      "foo": "bar"
    }
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "custom_metadata": {
    "foo": {
      "foo": "bar"
    }
  },
  "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "external_file_id": "external_file_id",
  "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "file_size": 0,
  "file_type": "file_type",
  "indexed_page_count": 0,
  "last_modified_at": "2019-12-27T18:11:19.117Z",
  "name": "name",
  "permission_info": {
    "foo": {
      "foo": "bar"
    }
  },
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "resource_info": {
    "foo": {
      "foo": "bar"
    }
  },
  "status": "NOT_STARTED",
  "status_updated_at": "2019-12-27T18:11:19.117Z",
  "updated_at": "2019-12-27T18:11:19.117Z"
}
```

## Delete Pipeline File

**delete** `/api/v1/pipelines/{pipeline_id}/files/{file_id}`

Delete a file from a pipeline.

### Path Parameters

- `pipeline_id: string`

- `file_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files/$FILE_ID \
    -X DELETE \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

## List Pipeline Files2

**get** `/api/v1/pipelines/{pipeline_id}/files2`

List files for a pipeline with optional filtering, sorting, and pagination.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `data_source_id: optional string`

- `file_name_contains: optional string`

- `limit: optional number`

- `offset: optional number`

- `only_manually_uploaded: optional boolean`

- `order_by: optional string`

- `statuses: optional array of "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

  Filter by file statuses

  - `"NOT_STARTED"`

  - `"IN_PROGRESS"`

  - `"SUCCESS"`

  - `"ERROR"`

  - `"CANCELLED"`

### Cookie Parameters

- `session: optional string`

### Returns

- `files: array of PipelineFile`

  The files to list

  - `id: string`

    Unique identifier for the pipeline file.

  - `pipeline_id: string`

    The ID of the pipeline that the file is associated with.

  - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]`

    Hashes for the configuration of the pipeline.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `created_at: optional string`

    When the pipeline file was created.

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `data_source_id: optional string`

    The ID of the data source that the file belongs to.

  - `external_file_id: optional string`

    The ID of the file in the external system.

  - `file_id: optional string`

    The ID of the file.

  - `file_size: optional number`

    Size of the file in bytes.

  - `file_type: optional string`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: optional number`

    The number of pages that have been indexed for this file.

  - `last_modified_at: optional string`

    The last modified time of the file.

  - `name: optional string`

    Name of the file.

  - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Permission information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `project_id: optional string`

    The ID of the project that the file belongs to.

  - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Resource information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `updated_at: optional string`

    When the pipeline file was last updated.

- `limit: number`

  The limit of the files

- `offset: number`

  The offset of the files

- `total_count: number`

  The total number of files

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/files2 \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "files": [
    {
      "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "config_hash": {
        "foo": {
          "foo": "bar"
        }
      },
      "created_at": "2019-12-27T18:11:19.117Z",
      "custom_metadata": {
        "foo": {
          "foo": "bar"
        }
      },
      "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "external_file_id": "external_file_id",
      "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "file_size": 0,
      "file_type": "file_type",
      "indexed_page_count": 0,
      "last_modified_at": "2019-12-27T18:11:19.117Z",
      "name": "name",
      "permission_info": {
        "foo": {
          "foo": "bar"
        }
      },
      "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "resource_info": {
        "foo": {
          "foo": "bar"
        }
      },
      "status": "NOT_STARTED",
      "status_updated_at": "2019-12-27T18:11:19.117Z",
      "updated_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "limit": 0,
  "offset": 0,
  "total_count": 0
}
```

## Domain Types

### Pipeline File

- `PipelineFile = object { id, pipeline_id, config_hash, 16 more }`

  A file associated with a pipeline.

  - `id: string`

    Unique identifier for the pipeline file.

  - `pipeline_id: string`

    The ID of the pipeline that the file is associated with.

  - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]`

    Hashes for the configuration of the pipeline.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `created_at: optional string`

    When the pipeline file was created.

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `data_source_id: optional string`

    The ID of the data source that the file belongs to.

  - `external_file_id: optional string`

    The ID of the file in the external system.

  - `file_id: optional string`

    The ID of the file.

  - `file_size: optional number`

    Size of the file in bytes.

  - `file_type: optional string`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: optional number`

    The number of pages that have been indexed for this file.

  - `last_modified_at: optional string`

    The last modified time of the file.

  - `name: optional string`

    Name of the file.

  - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Permission information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `project_id: optional string`

    The ID of the project that the file belongs to.

  - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Resource information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `updated_at: optional string`

    When the pipeline file was last updated.

### File Get Status Counts Response

- `FileGetStatusCountsResponse = object { counts, total_count, data_source_id, 2 more }`

  - `counts: map[number]`

    The counts of files by status

  - `total_count: number`

    The total number of files

  - `data_source_id: optional string`

    The ID of the data source that the files belong to

  - `only_manually_uploaded: optional boolean`

    Whether to only count manually uploaded files

  - `pipeline_id: optional string`

    The ID of the pipeline that the files belong to

### File Create Response

- `FileCreateResponse = array of PipelineFile`

  - `id: string`

    Unique identifier for the pipeline file.

  - `pipeline_id: string`

    The ID of the pipeline that the file is associated with.

  - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]`

    Hashes for the configuration of the pipeline.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `created_at: optional string`

    When the pipeline file was created.

  - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]`

    Custom metadata for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `data_source_id: optional string`

    The ID of the data source that the file belongs to.

  - `external_file_id: optional string`

    The ID of the file in the external system.

  - `file_id: optional string`

    The ID of the file.

  - `file_size: optional number`

    Size of the file in bytes.

  - `file_type: optional string`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: optional number`

    The number of pages that have been indexed for this file.

  - `last_modified_at: optional string`

    The last modified time of the file.

  - `name: optional string`

    Name of the file.

  - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Permission information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `project_id: optional string`

    The ID of the project that the file belongs to.

  - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]`

    Resource information for the file.

    - `map[unknown]`

    - `array of unknown`

    - `string`

    - `number`

    - `boolean`

  - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: optional string`

    The last time the status was updated.

  - `updated_at: optional string`

    When the pipeline file was last updated.

# Metadata

## Import Pipeline Metadata

**put** `/api/v1/pipelines/{pipeline_id}/metadata`

Import metadata for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/metadata \
    -X PUT \
    -H 'Content-Type: multipart/form-data' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -F 'upload_file=@/path/to/upload_file'
```

#### Response

```json
{
  "foo": "string"
}
```

## Delete Pipeline Files Metadata

**delete** `/api/v1/pipelines/{pipeline_id}/metadata`

Delete metadata for all files in a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/metadata \
    -X DELETE \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

## Domain Types

### Metadata Create Response

- `MetadataCreateResponse = map[string]`

# Documents

## Create Batch Pipeline Documents

**post** `/api/v1/pipelines/{pipeline_id}/documents`

Batch create documents for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `body: array of CloudDocumentCreate`

  - `metadata: map[unknown]`

  - `text: string`

  - `id: optional string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Returns

- `id: string`

- `metadata: map[unknown]`

- `text: string`

- `excluded_embed_metadata_keys: optional array of string`

- `excluded_llm_metadata_keys: optional array of string`

- `page_positions: optional array of number`

  indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

- `status_metadata: optional map[unknown]`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '[
          {
            "metadata": {
              "foo": "bar"
            },
            "text": "text",
            "id": "id",
            "excluded_embed_metadata_keys": [
              "string"
            ],
            "excluded_llm_metadata_keys": [
              "string"
            ],
            "page_positions": [
              0
            ]
          }
        ]'
```

#### Response

```json
[
  {
    "id": "id",
    "metadata": {
      "foo": "bar"
    },
    "text": "text",
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ],
    "page_positions": [
      0
    ],
    "status_metadata": {
      "foo": "bar"
    }
  }
]
```

## Paginated List Pipeline Documents

**get** `/api/v1/pipelines/{pipeline_id}/documents/paginated`

Return a list of documents for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `file_id: optional string`

- `limit: optional number`

- `only_api_data_source_documents: optional boolean`

- `only_direct_upload: optional boolean`

- `skip: optional number`

- `status_refresh_policy: optional "cached" or "ttl"`

  - `"cached"`

  - `"ttl"`

### Cookie Parameters

- `session: optional string`

### Returns

- `documents: array of CloudDocument`

  The documents to list

  - `id: string`

  - `metadata: map[unknown]`

  - `text: string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

  - `status_metadata: optional map[unknown]`

- `limit: number`

  The limit of the documents

- `offset: number`

  The offset of the documents

- `total_count: number`

  The total number of documents

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/paginated \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "documents": [
    {
      "id": "id",
      "metadata": {
        "foo": "bar"
      },
      "text": "text",
      "excluded_embed_metadata_keys": [
        "string"
      ],
      "excluded_llm_metadata_keys": [
        "string"
      ],
      "page_positions": [
        0
      ],
      "status_metadata": {
        "foo": "bar"
      }
    }
  ],
  "limit": 0,
  "offset": 0,
  "total_count": 0
}
```

## Get Pipeline Document

**get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}`

Return a single document for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `document_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `CloudDocument = object { id, metadata, text, 4 more }`

  Cloud document stored in S3.

  - `id: string`

  - `metadata: map[unknown]`

  - `text: string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

  - `status_metadata: optional map[unknown]`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/$DOCUMENT_ID \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "id": "id",
  "metadata": {
    "foo": "bar"
  },
  "text": "text",
  "excluded_embed_metadata_keys": [
    "string"
  ],
  "excluded_llm_metadata_keys": [
    "string"
  ],
  "page_positions": [
    0
  ],
  "status_metadata": {
    "foo": "bar"
  }
}
```

## Delete Pipeline Document

**delete** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}`

Delete a document from a pipeline.
Initiates an async job that will:

1. Delete vectors from the vector store
1. Delete the document from MongoDB after vectors are successfully deleted

### Path Parameters

- `pipeline_id: string`

- `document_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/$DOCUMENT_ID \
    -X DELETE \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

## Get Pipeline Document Status

**get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/status`

Return a single document for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `document_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `ManagedIngestionStatusResponse = object { status, deployment_date, effective_at, 2 more }`

  - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: optional string`

    Date of the deployment.

  - `effective_at: optional string`

    When the status is effective

  - `error: optional array of object { job_id, message, step }`

    List of errors that occurred during ingestion.

    - `job_id: string`

      ID of the job that failed.

    - `message: string`

      List of errors that occurred during ingestion.

    - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: optional string`

    ID of the latest job.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/$DOCUMENT_ID/status \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{
  "status": "NOT_STARTED",
  "deployment_date": "2019-12-27T18:11:19.117Z",
  "effective_at": "2019-12-27T18:11:19.117Z",
  "error": [
    {
      "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "message": "message",
      "step": "MANAGED_INGESTION"
    }
  ],
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
}
```

## Sync Pipeline Document

**post** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/sync`

Sync a specific document for a pipeline.

### Path Parameters

- `pipeline_id: string`

- `document_id: string`

### Cookie Parameters

- `session: optional string`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/$DOCUMENT_ID/sync \
    -X POST \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
{}
```

## List Pipeline Document Chunks

**get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/chunks`

Return a list of chunks for a pipeline document.

### Path Parameters

- `pipeline_id: string`

- `document_id: string`

### Cookie Parameters

- `session: optional string`

### Returns

- `class_name: optional string`

- `embedding: optional array of number`

  Embedding of the node.

- `end_char_idx: optional number`

  End char index of the node.

- `excluded_embed_metadata_keys: optional array of string`

  Metadata keys that are excluded from text for the embed model.

- `excluded_llm_metadata_keys: optional array of string`

  Metadata keys that are excluded from text for the LLM.

- `extra_info: optional map[unknown]`

  A flat dictionary of metadata fields

- `id_: optional string`

  Unique ID of the node.

- `metadata_seperator: optional string`

  Separator between metadata fields when converting to string.

- `metadata_template: optional string`

  Template for how metadata is formatted, with {key} and {value} placeholders.

- `mimetype: optional string`

  MIME type of the node content.

- `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

  A mapping of relationships to other node information.

  - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

    - `node_id: string`

    - `class_name: optional string`

    - `hash: optional string`

    - `metadata: optional map[unknown]`

    - `node_type: optional "1" or "2" or "3" or 2 more or string`

      - `ObjectType = "1" or "2" or "3" or 2 more`

        - `"1"`

        - `"2"`

        - `"3"`

        - `"4"`

        - `"5"`

      - `string`

  - `array of object { node_id, class_name, hash, 2 more }`

    - `node_id: string`

    - `class_name: optional string`

    - `hash: optional string`

    - `metadata: optional map[unknown]`

    - `node_type: optional "1" or "2" or "3" or 2 more or string`

      - `ObjectType = "1" or "2" or "3" or 2 more`

        - `"1"`

        - `"2"`

        - `"3"`

        - `"4"`

        - `"5"`

      - `string`

- `start_char_idx: optional number`

  Start char index of the node.

- `text: optional string`

  Text content of the node.

- `text_template: optional string`

  Template for how text is formatted, with {content} and {metadata_str} placeholders.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents/$DOCUMENT_ID/chunks \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
```

#### Response

```json
[
  {
    "class_name": "class_name",
    "embedding": [
      0
    ],
    "end_char_idx": 0,
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ],
    "extra_info": {
      "foo": "bar"
    },
    "id_": "id_",
    "metadata_seperator": "metadata_seperator",
    "metadata_template": "metadata_template",
    "mimetype": "mimetype",
    "relationships": {
      "foo": {
        "node_id": "node_id",
        "class_name": "class_name",
        "hash": "hash",
        "metadata": {
          "foo": "bar"
        },
        "node_type": "1"
      }
    },
    "start_char_idx": 0,
    "text": "text",
    "text_template": "text_template"
  }
]
```

## Upsert Batch Pipeline Documents

**put** `/api/v1/pipelines/{pipeline_id}/documents`

Batch create or update a document for a pipeline.

### Path Parameters

- `pipeline_id: string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `body: array of CloudDocumentCreate`

  - `metadata: map[unknown]`

  - `text: string`

  - `id: optional string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Returns

- `id: string`

- `metadata: map[unknown]`

- `text: string`

- `excluded_embed_metadata_keys: optional array of string`

- `excluded_llm_metadata_keys: optional array of string`

- `page_positions: optional array of number`

  indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

- `status_metadata: optional map[unknown]`

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/documents \
    -X PUT \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '[
          {
            "metadata": {
              "foo": "bar"
            },
            "text": "text",
            "id": "id",
            "excluded_embed_metadata_keys": [
              "string"
            ],
            "excluded_llm_metadata_keys": [
              "string"
            ],
            "page_positions": [
              0
            ]
          }
        ]'
```

#### Response

```json
[
  {
    "id": "id",
    "metadata": {
      "foo": "bar"
    },
    "text": "text",
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ],
    "page_positions": [
      0
    ],
    "status_metadata": {
      "foo": "bar"
    }
  }
]
```

## Domain Types

### Cloud Document

- `CloudDocument = object { id, metadata, text, 4 more }`

  Cloud document stored in S3.

  - `id: string`

  - `metadata: map[unknown]`

  - `text: string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

  - `status_metadata: optional map[unknown]`

### Cloud Document Create

- `CloudDocumentCreate = object { metadata, text, id, 3 more }`

  Create a new cloud document.

  - `metadata: map[unknown]`

  - `text: string`

  - `id: optional string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Text Node

- `TextNode = object { class_name, embedding, end_char_idx, 11 more }`

  Provided for backward compatibility.

  - `class_name: optional string`

  - `embedding: optional array of number`

    Embedding of the node.

  - `end_char_idx: optional number`

    End char index of the node.

  - `excluded_embed_metadata_keys: optional array of string`

    Metadata keys that are excluded from text for the embed model.

  - `excluded_llm_metadata_keys: optional array of string`

    Metadata keys that are excluded from text for the LLM.

  - `extra_info: optional map[unknown]`

    A flat dictionary of metadata fields

  - `id_: optional string`

    Unique ID of the node.

  - `metadata_seperator: optional string`

    Separator between metadata fields when converting to string.

  - `metadata_template: optional string`

    Template for how metadata is formatted, with {key} and {value} placeholders.

  - `mimetype: optional string`

    MIME type of the node content.

  - `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

    A mapping of relationships to other node information.

    - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

      - `node_id: string`

      - `class_name: optional string`

      - `hash: optional string`

      - `metadata: optional map[unknown]`

      - `node_type: optional "1" or "2" or "3" or 2 more or string`

        - `ObjectType = "1" or "2" or "3" or 2 more`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `string`

    - `array of object { node_id, class_name, hash, 2 more }`

      - `node_id: string`

      - `class_name: optional string`

      - `hash: optional string`

      - `metadata: optional map[unknown]`

      - `node_type: optional "1" or "2" or "3" or 2 more or string`

        - `ObjectType = "1" or "2" or "3" or 2 more`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `string`

  - `start_char_idx: optional number`

    Start char index of the node.

  - `text: optional string`

    Text content of the node.

  - `text_template: optional string`

    Template for how text is formatted, with {content} and {metadata_str} placeholders.

### Document Create Response

- `DocumentCreateResponse = array of CloudDocument`

  - `id: string`

  - `metadata: map[unknown]`

  - `text: string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

  - `status_metadata: optional map[unknown]`

### Document Sync Response

- `DocumentSyncResponse = unknown`

### Document Get Chunks Response

- `DocumentGetChunksResponse = array of TextNode`

  - `class_name: optional string`

  - `embedding: optional array of number`

    Embedding of the node.

  - `end_char_idx: optional number`

    End char index of the node.

  - `excluded_embed_metadata_keys: optional array of string`

    Metadata keys that are excluded from text for the embed model.

  - `excluded_llm_metadata_keys: optional array of string`

    Metadata keys that are excluded from text for the LLM.

  - `extra_info: optional map[unknown]`

    A flat dictionary of metadata fields

  - `id_: optional string`

    Unique ID of the node.

  - `metadata_seperator: optional string`

    Separator between metadata fields when converting to string.

  - `metadata_template: optional string`

    Template for how metadata is formatted, with {key} and {value} placeholders.

  - `mimetype: optional string`

    MIME type of the node content.

  - `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

    A mapping of relationships to other node information.

    - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

      - `node_id: string`

      - `class_name: optional string`

      - `hash: optional string`

      - `metadata: optional map[unknown]`

      - `node_type: optional "1" or "2" or "3" or 2 more or string`

        - `ObjectType = "1" or "2" or "3" or 2 more`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `string`

    - `array of object { node_id, class_name, hash, 2 more }`

      - `node_id: string`

      - `class_name: optional string`

      - `hash: optional string`

      - `metadata: optional map[unknown]`

      - `node_type: optional "1" or "2" or "3" or 2 more or string`

        - `ObjectType = "1" or "2" or "3" or 2 more`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `string`

  - `start_char_idx: optional number`

    Start char index of the node.

  - `text: optional string`

    Text content of the node.

  - `text_template: optional string`

    Template for how text is formatted, with {content} and {metadata_str} placeholders.

### Document Upsert Response

- `DocumentUpsertResponse = array of CloudDocument`

  - `id: string`

  - `metadata: map[unknown]`

  - `text: string`

  - `excluded_embed_metadata_keys: optional array of string`

  - `excluded_llm_metadata_keys: optional array of string`

  - `page_positions: optional array of number`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

  - `status_metadata: optional map[unknown]`