# Pipelines

## Search Pipelines

`pipelines.list(PipelineListParams**kwargs)  -> PipelineListResponse`

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

Search for pipelines by name, type, or project.

### Parameters

- `organization_id: Optional[str]`

- `pipeline_name: Optional[str]`

- `pipeline_type: Optional[PipelineType]`

  Enum for representing the type of a pipeline

  - `"PLAYGROUND"`

  - `"MANAGED"`

- `project_id: Optional[str]`

- `project_name: Optional[str]`

### Returns

- `List[Pipeline]`

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipelines = client.pipelines.list()
print(pipelines)
```

#### 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

`pipelines.create(PipelineCreateParams**kwargs)  -> 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.

### Parameters

- `name: str`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

- `data_sink: Optional[DataSinkCreateParam]`

  Schema for creating a data sink.

  - `component: Component`

    Component that implements the data sink

    - `Dict[str, object]`

    - `class CloudPineconeVectorStore: …`

      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: str`

        The API key for authenticating with Pinecone

      - `index_name: str`

      - `class_name: Optional[str]`

      - `insert_kwargs: Optional[Dict[str, object]]`

      - `namespace: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudPostgresVectorStore: …`

      - `database: str`

      - `embed_dim: int`

      - `host: str`

      - `password: str`

      - `port: int`

      - `schema_name: str`

      - `table_name: str`

      - `user: str`

      - `class_name: Optional[str]`

      - `hnsw_settings: Optional[PgVectorHnswSettings]`

        HNSW settings for PGVector.

        - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: Optional[int]`

          The number of edges to use during the construction phase.

        - `ef_search: Optional[int]`

          The number of edges to use during the search phase.

        - `m: Optional[int]`

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

        - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: Optional[bool]`

      - `perform_setup: Optional[bool]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudQdrantVectorStore: …`

      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: str`

      - `collection_name: str`

      - `url: str`

      - `class_name: Optional[str]`

      - `client_kwargs: Optional[Dict[str, object]]`

      - `max_retries: Optional[int]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudAzureAISearchVectorStore: …`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: str`

      - `search_service_endpoint: str`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

      - `client_secret: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

      - `index_name: Optional[str]`

      - `search_service_api_version: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

      - `tenant_id: Optional[str]`

    - `class CloudMongoDBAtlasVectorSearch: …`

      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: str`

      - `db_name: str`

      - `mongodb_uri: str`

      - `class_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `fulltext_index_name: Optional[str]`

      - `supports_nested_metadata_filters: Optional[bool]`

      - `vector_index_name: Optional[str]`

    - `class CloudMilvusVectorStore: …`

      Cloud Milvus Vector Store.

      - `uri: str`

      - `token: Optional[str]`

      - `class_name: Optional[str]`

      - `collection_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudAstraDBVectorStore: …`

      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: 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

      - `class_name: Optional[str]`

      - `keyspace: Optional[str]`

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

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

  - `name: str`

    The name of the data sink.

  - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: Optional[str]`

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

- `embedding_config: Optional[EmbeddingConfig]`

  - `class AzureOpenAIEmbeddingConfig: …`

    - `component: Optional[AzureOpenAIEmbedding]`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for Azure deployment.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for Azure OpenAI API.

      - `azure_deployment: Optional[str]`

        The Azure deployment to use.

      - `azure_endpoint: Optional[str]`

        The Azure endpoint to use.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["AZURE_EMBEDDING"]]`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `class CohereEmbeddingConfig: …`

    - `component: Optional[CohereEmbedding]`

      Configuration for the Cohere embedding model.

      - `api_key: Optional[str]`

        The Cohere API key.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embedding_type: Optional[str]`

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

      - `input_type: Optional[str]`

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

      - `model_name: Optional[str]`

        The modelId of the Cohere model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `truncate: Optional[str]`

        Truncation type - START/ END/ NONE

    - `type: Optional[Literal["COHERE_EMBEDDING"]]`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `class GeminiEmbeddingConfig: …`

    - `component: Optional[GeminiEmbedding]`

      Configuration for the Gemini embedding model.

      - `api_base: Optional[str]`

        API base to access the model. Defaults to None.

      - `api_key: Optional[str]`

        API key to access the model. Defaults to None.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `model_name: Optional[str]`

        The modelId of the Gemini model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: Optional[int]`

        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[str]`

        The task for embedding model.

      - `title: Optional[str]`

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

      - `transport: Optional[str]`

        Transport to access the model. Defaults to None.

    - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

    - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: Optional[Union[str, bool, null]]`

        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.

        - `str`

        - `bool`

      - `class_name: Optional[str]`

      - `cookies: Optional[Dict[str, str]]`

        Additional cookies to send to the server.

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `headers: Optional[Dict[str, str]]`

        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[str]`

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

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `pooling: Optional[Literal["cls", "mean", "last"]]`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: Optional[str]`

        Instruction to prepend during query embedding.

      - `task: Optional[str]`

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

      - `text_instruction: Optional[str]`

        Instruction to prepend during text embedding.

      - `timeout: Optional[float]`

        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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `class OpenAIEmbeddingConfig: …`

    - `component: Optional[OpenAIEmbedding]`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for OpenAI API.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for OpenAI API.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `class VertexAIEmbeddingConfig: …`

    - `component: Optional[VertexTextEmbedding]`

      Configuration for the VertexAI embedding model.

      - `client_email: Optional[str]`

        The client email for the VertexAI credentials.

      - `location: str`

        The default location to use when making API calls.

      - `private_key: Optional[str]`

        The private key for the VertexAI credentials.

      - `private_key_id: Optional[str]`

        The private key ID for the VertexAI credentials.

      - `project: str`

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

      - `token_uri: Optional[str]`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the Vertex.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: Optional[str]`

        The modelId of the VertexAI model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

    - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `class BedrockEmbeddingConfig: …`

    - `component: Optional[BedrockEmbedding]`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: Optional[str]`

        AWS Access Key ID to use

      - `aws_secret_access_key: Optional[str]`

        AWS Secret Access Key to use

      - `aws_session_token: Optional[str]`

        AWS Session Token to use

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        The maximum number of API retries.

      - `model_name: Optional[str]`

        The modelId of the Bedrock model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `profile_name: Optional[str]`

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

      - `region_name: Optional[str]`

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

      - `timeout: Optional[float]`

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

    - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: Optional[str]`

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

- `llama_parse_parameters: Optional[LlamaParseParametersParam]`

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

  - `adaptive_long_table: Optional[bool]`

  - `aggressive_table_extraction: Optional[bool]`

  - `annotate_links: Optional[bool]`

  - `auto_mode: Optional[bool]`

  - `auto_mode_configuration_json: Optional[str]`

  - `auto_mode_trigger_on_image_in_page: Optional[bool]`

  - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

  - `auto_mode_trigger_on_table_in_page: Optional[bool]`

  - `auto_mode_trigger_on_text_in_page: Optional[str]`

  - `azure_openai_api_version: Optional[str]`

  - `azure_openai_deployment_name: Optional[str]`

  - `azure_openai_endpoint: Optional[str]`

  - `azure_openai_key: Optional[str]`

  - `bbox_bottom: Optional[float]`

  - `bbox_left: Optional[float]`

  - `bbox_right: Optional[float]`

  - `bbox_top: Optional[float]`

  - `bounding_box: Optional[str]`

  - `compact_markdown_table: Optional[bool]`

  - `complemental_formatting_instruction: Optional[str]`

  - `content_guideline_instruction: Optional[str]`

  - `continuous_mode: Optional[bool]`

  - `disable_image_extraction: Optional[bool]`

  - `disable_ocr: Optional[bool]`

  - `disable_reconstruction: Optional[bool]`

  - `do_not_cache: Optional[bool]`

  - `do_not_unroll_columns: Optional[bool]`

  - `enable_cost_optimizer: Optional[bool]`

  - `extract_charts: Optional[bool]`

  - `extract_layout: Optional[bool]`

  - `extract_printed_page_number: Optional[bool]`

  - `fast_mode: Optional[bool]`

  - `formatting_instruction: Optional[str]`

  - `gpt4o_api_key: Optional[str]`

  - `gpt4o_mode: Optional[bool]`

  - `guess_xlsx_sheet_name: Optional[bool]`

  - `hide_footers: Optional[bool]`

  - `hide_headers: Optional[bool]`

  - `high_res_ocr: Optional[bool]`

  - `html_make_all_elements_visible: Optional[bool]`

  - `html_remove_fixed_elements: Optional[bool]`

  - `html_remove_navigation_elements: Optional[bool]`

  - `http_proxy: Optional[str]`

  - `ignore_document_elements_for_layout_detection: Optional[bool]`

  - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: Optional[bool]`

  - `input_s3_path: Optional[str]`

  - `input_s3_region: Optional[str]`

  - `input_url: Optional[str]`

  - `internal_is_screenshot_job: Optional[bool]`

  - `invalidate_cache: Optional[bool]`

  - `is_formatting_instruction: Optional[bool]`

  - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

  - `job_timeout_in_seconds: Optional[float]`

  - `keep_page_separator_when_merging_tables: Optional[bool]`

  - `languages: Optional[List[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[bool]`

  - `line_level_bounding_box: Optional[bool]`

  - `markdown_table_multiline_header_separator: Optional[str]`

  - `max_pages: Optional[int]`

  - `max_pages_enforced: Optional[int]`

  - `merge_tables_across_pages_in_markdown: Optional[bool]`

  - `model: Optional[str]`

  - `outlined_table_extraction: Optional[bool]`

  - `output_pdf_of_document: Optional[bool]`

  - `output_s3_path_prefix: Optional[str]`

  - `output_s3_region: Optional[str]`

  - `output_tables_as_html: Optional[bool]`

  - `page_error_tolerance: Optional[float]`

  - `page_footer_prefix: Optional[str]`

  - `page_footer_suffix: Optional[str]`

  - `page_header_prefix: Optional[str]`

  - `page_header_suffix: Optional[str]`

  - `page_prefix: Optional[str]`

  - `page_separator: Optional[str]`

  - `page_suffix: Optional[str]`

  - `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[str]`

  - `precise_bounding_box: Optional[bool]`

  - `premium_mode: Optional[bool]`

  - `presentation_out_of_bounds_content: Optional[bool]`

  - `presentation_skip_embedded_data: Optional[bool]`

  - `preserve_layout_alignment_across_pages: Optional[bool]`

  - `preserve_very_small_text: Optional[bool]`

  - `preset: Optional[str]`

  - `priority: Optional[Literal["low", "medium", "high", "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[str]`

  - `remove_hidden_text: Optional[bool]`

  - `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[str]`

  - `replace_failed_page_with_error_message_suffix: Optional[str]`

  - `save_images: Optional[bool]`

  - `skip_diagonal_text: Optional[bool]`

  - `specialized_chart_parsing_agentic: Optional[bool]`

  - `specialized_chart_parsing_efficient: Optional[bool]`

  - `specialized_chart_parsing_plus: Optional[bool]`

  - `specialized_image_parsing: Optional[bool]`

  - `spreadsheet_extract_sub_tables: Optional[bool]`

  - `spreadsheet_force_formula_computation: Optional[bool]`

  - `spreadsheet_include_hidden_sheets: Optional[bool]`

  - `strict_mode_buggy_font: Optional[bool]`

  - `strict_mode_image_extraction: Optional[bool]`

  - `strict_mode_image_ocr: Optional[bool]`

  - `strict_mode_reconstruction: Optional[bool]`

  - `structured_output: Optional[bool]`

  - `structured_output_json_schema: Optional[str]`

  - `structured_output_json_schema_name: Optional[str]`

  - `system_prompt: Optional[str]`

  - `system_prompt_append: Optional[str]`

  - `take_screenshot: Optional[bool]`

  - `target_pages: Optional[str]`

  - `tier: Optional[str]`

  - `use_vendor_multimodal_model: Optional[bool]`

  - `user_prompt: Optional[str]`

  - `vendor_multimodal_api_key: Optional[str]`

  - `vendor_multimodal_model_name: Optional[str]`

  - `version: Optional[str]`

  - `webhook_configurations: Optional[List[WebhookConfiguration]]`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

    - `webhook_output_format: Optional[str]`

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

    - `webhook_url: Optional[str]`

      URL to receive webhook POST notifications

  - `webhook_url: Optional[str]`

- `managed_pipeline_id: Optional[str]`

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

- `metadata_config: Optional[PipelineMetadataConfigParam]`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    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[PresetRetrievalParamsParam]`

  Preset retrieval parameters for the pipeline.

  - `alpha: Optional[float]`

    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[str]`

  - `dense_similarity_cutoff: Optional[float]`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: Optional[int]`

    Number of nodes for dense retrieval.

  - `enable_reranking: Optional[bool]`

    Enable reranking for retrieval

  - `files_top_k: Optional[int]`

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

  - `rerank_top_n: Optional[int]`

    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[bool]`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: Optional[bool]`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: Optional[bool]`

    Whether to retrieve page screenshot nodes.

  - `search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `sparse_similarity_top_k: Optional[int]`

    Number of nodes for sparse retrieval.

- `sparse_model_config: Optional[SparseModelConfigParam]`

  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[str]`

  - `model_type: Optional[Literal["splade", "bm25", "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[str]`

  Status of the pipeline deployment.

- `transform_config: Optional[TransformConfig]`

  Configuration for the transformation.

  - `class AutoTransformConfig: …`

    - `chunk_overlap: Optional[int]`

      Chunk overlap for the transformation.

    - `chunk_size: Optional[int]`

      Chunk size for the transformation.

    - `mode: Optional[Literal["auto"]]`

      - `"auto"`

  - `class AdvancedModeTransformConfig: …`

    - `chunking_config: Optional[ChunkingConfig]`

      Configuration for the chunking.

      - `class ChunkingConfigNoneChunkingConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class ChunkingConfigCharacterChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["character"]]`

          - `"character"`

      - `class ChunkingConfigTokenChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["token"]]`

          - `"token"`

        - `separator: Optional[str]`

      - `class ChunkingConfigSentenceChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["sentence"]]`

          - `"sentence"`

        - `paragraph_separator: Optional[str]`

        - `separator: Optional[str]`

      - `class ChunkingConfigSemanticChunkingConfig: …`

        - `breakpoint_percentile_threshold: Optional[int]`

        - `buffer_size: Optional[int]`

        - `mode: Optional[Literal["semantic"]]`

          - `"semantic"`

    - `mode: Optional[Literal["advanced"]]`

      - `"advanced"`

    - `segmentation_config: Optional[SegmentationConfig]`

      Configuration for the segmentation.

      - `class SegmentationConfigNoneSegmentationConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class SegmentationConfigPageSegmentationConfig: …`

        - `mode: Optional[Literal["page"]]`

          - `"page"`

        - `page_separator: Optional[str]`

      - `class SegmentationConfigElementSegmentationConfig: …`

        - `mode: Optional[Literal["element"]]`

          - `"element"`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.create(
    name="x",
)
print(pipeline.id)
```

#### 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

`pipelines.get(strpipeline_id)  -> Pipeline`

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

Get a pipeline by ID.

### Parameters

- `pipeline_id: str`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.get(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline.id)
```

#### 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

`pipelines.update(strpipeline_id, PipelineUpdateParams**kwargs)  -> Pipeline`

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

Update an existing pipeline's configuration.

### Parameters

- `pipeline_id: str`

- `data_sink: Optional[DataSinkCreateParam]`

  Schema for creating a data sink.

  - `component: Component`

    Component that implements the data sink

    - `Dict[str, object]`

    - `class CloudPineconeVectorStore: …`

      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: str`

        The API key for authenticating with Pinecone

      - `index_name: str`

      - `class_name: Optional[str]`

      - `insert_kwargs: Optional[Dict[str, object]]`

      - `namespace: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudPostgresVectorStore: …`

      - `database: str`

      - `embed_dim: int`

      - `host: str`

      - `password: str`

      - `port: int`

      - `schema_name: str`

      - `table_name: str`

      - `user: str`

      - `class_name: Optional[str]`

      - `hnsw_settings: Optional[PgVectorHnswSettings]`

        HNSW settings for PGVector.

        - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: Optional[int]`

          The number of edges to use during the construction phase.

        - `ef_search: Optional[int]`

          The number of edges to use during the search phase.

        - `m: Optional[int]`

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

        - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: Optional[bool]`

      - `perform_setup: Optional[bool]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudQdrantVectorStore: …`

      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: str`

      - `collection_name: str`

      - `url: str`

      - `class_name: Optional[str]`

      - `client_kwargs: Optional[Dict[str, object]]`

      - `max_retries: Optional[int]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudAzureAISearchVectorStore: …`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: str`

      - `search_service_endpoint: str`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

      - `client_secret: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

      - `index_name: Optional[str]`

      - `search_service_api_version: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

      - `tenant_id: Optional[str]`

    - `class CloudMongoDBAtlasVectorSearch: …`

      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: str`

      - `db_name: str`

      - `mongodb_uri: str`

      - `class_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `fulltext_index_name: Optional[str]`

      - `supports_nested_metadata_filters: Optional[bool]`

      - `vector_index_name: Optional[str]`

    - `class CloudMilvusVectorStore: …`

      Cloud Milvus Vector Store.

      - `uri: str`

      - `token: Optional[str]`

      - `class_name: Optional[str]`

      - `collection_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudAstraDBVectorStore: …`

      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: 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

      - `class_name: Optional[str]`

      - `keyspace: Optional[str]`

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

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

  - `name: str`

    The name of the data sink.

  - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: Optional[str]`

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

- `embedding_config: Optional[EmbeddingConfig]`

  - `class AzureOpenAIEmbeddingConfig: …`

    - `component: Optional[AzureOpenAIEmbedding]`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for Azure deployment.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for Azure OpenAI API.

      - `azure_deployment: Optional[str]`

        The Azure deployment to use.

      - `azure_endpoint: Optional[str]`

        The Azure endpoint to use.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["AZURE_EMBEDDING"]]`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `class CohereEmbeddingConfig: …`

    - `component: Optional[CohereEmbedding]`

      Configuration for the Cohere embedding model.

      - `api_key: Optional[str]`

        The Cohere API key.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embedding_type: Optional[str]`

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

      - `input_type: Optional[str]`

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

      - `model_name: Optional[str]`

        The modelId of the Cohere model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `truncate: Optional[str]`

        Truncation type - START/ END/ NONE

    - `type: Optional[Literal["COHERE_EMBEDDING"]]`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `class GeminiEmbeddingConfig: …`

    - `component: Optional[GeminiEmbedding]`

      Configuration for the Gemini embedding model.

      - `api_base: Optional[str]`

        API base to access the model. Defaults to None.

      - `api_key: Optional[str]`

        API key to access the model. Defaults to None.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `model_name: Optional[str]`

        The modelId of the Gemini model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: Optional[int]`

        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[str]`

        The task for embedding model.

      - `title: Optional[str]`

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

      - `transport: Optional[str]`

        Transport to access the model. Defaults to None.

    - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

    - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: Optional[Union[str, bool, null]]`

        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.

        - `str`

        - `bool`

      - `class_name: Optional[str]`

      - `cookies: Optional[Dict[str, str]]`

        Additional cookies to send to the server.

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `headers: Optional[Dict[str, str]]`

        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[str]`

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

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `pooling: Optional[Literal["cls", "mean", "last"]]`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: Optional[str]`

        Instruction to prepend during query embedding.

      - `task: Optional[str]`

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

      - `text_instruction: Optional[str]`

        Instruction to prepend during text embedding.

      - `timeout: Optional[float]`

        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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `class OpenAIEmbeddingConfig: …`

    - `component: Optional[OpenAIEmbedding]`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for OpenAI API.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for OpenAI API.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `class VertexAIEmbeddingConfig: …`

    - `component: Optional[VertexTextEmbedding]`

      Configuration for the VertexAI embedding model.

      - `client_email: Optional[str]`

        The client email for the VertexAI credentials.

      - `location: str`

        The default location to use when making API calls.

      - `private_key: Optional[str]`

        The private key for the VertexAI credentials.

      - `private_key_id: Optional[str]`

        The private key ID for the VertexAI credentials.

      - `project: str`

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

      - `token_uri: Optional[str]`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the Vertex.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: Optional[str]`

        The modelId of the VertexAI model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

    - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `class BedrockEmbeddingConfig: …`

    - `component: Optional[BedrockEmbedding]`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: Optional[str]`

        AWS Access Key ID to use

      - `aws_secret_access_key: Optional[str]`

        AWS Secret Access Key to use

      - `aws_session_token: Optional[str]`

        AWS Session Token to use

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        The maximum number of API retries.

      - `model_name: Optional[str]`

        The modelId of the Bedrock model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `profile_name: Optional[str]`

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

      - `region_name: Optional[str]`

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

      - `timeout: Optional[float]`

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

    - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: Optional[str]`

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

- `llama_parse_parameters: Optional[LlamaParseParametersParam]`

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

  - `adaptive_long_table: Optional[bool]`

  - `aggressive_table_extraction: Optional[bool]`

  - `annotate_links: Optional[bool]`

  - `auto_mode: Optional[bool]`

  - `auto_mode_configuration_json: Optional[str]`

  - `auto_mode_trigger_on_image_in_page: Optional[bool]`

  - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

  - `auto_mode_trigger_on_table_in_page: Optional[bool]`

  - `auto_mode_trigger_on_text_in_page: Optional[str]`

  - `azure_openai_api_version: Optional[str]`

  - `azure_openai_deployment_name: Optional[str]`

  - `azure_openai_endpoint: Optional[str]`

  - `azure_openai_key: Optional[str]`

  - `bbox_bottom: Optional[float]`

  - `bbox_left: Optional[float]`

  - `bbox_right: Optional[float]`

  - `bbox_top: Optional[float]`

  - `bounding_box: Optional[str]`

  - `compact_markdown_table: Optional[bool]`

  - `complemental_formatting_instruction: Optional[str]`

  - `content_guideline_instruction: Optional[str]`

  - `continuous_mode: Optional[bool]`

  - `disable_image_extraction: Optional[bool]`

  - `disable_ocr: Optional[bool]`

  - `disable_reconstruction: Optional[bool]`

  - `do_not_cache: Optional[bool]`

  - `do_not_unroll_columns: Optional[bool]`

  - `enable_cost_optimizer: Optional[bool]`

  - `extract_charts: Optional[bool]`

  - `extract_layout: Optional[bool]`

  - `extract_printed_page_number: Optional[bool]`

  - `fast_mode: Optional[bool]`

  - `formatting_instruction: Optional[str]`

  - `gpt4o_api_key: Optional[str]`

  - `gpt4o_mode: Optional[bool]`

  - `guess_xlsx_sheet_name: Optional[bool]`

  - `hide_footers: Optional[bool]`

  - `hide_headers: Optional[bool]`

  - `high_res_ocr: Optional[bool]`

  - `html_make_all_elements_visible: Optional[bool]`

  - `html_remove_fixed_elements: Optional[bool]`

  - `html_remove_navigation_elements: Optional[bool]`

  - `http_proxy: Optional[str]`

  - `ignore_document_elements_for_layout_detection: Optional[bool]`

  - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: Optional[bool]`

  - `input_s3_path: Optional[str]`

  - `input_s3_region: Optional[str]`

  - `input_url: Optional[str]`

  - `internal_is_screenshot_job: Optional[bool]`

  - `invalidate_cache: Optional[bool]`

  - `is_formatting_instruction: Optional[bool]`

  - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

  - `job_timeout_in_seconds: Optional[float]`

  - `keep_page_separator_when_merging_tables: Optional[bool]`

  - `languages: Optional[List[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[bool]`

  - `line_level_bounding_box: Optional[bool]`

  - `markdown_table_multiline_header_separator: Optional[str]`

  - `max_pages: Optional[int]`

  - `max_pages_enforced: Optional[int]`

  - `merge_tables_across_pages_in_markdown: Optional[bool]`

  - `model: Optional[str]`

  - `outlined_table_extraction: Optional[bool]`

  - `output_pdf_of_document: Optional[bool]`

  - `output_s3_path_prefix: Optional[str]`

  - `output_s3_region: Optional[str]`

  - `output_tables_as_html: Optional[bool]`

  - `page_error_tolerance: Optional[float]`

  - `page_footer_prefix: Optional[str]`

  - `page_footer_suffix: Optional[str]`

  - `page_header_prefix: Optional[str]`

  - `page_header_suffix: Optional[str]`

  - `page_prefix: Optional[str]`

  - `page_separator: Optional[str]`

  - `page_suffix: Optional[str]`

  - `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[str]`

  - `precise_bounding_box: Optional[bool]`

  - `premium_mode: Optional[bool]`

  - `presentation_out_of_bounds_content: Optional[bool]`

  - `presentation_skip_embedded_data: Optional[bool]`

  - `preserve_layout_alignment_across_pages: Optional[bool]`

  - `preserve_very_small_text: Optional[bool]`

  - `preset: Optional[str]`

  - `priority: Optional[Literal["low", "medium", "high", "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[str]`

  - `remove_hidden_text: Optional[bool]`

  - `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[str]`

  - `replace_failed_page_with_error_message_suffix: Optional[str]`

  - `save_images: Optional[bool]`

  - `skip_diagonal_text: Optional[bool]`

  - `specialized_chart_parsing_agentic: Optional[bool]`

  - `specialized_chart_parsing_efficient: Optional[bool]`

  - `specialized_chart_parsing_plus: Optional[bool]`

  - `specialized_image_parsing: Optional[bool]`

  - `spreadsheet_extract_sub_tables: Optional[bool]`

  - `spreadsheet_force_formula_computation: Optional[bool]`

  - `spreadsheet_include_hidden_sheets: Optional[bool]`

  - `strict_mode_buggy_font: Optional[bool]`

  - `strict_mode_image_extraction: Optional[bool]`

  - `strict_mode_image_ocr: Optional[bool]`

  - `strict_mode_reconstruction: Optional[bool]`

  - `structured_output: Optional[bool]`

  - `structured_output_json_schema: Optional[str]`

  - `structured_output_json_schema_name: Optional[str]`

  - `system_prompt: Optional[str]`

  - `system_prompt_append: Optional[str]`

  - `take_screenshot: Optional[bool]`

  - `target_pages: Optional[str]`

  - `tier: Optional[str]`

  - `use_vendor_multimodal_model: Optional[bool]`

  - `user_prompt: Optional[str]`

  - `vendor_multimodal_api_key: Optional[str]`

  - `vendor_multimodal_model_name: Optional[str]`

  - `version: Optional[str]`

  - `webhook_configurations: Optional[List[WebhookConfiguration]]`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

    - `webhook_output_format: Optional[str]`

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

    - `webhook_url: Optional[str]`

      URL to receive webhook POST notifications

  - `webhook_url: Optional[str]`

- `managed_pipeline_id: Optional[str]`

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

- `metadata_config: Optional[PipelineMetadataConfigParam]`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from LLM during retrieval

- `name: Optional[str]`

- `preset_retrieval_parameters: Optional[PresetRetrievalParamsParam]`

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

  - `alpha: Optional[float]`

    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[str]`

  - `dense_similarity_cutoff: Optional[float]`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: Optional[int]`

    Number of nodes for dense retrieval.

  - `enable_reranking: Optional[bool]`

    Enable reranking for retrieval

  - `files_top_k: Optional[int]`

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

  - `rerank_top_n: Optional[int]`

    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[bool]`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: Optional[bool]`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: Optional[bool]`

    Whether to retrieve page screenshot nodes.

  - `search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `sparse_similarity_top_k: Optional[int]`

    Number of nodes for sparse retrieval.

- `sparse_model_config: Optional[SparseModelConfigParam]`

  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[str]`

  - `model_type: Optional[Literal["splade", "bm25", "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[str]`

  Status of the pipeline deployment.

- `transform_config: Optional[TransformConfig]`

  Configuration for the transformation.

  - `class AutoTransformConfig: …`

    - `chunk_overlap: Optional[int]`

      Chunk overlap for the transformation.

    - `chunk_size: Optional[int]`

      Chunk size for the transformation.

    - `mode: Optional[Literal["auto"]]`

      - `"auto"`

  - `class AdvancedModeTransformConfig: …`

    - `chunking_config: Optional[ChunkingConfig]`

      Configuration for the chunking.

      - `class ChunkingConfigNoneChunkingConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class ChunkingConfigCharacterChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["character"]]`

          - `"character"`

      - `class ChunkingConfigTokenChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["token"]]`

          - `"token"`

        - `separator: Optional[str]`

      - `class ChunkingConfigSentenceChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["sentence"]]`

          - `"sentence"`

        - `paragraph_separator: Optional[str]`

        - `separator: Optional[str]`

      - `class ChunkingConfigSemanticChunkingConfig: …`

        - `breakpoint_percentile_threshold: Optional[int]`

        - `buffer_size: Optional[int]`

        - `mode: Optional[Literal["semantic"]]`

          - `"semantic"`

    - `mode: Optional[Literal["advanced"]]`

      - `"advanced"`

    - `segmentation_config: Optional[SegmentationConfig]`

      Configuration for the segmentation.

      - `class SegmentationConfigNoneSegmentationConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class SegmentationConfigPageSegmentationConfig: …`

        - `mode: Optional[Literal["page"]]`

          - `"page"`

        - `page_separator: Optional[str]`

      - `class SegmentationConfigElementSegmentationConfig: …`

        - `mode: Optional[Literal["element"]]`

          - `"element"`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.update(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline.id)
```

#### 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

`pipelines.delete(strpipeline_id)`

**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.

### Parameters

- `pipeline_id: str`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
client.pipelines.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Get Pipeline Status

`pipelines.get_status(strpipeline_id, PipelineGetStatusParams**kwargs)  -> ManagedIngestionStatusResponse`

**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.

### Parameters

- `pipeline_id: str`

- `full_details: Optional[bool]`

### Returns

- `class ManagedIngestionStatusResponse: …`

  - `status: Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 3 more]`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: Optional[datetime]`

    Date of the deployment.

  - `effective_at: Optional[datetime]`

    When the status is effective

  - `error: Optional[List[Error]]`

    List of errors that occurred during ingestion.

    - `job_id: str`

      ID of the job that failed.

    - `message: str`

      List of errors that occurred during ingestion.

    - `step: Literal["MANAGED_INGESTION", "DATA_SOURCE", "FILE_UPDATER", 4 more]`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: Optional[str]`

    ID of the latest job.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
managed_ingestion_status_response = client.pipelines.get_status(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(managed_ingestion_status_response.job_id)
```

#### 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

`pipelines.upsert(PipelineUpsertParams**kwargs)  -> 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.

### Parameters

- `name: str`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

- `data_sink: Optional[DataSinkCreateParam]`

  Schema for creating a data sink.

  - `component: Component`

    Component that implements the data sink

    - `Dict[str, object]`

    - `class CloudPineconeVectorStore: …`

      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: str`

        The API key for authenticating with Pinecone

      - `index_name: str`

      - `class_name: Optional[str]`

      - `insert_kwargs: Optional[Dict[str, object]]`

      - `namespace: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudPostgresVectorStore: …`

      - `database: str`

      - `embed_dim: int`

      - `host: str`

      - `password: str`

      - `port: int`

      - `schema_name: str`

      - `table_name: str`

      - `user: str`

      - `class_name: Optional[str]`

      - `hnsw_settings: Optional[PgVectorHnswSettings]`

        HNSW settings for PGVector.

        - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: Optional[int]`

          The number of edges to use during the construction phase.

        - `ef_search: Optional[int]`

          The number of edges to use during the search phase.

        - `m: Optional[int]`

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

        - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: Optional[bool]`

      - `perform_setup: Optional[bool]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudQdrantVectorStore: …`

      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: str`

      - `collection_name: str`

      - `url: str`

      - `class_name: Optional[str]`

      - `client_kwargs: Optional[Dict[str, object]]`

      - `max_retries: Optional[int]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudAzureAISearchVectorStore: …`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: str`

      - `search_service_endpoint: str`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

      - `client_secret: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

      - `index_name: Optional[str]`

      - `search_service_api_version: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

      - `tenant_id: Optional[str]`

    - `class CloudMongoDBAtlasVectorSearch: …`

      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: str`

      - `db_name: str`

      - `mongodb_uri: str`

      - `class_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `fulltext_index_name: Optional[str]`

      - `supports_nested_metadata_filters: Optional[bool]`

      - `vector_index_name: Optional[str]`

    - `class CloudMilvusVectorStore: …`

      Cloud Milvus Vector Store.

      - `uri: str`

      - `token: Optional[str]`

      - `class_name: Optional[str]`

      - `collection_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudAstraDBVectorStore: …`

      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: 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

      - `class_name: Optional[str]`

      - `keyspace: Optional[str]`

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

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

  - `name: str`

    The name of the data sink.

  - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

- `data_sink_id: Optional[str]`

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

- `embedding_config: Optional[EmbeddingConfig]`

  - `class AzureOpenAIEmbeddingConfig: …`

    - `component: Optional[AzureOpenAIEmbedding]`

      Configuration for the Azure OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for Azure deployment.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for Azure OpenAI API.

      - `azure_deployment: Optional[str]`

        The Azure deployment to use.

      - `azure_endpoint: Optional[str]`

        The Azure endpoint to use.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["AZURE_EMBEDDING"]]`

      Type of the embedding model.

      - `"AZURE_EMBEDDING"`

  - `class CohereEmbeddingConfig: …`

    - `component: Optional[CohereEmbedding]`

      Configuration for the Cohere embedding model.

      - `api_key: Optional[str]`

        The Cohere API key.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embedding_type: Optional[str]`

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

      - `input_type: Optional[str]`

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

      - `model_name: Optional[str]`

        The modelId of the Cohere model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `truncate: Optional[str]`

        Truncation type - START/ END/ NONE

    - `type: Optional[Literal["COHERE_EMBEDDING"]]`

      Type of the embedding model.

      - `"COHERE_EMBEDDING"`

  - `class GeminiEmbeddingConfig: …`

    - `component: Optional[GeminiEmbedding]`

      Configuration for the Gemini embedding model.

      - `api_base: Optional[str]`

        API base to access the model. Defaults to None.

      - `api_key: Optional[str]`

        API key to access the model. Defaults to None.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `model_name: Optional[str]`

        The modelId of the Gemini model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `output_dimensionality: Optional[int]`

        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[str]`

        The task for embedding model.

      - `title: Optional[str]`

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

      - `transport: Optional[str]`

        Transport to access the model. Defaults to None.

    - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

      Type of the embedding model.

      - `"GEMINI_EMBEDDING"`

  - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

    - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

      Configuration for the HuggingFace Inference API embedding model.

      - `token: Optional[Union[str, bool, null]]`

        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.

        - `str`

        - `bool`

      - `class_name: Optional[str]`

      - `cookies: Optional[Dict[str, str]]`

        Additional cookies to send to the server.

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `headers: Optional[Dict[str, str]]`

        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[str]`

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

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `pooling: Optional[Literal["cls", "mean", "last"]]`

        Enum of possible pooling choices with pooling behaviors.

        - `"cls"`

        - `"mean"`

        - `"last"`

      - `query_instruction: Optional[str]`

        Instruction to prepend during query embedding.

      - `task: Optional[str]`

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

      - `text_instruction: Optional[str]`

        Instruction to prepend during text embedding.

      - `timeout: Optional[float]`

        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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

      Type of the embedding model.

      - `"HUGGINGFACE_API_EMBEDDING"`

  - `class OpenAIEmbeddingConfig: …`

    - `component: Optional[OpenAIEmbedding]`

      Configuration for the OpenAI embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the OpenAI API.

      - `api_base: Optional[str]`

        The base URL for OpenAI API.

      - `api_key: Optional[str]`

        The OpenAI API key.

      - `api_version: Optional[str]`

        The version for OpenAI API.

      - `class_name: Optional[str]`

      - `default_headers: Optional[Dict[str, str]]`

        The default headers for API requests.

      - `dimensions: Optional[int]`

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

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        Maximum number of retries.

      - `model_name: Optional[str]`

        The name of the OpenAI embedding model.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `reuse_client: Optional[bool]`

        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[float]`

        Timeout for each request.

    - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"OPENAI_EMBEDDING"`

  - `class VertexAIEmbeddingConfig: …`

    - `component: Optional[VertexTextEmbedding]`

      Configuration for the VertexAI embedding model.

      - `client_email: Optional[str]`

        The client email for the VertexAI credentials.

      - `location: str`

        The default location to use when making API calls.

      - `private_key: Optional[str]`

        The private key for the VertexAI credentials.

      - `private_key_id: Optional[str]`

        The private key ID for the VertexAI credentials.

      - `project: str`

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

      - `token_uri: Optional[str]`

        The token URI for the VertexAI credentials.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the Vertex.

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

        The embedding mode to use.

        - `"default"`

        - `"classification"`

        - `"clustering"`

        - `"similarity"`

        - `"retrieval"`

      - `model_name: Optional[str]`

        The modelId of the VertexAI model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

    - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

      Type of the embedding model.

      - `"VERTEXAI_EMBEDDING"`

  - `class BedrockEmbeddingConfig: …`

    - `component: Optional[BedrockEmbedding]`

      Configuration for the Bedrock embedding model.

      - `additional_kwargs: Optional[Dict[str, object]]`

        Additional kwargs for the bedrock client.

      - `aws_access_key_id: Optional[str]`

        AWS Access Key ID to use

      - `aws_secret_access_key: Optional[str]`

        AWS Secret Access Key to use

      - `aws_session_token: Optional[str]`

        AWS Session Token to use

      - `class_name: Optional[str]`

      - `embed_batch_size: Optional[int]`

        The batch size for embedding calls.

      - `max_retries: Optional[int]`

        The maximum number of API retries.

      - `model_name: Optional[str]`

        The modelId of the Bedrock model to use.

      - `num_workers: Optional[int]`

        The number of workers to use for async embedding calls.

      - `profile_name: Optional[str]`

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

      - `region_name: Optional[str]`

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

      - `timeout: Optional[float]`

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

    - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

      Type of the embedding model.

      - `"BEDROCK_EMBEDDING"`

- `embedding_model_config_id: Optional[str]`

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

- `llama_parse_parameters: Optional[LlamaParseParametersParam]`

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

  - `adaptive_long_table: Optional[bool]`

  - `aggressive_table_extraction: Optional[bool]`

  - `annotate_links: Optional[bool]`

  - `auto_mode: Optional[bool]`

  - `auto_mode_configuration_json: Optional[str]`

  - `auto_mode_trigger_on_image_in_page: Optional[bool]`

  - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

  - `auto_mode_trigger_on_table_in_page: Optional[bool]`

  - `auto_mode_trigger_on_text_in_page: Optional[str]`

  - `azure_openai_api_version: Optional[str]`

  - `azure_openai_deployment_name: Optional[str]`

  - `azure_openai_endpoint: Optional[str]`

  - `azure_openai_key: Optional[str]`

  - `bbox_bottom: Optional[float]`

  - `bbox_left: Optional[float]`

  - `bbox_right: Optional[float]`

  - `bbox_top: Optional[float]`

  - `bounding_box: Optional[str]`

  - `compact_markdown_table: Optional[bool]`

  - `complemental_formatting_instruction: Optional[str]`

  - `content_guideline_instruction: Optional[str]`

  - `continuous_mode: Optional[bool]`

  - `disable_image_extraction: Optional[bool]`

  - `disable_ocr: Optional[bool]`

  - `disable_reconstruction: Optional[bool]`

  - `do_not_cache: Optional[bool]`

  - `do_not_unroll_columns: Optional[bool]`

  - `enable_cost_optimizer: Optional[bool]`

  - `extract_charts: Optional[bool]`

  - `extract_layout: Optional[bool]`

  - `extract_printed_page_number: Optional[bool]`

  - `fast_mode: Optional[bool]`

  - `formatting_instruction: Optional[str]`

  - `gpt4o_api_key: Optional[str]`

  - `gpt4o_mode: Optional[bool]`

  - `guess_xlsx_sheet_name: Optional[bool]`

  - `hide_footers: Optional[bool]`

  - `hide_headers: Optional[bool]`

  - `high_res_ocr: Optional[bool]`

  - `html_make_all_elements_visible: Optional[bool]`

  - `html_remove_fixed_elements: Optional[bool]`

  - `html_remove_navigation_elements: Optional[bool]`

  - `http_proxy: Optional[str]`

  - `ignore_document_elements_for_layout_detection: Optional[bool]`

  - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: Optional[bool]`

  - `input_s3_path: Optional[str]`

  - `input_s3_region: Optional[str]`

  - `input_url: Optional[str]`

  - `internal_is_screenshot_job: Optional[bool]`

  - `invalidate_cache: Optional[bool]`

  - `is_formatting_instruction: Optional[bool]`

  - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

  - `job_timeout_in_seconds: Optional[float]`

  - `keep_page_separator_when_merging_tables: Optional[bool]`

  - `languages: Optional[List[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[bool]`

  - `line_level_bounding_box: Optional[bool]`

  - `markdown_table_multiline_header_separator: Optional[str]`

  - `max_pages: Optional[int]`

  - `max_pages_enforced: Optional[int]`

  - `merge_tables_across_pages_in_markdown: Optional[bool]`

  - `model: Optional[str]`

  - `outlined_table_extraction: Optional[bool]`

  - `output_pdf_of_document: Optional[bool]`

  - `output_s3_path_prefix: Optional[str]`

  - `output_s3_region: Optional[str]`

  - `output_tables_as_html: Optional[bool]`

  - `page_error_tolerance: Optional[float]`

  - `page_footer_prefix: Optional[str]`

  - `page_footer_suffix: Optional[str]`

  - `page_header_prefix: Optional[str]`

  - `page_header_suffix: Optional[str]`

  - `page_prefix: Optional[str]`

  - `page_separator: Optional[str]`

  - `page_suffix: Optional[str]`

  - `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[str]`

  - `precise_bounding_box: Optional[bool]`

  - `premium_mode: Optional[bool]`

  - `presentation_out_of_bounds_content: Optional[bool]`

  - `presentation_skip_embedded_data: Optional[bool]`

  - `preserve_layout_alignment_across_pages: Optional[bool]`

  - `preserve_very_small_text: Optional[bool]`

  - `preset: Optional[str]`

  - `priority: Optional[Literal["low", "medium", "high", "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[str]`

  - `remove_hidden_text: Optional[bool]`

  - `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[str]`

  - `replace_failed_page_with_error_message_suffix: Optional[str]`

  - `save_images: Optional[bool]`

  - `skip_diagonal_text: Optional[bool]`

  - `specialized_chart_parsing_agentic: Optional[bool]`

  - `specialized_chart_parsing_efficient: Optional[bool]`

  - `specialized_chart_parsing_plus: Optional[bool]`

  - `specialized_image_parsing: Optional[bool]`

  - `spreadsheet_extract_sub_tables: Optional[bool]`

  - `spreadsheet_force_formula_computation: Optional[bool]`

  - `spreadsheet_include_hidden_sheets: Optional[bool]`

  - `strict_mode_buggy_font: Optional[bool]`

  - `strict_mode_image_extraction: Optional[bool]`

  - `strict_mode_image_ocr: Optional[bool]`

  - `strict_mode_reconstruction: Optional[bool]`

  - `structured_output: Optional[bool]`

  - `structured_output_json_schema: Optional[str]`

  - `structured_output_json_schema_name: Optional[str]`

  - `system_prompt: Optional[str]`

  - `system_prompt_append: Optional[str]`

  - `take_screenshot: Optional[bool]`

  - `target_pages: Optional[str]`

  - `tier: Optional[str]`

  - `use_vendor_multimodal_model: Optional[bool]`

  - `user_prompt: Optional[str]`

  - `vendor_multimodal_api_key: Optional[str]`

  - `vendor_multimodal_model_name: Optional[str]`

  - `version: Optional[str]`

  - `webhook_configurations: Optional[List[WebhookConfiguration]]`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

    - `webhook_output_format: Optional[str]`

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

    - `webhook_url: Optional[str]`

      URL to receive webhook POST notifications

  - `webhook_url: Optional[str]`

- `managed_pipeline_id: Optional[str]`

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

- `metadata_config: Optional[PipelineMetadataConfigParam]`

  Metadata configuration for the pipeline.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    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[PresetRetrievalParamsParam]`

  Preset retrieval parameters for the pipeline.

  - `alpha: Optional[float]`

    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[str]`

  - `dense_similarity_cutoff: Optional[float]`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: Optional[int]`

    Number of nodes for dense retrieval.

  - `enable_reranking: Optional[bool]`

    Enable reranking for retrieval

  - `files_top_k: Optional[int]`

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

  - `rerank_top_n: Optional[int]`

    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[bool]`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: Optional[bool]`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: Optional[bool]`

    Whether to retrieve page screenshot nodes.

  - `search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `sparse_similarity_top_k: Optional[int]`

    Number of nodes for sparse retrieval.

- `sparse_model_config: Optional[SparseModelConfigParam]`

  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[str]`

  - `model_type: Optional[Literal["splade", "bm25", "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[str]`

  Status of the pipeline deployment.

- `transform_config: Optional[TransformConfig]`

  Configuration for the transformation.

  - `class AutoTransformConfig: …`

    - `chunk_overlap: Optional[int]`

      Chunk overlap for the transformation.

    - `chunk_size: Optional[int]`

      Chunk size for the transformation.

    - `mode: Optional[Literal["auto"]]`

      - `"auto"`

  - `class AdvancedModeTransformConfig: …`

    - `chunking_config: Optional[ChunkingConfig]`

      Configuration for the chunking.

      - `class ChunkingConfigNoneChunkingConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class ChunkingConfigCharacterChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["character"]]`

          - `"character"`

      - `class ChunkingConfigTokenChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["token"]]`

          - `"token"`

        - `separator: Optional[str]`

      - `class ChunkingConfigSentenceChunkingConfig: …`

        - `chunk_overlap: Optional[int]`

        - `chunk_size: Optional[int]`

        - `mode: Optional[Literal["sentence"]]`

          - `"sentence"`

        - `paragraph_separator: Optional[str]`

        - `separator: Optional[str]`

      - `class ChunkingConfigSemanticChunkingConfig: …`

        - `breakpoint_percentile_threshold: Optional[int]`

        - `buffer_size: Optional[int]`

        - `mode: Optional[Literal["semantic"]]`

          - `"semantic"`

    - `mode: Optional[Literal["advanced"]]`

      - `"advanced"`

    - `segmentation_config: Optional[SegmentationConfig]`

      Configuration for the segmentation.

      - `class SegmentationConfigNoneSegmentationConfig: …`

        - `mode: Optional[Literal["none"]]`

          - `"none"`

      - `class SegmentationConfigPageSegmentationConfig: …`

        - `mode: Optional[Literal["page"]]`

          - `"page"`

        - `page_separator: Optional[str]`

      - `class SegmentationConfigElementSegmentationConfig: …`

        - `mode: Optional[Literal["element"]]`

          - `"element"`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.upsert(
    name="x",
)
print(pipeline.id)
```

#### 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

`pipelines.retrieve(strpipeline_id, PipelineRetrieveParams**kwargs)  -> PipelineRetrieveResponse`

**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.

### Parameters

- `pipeline_id: str`

- `query: str`

  The query to retrieve against.

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

- `alpha: Optional[float]`

  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[str]`

- `dense_similarity_cutoff: Optional[float]`

  Minimum similarity score wrt query for retrieval

- `dense_similarity_top_k: Optional[int]`

  Number of nodes for dense retrieval.

- `enable_reranking: Optional[bool]`

  Enable reranking for retrieval

- `files_top_k: Optional[int]`

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

- `rerank_top_n: Optional[int]`

  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[bool]`

  Whether to retrieve image nodes.

- `retrieve_page_figure_nodes: Optional[bool]`

  Whether to retrieve page figure nodes.

- `retrieve_page_screenshot_nodes: Optional[bool]`

  Whether to retrieve page screenshot nodes.

- `search_filters: Optional[MetadataFiltersParam]`

  Metadata filters for vector stores.

  - `filters: List[Filter]`

    - `class FilterMetadataFilter: …`

      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: str`

      - `value: Union[float, str, List[str], 3 more]`

        - `float`

        - `str`

        - `List[str]`

        - `List[float]`

        - `List[int]`

      - `operator: Optional[Literal["==", ">", "<", 11 more]]`

        Vector store filter operator.

        - `"=="`

        - `">"`

        - `"<"`

        - `"!="`

        - `">="`

        - `"<="`

        - `"in"`

        - `"nin"`

        - `"any"`

        - `"all"`

        - `"text_match"`

        - `"text_match_insensitive"`

        - `"contains"`

        - `"is_empty"`

    - `class MetadataFilters: …`

      Metadata filters for vector stores.

  - `condition: Optional[Literal["and", "or", "not"]]`

    Vector store filter conditions to combine different filters.

    - `"and"`

    - `"or"`

    - `"not"`

- `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], Iterable[object], str, 3 more]]]`

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

  - `Dict[str, object]`

  - `Iterable[object]`

  - `str`

  - `float`

  - `bool`

- `sparse_similarity_top_k: Optional[int]`

  Number of nodes for sparse retrieval.

### Returns

- `class PipelineRetrieveResponse: …`

  Schema for the result of an retrieval execution.

  - `pipeline_id: str`

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

  - `retrieval_nodes: List[RetrievalNode]`

    The nodes retrieved by the pipeline for the given query.

    - `node: TextNode`

      Provided for backward compatibility.

      - `class_name: Optional[str]`

      - `embedding: Optional[List[float]]`

        Embedding of the node.

      - `end_char_idx: Optional[int]`

        End char index of the node.

      - `excluded_embed_metadata_keys: Optional[List[str]]`

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

      - `excluded_llm_metadata_keys: Optional[List[str]]`

        Metadata keys that are excluded from text for the LLM.

      - `extra_info: Optional[Dict[str, object]]`

        A flat dictionary of metadata fields

      - `id: Optional[str]`

        Unique ID of the node.

      - `metadata_seperator: Optional[str]`

        Separator between metadata fields when converting to string.

      - `metadata_template: Optional[str]`

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

      - `mimetype: Optional[str]`

        MIME type of the node content.

      - `relationships: Optional[Dict[str, Relationships]]`

        A mapping of relationships to other node information.

        - `class RelationshipsRelatedNodeInfo: …`

          - `node_id: str`

          - `class_name: Optional[str]`

          - `hash: Optional[str]`

          - `metadata: Optional[Dict[str, object]]`

          - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

            - `Literal["1", "2", "3", 2 more]`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `str`

        - `List[RelationshipsUnionMember1]`

          - `node_id: str`

          - `class_name: Optional[str]`

          - `hash: Optional[str]`

          - `metadata: Optional[Dict[str, object]]`

          - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

            - `Literal["1", "2", "3", 2 more]`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `str`

      - `start_char_idx: Optional[int]`

        Start char index of the node.

      - `text: Optional[str]`

        Text content of the node.

      - `text_template: Optional[str]`

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

    - `class_name: Optional[str]`

    - `score: Optional[float]`

  - `class_name: Optional[str]`

  - `image_nodes: Optional[List[PageScreenshotNodeWithScore]]`

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

    - `node: Node`

      - `file_id: str`

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

      - `image_size: int`

        The size of the image in bytes

      - `page_index: int`

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

      - `metadata: Optional[Dict[str, object]]`

        Metadata for the screenshot

    - `score: float`

      The score of the screenshot node

    - `class_name: Optional[str]`

  - `inferred_search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `metadata: Optional[Dict[str, str]]`

    Metadata associated with the retrieval execution

  - `page_figure_nodes: Optional[List[PageFigureNodeWithScore]]`

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

    - `node: Node`

      - `confidence: float`

        The confidence of the figure

      - `figure_name: str`

        The name of the figure

      - `figure_size: int`

        The size of the figure in bytes

      - `file_id: str`

        The ID of the file that the figure was taken from

      - `page_index: int`

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

      - `is_likely_noise: Optional[bool]`

        Whether the figure is likely to be noise

      - `metadata: Optional[Dict[str, object]]`

        Metadata for the figure

    - `score: float`

      The score of the figure node

    - `class_name: Optional[str]`

  - `retrieval_latency: Optional[Dict[str, float]]`

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

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.retrieve(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    query="x",
)
print(pipeline.pipeline_id)
```

#### 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

- `class AdvancedModeTransformConfig: …`

  - `chunking_config: Optional[ChunkingConfig]`

    Configuration for the chunking.

    - `class ChunkingConfigNoneChunkingConfig: …`

      - `mode: Optional[Literal["none"]]`

        - `"none"`

    - `class ChunkingConfigCharacterChunkingConfig: …`

      - `chunk_overlap: Optional[int]`

      - `chunk_size: Optional[int]`

      - `mode: Optional[Literal["character"]]`

        - `"character"`

    - `class ChunkingConfigTokenChunkingConfig: …`

      - `chunk_overlap: Optional[int]`

      - `chunk_size: Optional[int]`

      - `mode: Optional[Literal["token"]]`

        - `"token"`

      - `separator: Optional[str]`

    - `class ChunkingConfigSentenceChunkingConfig: …`

      - `chunk_overlap: Optional[int]`

      - `chunk_size: Optional[int]`

      - `mode: Optional[Literal["sentence"]]`

        - `"sentence"`

      - `paragraph_separator: Optional[str]`

      - `separator: Optional[str]`

    - `class ChunkingConfigSemanticChunkingConfig: …`

      - `breakpoint_percentile_threshold: Optional[int]`

      - `buffer_size: Optional[int]`

      - `mode: Optional[Literal["semantic"]]`

        - `"semantic"`

  - `mode: Optional[Literal["advanced"]]`

    - `"advanced"`

  - `segmentation_config: Optional[SegmentationConfig]`

    Configuration for the segmentation.

    - `class SegmentationConfigNoneSegmentationConfig: …`

      - `mode: Optional[Literal["none"]]`

        - `"none"`

    - `class SegmentationConfigPageSegmentationConfig: …`

      - `mode: Optional[Literal["page"]]`

        - `"page"`

      - `page_separator: Optional[str]`

    - `class SegmentationConfigElementSegmentationConfig: …`

      - `mode: Optional[Literal["element"]]`

        - `"element"`

### Auto Transform Config

- `class AutoTransformConfig: …`

  - `chunk_overlap: Optional[int]`

    Chunk overlap for the transformation.

  - `chunk_size: Optional[int]`

    Chunk size for the transformation.

  - `mode: Optional[Literal["auto"]]`

    - `"auto"`

### Azure OpenAI Embedding

- `class AzureOpenAIEmbedding: …`

  - `additional_kwargs: Optional[Dict[str, object]]`

    Additional kwargs for the OpenAI API.

  - `api_base: Optional[str]`

    The base URL for Azure deployment.

  - `api_key: Optional[str]`

    The OpenAI API key.

  - `api_version: Optional[str]`

    The version for Azure OpenAI API.

  - `azure_deployment: Optional[str]`

    The Azure deployment to use.

  - `azure_endpoint: Optional[str]`

    The Azure endpoint to use.

  - `class_name: Optional[str]`

  - `default_headers: Optional[Dict[str, str]]`

    The default headers for API requests.

  - `dimensions: Optional[int]`

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

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `max_retries: Optional[int]`

    Maximum number of retries.

  - `model_name: Optional[str]`

    The name of the OpenAI embedding model.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `reuse_client: Optional[bool]`

    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[float]`

    Timeout for each request.

### Azure OpenAI Embedding Config

- `class AzureOpenAIEmbeddingConfig: …`

  - `component: Optional[AzureOpenAIEmbedding]`

    Configuration for the Azure OpenAI embedding model.

    - `additional_kwargs: Optional[Dict[str, object]]`

      Additional kwargs for the OpenAI API.

    - `api_base: Optional[str]`

      The base URL for Azure deployment.

    - `api_key: Optional[str]`

      The OpenAI API key.

    - `api_version: Optional[str]`

      The version for Azure OpenAI API.

    - `azure_deployment: Optional[str]`

      The Azure deployment to use.

    - `azure_endpoint: Optional[str]`

      The Azure endpoint to use.

    - `class_name: Optional[str]`

    - `default_headers: Optional[Dict[str, str]]`

      The default headers for API requests.

    - `dimensions: Optional[int]`

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

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `max_retries: Optional[int]`

      Maximum number of retries.

    - `model_name: Optional[str]`

      The name of the OpenAI embedding model.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `reuse_client: Optional[bool]`

      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[float]`

      Timeout for each request.

  - `type: Optional[Literal["AZURE_EMBEDDING"]]`

    Type of the embedding model.

    - `"AZURE_EMBEDDING"`

### Bedrock Embedding

- `class BedrockEmbedding: …`

  - `additional_kwargs: Optional[Dict[str, object]]`

    Additional kwargs for the bedrock client.

  - `aws_access_key_id: Optional[str]`

    AWS Access Key ID to use

  - `aws_secret_access_key: Optional[str]`

    AWS Secret Access Key to use

  - `aws_session_token: Optional[str]`

    AWS Session Token to use

  - `class_name: Optional[str]`

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `max_retries: Optional[int]`

    The maximum number of API retries.

  - `model_name: Optional[str]`

    The modelId of the Bedrock model to use.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `profile_name: Optional[str]`

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

  - `region_name: Optional[str]`

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

  - `timeout: Optional[float]`

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

### Bedrock Embedding Config

- `class BedrockEmbeddingConfig: …`

  - `component: Optional[BedrockEmbedding]`

    Configuration for the Bedrock embedding model.

    - `additional_kwargs: Optional[Dict[str, object]]`

      Additional kwargs for the bedrock client.

    - `aws_access_key_id: Optional[str]`

      AWS Access Key ID to use

    - `aws_secret_access_key: Optional[str]`

      AWS Secret Access Key to use

    - `aws_session_token: Optional[str]`

      AWS Session Token to use

    - `class_name: Optional[str]`

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `max_retries: Optional[int]`

      The maximum number of API retries.

    - `model_name: Optional[str]`

      The modelId of the Bedrock model to use.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `profile_name: Optional[str]`

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

    - `region_name: Optional[str]`

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

    - `timeout: Optional[float]`

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

  - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

    Type of the embedding model.

    - `"BEDROCK_EMBEDDING"`

### Cohere Embedding

- `class CohereEmbedding: …`

  - `api_key: Optional[str]`

    The Cohere API key.

  - `class_name: Optional[str]`

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `embedding_type: Optional[str]`

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

  - `input_type: Optional[str]`

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

  - `model_name: Optional[str]`

    The modelId of the Cohere model to use.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `truncate: Optional[str]`

    Truncation type - START/ END/ NONE

### Cohere Embedding Config

- `class CohereEmbeddingConfig: …`

  - `component: Optional[CohereEmbedding]`

    Configuration for the Cohere embedding model.

    - `api_key: Optional[str]`

      The Cohere API key.

    - `class_name: Optional[str]`

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `embedding_type: Optional[str]`

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

    - `input_type: Optional[str]`

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

    - `model_name: Optional[str]`

      The modelId of the Cohere model to use.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `truncate: Optional[str]`

      Truncation type - START/ END/ NONE

  - `type: Optional[Literal["COHERE_EMBEDDING"]]`

    Type of the embedding model.

    - `"COHERE_EMBEDDING"`

### Data Sink Create

- `class DataSinkCreate: …`

  Schema for creating a data sink.

  - `component: Component`

    Component that implements the data sink

    - `Dict[str, object]`

    - `class CloudPineconeVectorStore: …`

      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: str`

        The API key for authenticating with Pinecone

      - `index_name: str`

      - `class_name: Optional[str]`

      - `insert_kwargs: Optional[Dict[str, object]]`

      - `namespace: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudPostgresVectorStore: …`

      - `database: str`

      - `embed_dim: int`

      - `host: str`

      - `password: str`

      - `port: int`

      - `schema_name: str`

      - `table_name: str`

      - `user: str`

      - `class_name: Optional[str]`

      - `hnsw_settings: Optional[PgVectorHnswSettings]`

        HNSW settings for PGVector.

        - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

          The distance method to use.

          - `"l2"`

          - `"ip"`

          - `"cosine"`

          - `"l1"`

          - `"hamming"`

          - `"jaccard"`

        - `ef_construction: Optional[int]`

          The number of edges to use during the construction phase.

        - `ef_search: Optional[int]`

          The number of edges to use during the search phase.

        - `m: Optional[int]`

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

        - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

          The type of vector to use.

          - `"vector"`

          - `"half_vec"`

          - `"bit"`

          - `"sparse_vec"`

      - `hybrid_search: Optional[bool]`

      - `perform_setup: Optional[bool]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudQdrantVectorStore: …`

      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: str`

      - `collection_name: str`

      - `url: str`

      - `class_name: Optional[str]`

      - `client_kwargs: Optional[Dict[str, object]]`

      - `max_retries: Optional[int]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

    - `class CloudAzureAISearchVectorStore: …`

      Cloud Azure AI Search Vector Store.

      - `search_service_api_key: str`

      - `search_service_endpoint: str`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

      - `client_secret: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

      - `index_name: Optional[str]`

      - `search_service_api_version: Optional[str]`

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

      - `tenant_id: Optional[str]`

    - `class CloudMongoDBAtlasVectorSearch: …`

      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: str`

      - `db_name: str`

      - `mongodb_uri: str`

      - `class_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `fulltext_index_name: Optional[str]`

      - `supports_nested_metadata_filters: Optional[bool]`

      - `vector_index_name: Optional[str]`

    - `class CloudMilvusVectorStore: …`

      Cloud Milvus Vector Store.

      - `uri: str`

      - `token: Optional[str]`

      - `class_name: Optional[str]`

      - `collection_name: Optional[str]`

      - `embedding_dimension: Optional[int]`

      - `supports_nested_metadata_filters: Optional[bool]`

    - `class CloudAstraDBVectorStore: …`

      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: 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

      - `class_name: Optional[str]`

      - `keyspace: Optional[str]`

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

      - `supports_nested_metadata_filters: Optional[Literal[true]]`

        - `true`

  - `name: str`

    The name of the data sink.

  - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

    - `"PINECONE"`

    - `"POSTGRES"`

    - `"QDRANT"`

    - `"AZUREAI_SEARCH"`

    - `"MONGODB_ATLAS"`

    - `"MILVUS"`

    - `"ASTRA_DB"`

### Gemini Embedding

- `class GeminiEmbedding: …`

  - `api_base: Optional[str]`

    API base to access the model. Defaults to None.

  - `api_key: Optional[str]`

    API key to access the model. Defaults to None.

  - `class_name: Optional[str]`

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `model_name: Optional[str]`

    The modelId of the Gemini model to use.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `output_dimensionality: Optional[int]`

    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[str]`

    The task for embedding model.

  - `title: Optional[str]`

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

  - `transport: Optional[str]`

    Transport to access the model. Defaults to None.

### Gemini Embedding Config

- `class GeminiEmbeddingConfig: …`

  - `component: Optional[GeminiEmbedding]`

    Configuration for the Gemini embedding model.

    - `api_base: Optional[str]`

      API base to access the model. Defaults to None.

    - `api_key: Optional[str]`

      API key to access the model. Defaults to None.

    - `class_name: Optional[str]`

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `model_name: Optional[str]`

      The modelId of the Gemini model to use.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `output_dimensionality: Optional[int]`

      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[str]`

      The task for embedding model.

    - `title: Optional[str]`

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

    - `transport: Optional[str]`

      Transport to access the model. Defaults to None.

  - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

    Type of the embedding model.

    - `"GEMINI_EMBEDDING"`

### Hugging Face Inference API Embedding

- `class HuggingFaceInferenceAPIEmbedding: …`

  - `token: Optional[Union[str, bool, null]]`

    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.

    - `str`

    - `bool`

  - `class_name: Optional[str]`

  - `cookies: Optional[Dict[str, str]]`

    Additional cookies to send to the server.

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `headers: Optional[Dict[str, str]]`

    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[str]`

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

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `pooling: Optional[Literal["cls", "mean", "last"]]`

    Enum of possible pooling choices with pooling behaviors.

    - `"cls"`

    - `"mean"`

    - `"last"`

  - `query_instruction: Optional[str]`

    Instruction to prepend during query embedding.

  - `task: Optional[str]`

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

  - `text_instruction: Optional[str]`

    Instruction to prepend during text embedding.

  - `timeout: Optional[float]`

    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

- `class HuggingFaceInferenceAPIEmbeddingConfig: …`

  - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

    Configuration for the HuggingFace Inference API embedding model.

    - `token: Optional[Union[str, bool, null]]`

      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.

      - `str`

      - `bool`

    - `class_name: Optional[str]`

    - `cookies: Optional[Dict[str, str]]`

      Additional cookies to send to the server.

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `headers: Optional[Dict[str, str]]`

      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[str]`

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

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `pooling: Optional[Literal["cls", "mean", "last"]]`

      Enum of possible pooling choices with pooling behaviors.

      - `"cls"`

      - `"mean"`

      - `"last"`

    - `query_instruction: Optional[str]`

      Instruction to prepend during query embedding.

    - `task: Optional[str]`

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

    - `text_instruction: Optional[str]`

      Instruction to prepend during text embedding.

    - `timeout: Optional[float]`

      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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

    Type of the embedding model.

    - `"HUGGINGFACE_API_EMBEDDING"`

### Llama Parse Parameters

- `class LlamaParseParameters: …`

  - `adaptive_long_table: Optional[bool]`

  - `aggressive_table_extraction: Optional[bool]`

  - `annotate_links: Optional[bool]`

  - `auto_mode: Optional[bool]`

  - `auto_mode_configuration_json: Optional[str]`

  - `auto_mode_trigger_on_image_in_page: Optional[bool]`

  - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

  - `auto_mode_trigger_on_table_in_page: Optional[bool]`

  - `auto_mode_trigger_on_text_in_page: Optional[str]`

  - `azure_openai_api_version: Optional[str]`

  - `azure_openai_deployment_name: Optional[str]`

  - `azure_openai_endpoint: Optional[str]`

  - `azure_openai_key: Optional[str]`

  - `bbox_bottom: Optional[float]`

  - `bbox_left: Optional[float]`

  - `bbox_right: Optional[float]`

  - `bbox_top: Optional[float]`

  - `bounding_box: Optional[str]`

  - `compact_markdown_table: Optional[bool]`

  - `complemental_formatting_instruction: Optional[str]`

  - `content_guideline_instruction: Optional[str]`

  - `continuous_mode: Optional[bool]`

  - `disable_image_extraction: Optional[bool]`

  - `disable_ocr: Optional[bool]`

  - `disable_reconstruction: Optional[bool]`

  - `do_not_cache: Optional[bool]`

  - `do_not_unroll_columns: Optional[bool]`

  - `enable_cost_optimizer: Optional[bool]`

  - `extract_charts: Optional[bool]`

  - `extract_layout: Optional[bool]`

  - `extract_printed_page_number: Optional[bool]`

  - `fast_mode: Optional[bool]`

  - `formatting_instruction: Optional[str]`

  - `gpt4o_api_key: Optional[str]`

  - `gpt4o_mode: Optional[bool]`

  - `guess_xlsx_sheet_name: Optional[bool]`

  - `hide_footers: Optional[bool]`

  - `hide_headers: Optional[bool]`

  - `high_res_ocr: Optional[bool]`

  - `html_make_all_elements_visible: Optional[bool]`

  - `html_remove_fixed_elements: Optional[bool]`

  - `html_remove_navigation_elements: Optional[bool]`

  - `http_proxy: Optional[str]`

  - `ignore_document_elements_for_layout_detection: Optional[bool]`

  - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

    - `"screenshot"`

    - `"embedded"`

    - `"layout"`

  - `inline_images_in_markdown: Optional[bool]`

  - `input_s3_path: Optional[str]`

  - `input_s3_region: Optional[str]`

  - `input_url: Optional[str]`

  - `internal_is_screenshot_job: Optional[bool]`

  - `invalidate_cache: Optional[bool]`

  - `is_formatting_instruction: Optional[bool]`

  - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

  - `job_timeout_in_seconds: Optional[float]`

  - `keep_page_separator_when_merging_tables: Optional[bool]`

  - `languages: Optional[List[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[bool]`

  - `line_level_bounding_box: Optional[bool]`

  - `markdown_table_multiline_header_separator: Optional[str]`

  - `max_pages: Optional[int]`

  - `max_pages_enforced: Optional[int]`

  - `merge_tables_across_pages_in_markdown: Optional[bool]`

  - `model: Optional[str]`

  - `outlined_table_extraction: Optional[bool]`

  - `output_pdf_of_document: Optional[bool]`

  - `output_s3_path_prefix: Optional[str]`

  - `output_s3_region: Optional[str]`

  - `output_tables_as_html: Optional[bool]`

  - `page_error_tolerance: Optional[float]`

  - `page_footer_prefix: Optional[str]`

  - `page_footer_suffix: Optional[str]`

  - `page_header_prefix: Optional[str]`

  - `page_header_suffix: Optional[str]`

  - `page_prefix: Optional[str]`

  - `page_separator: Optional[str]`

  - `page_suffix: Optional[str]`

  - `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[str]`

  - `precise_bounding_box: Optional[bool]`

  - `premium_mode: Optional[bool]`

  - `presentation_out_of_bounds_content: Optional[bool]`

  - `presentation_skip_embedded_data: Optional[bool]`

  - `preserve_layout_alignment_across_pages: Optional[bool]`

  - `preserve_very_small_text: Optional[bool]`

  - `preset: Optional[str]`

  - `priority: Optional[Literal["low", "medium", "high", "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[str]`

  - `remove_hidden_text: Optional[bool]`

  - `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[str]`

  - `replace_failed_page_with_error_message_suffix: Optional[str]`

  - `save_images: Optional[bool]`

  - `skip_diagonal_text: Optional[bool]`

  - `specialized_chart_parsing_agentic: Optional[bool]`

  - `specialized_chart_parsing_efficient: Optional[bool]`

  - `specialized_chart_parsing_plus: Optional[bool]`

  - `specialized_image_parsing: Optional[bool]`

  - `spreadsheet_extract_sub_tables: Optional[bool]`

  - `spreadsheet_force_formula_computation: Optional[bool]`

  - `spreadsheet_include_hidden_sheets: Optional[bool]`

  - `strict_mode_buggy_font: Optional[bool]`

  - `strict_mode_image_extraction: Optional[bool]`

  - `strict_mode_image_ocr: Optional[bool]`

  - `strict_mode_reconstruction: Optional[bool]`

  - `structured_output: Optional[bool]`

  - `structured_output_json_schema: Optional[str]`

  - `structured_output_json_schema_name: Optional[str]`

  - `system_prompt: Optional[str]`

  - `system_prompt_append: Optional[str]`

  - `take_screenshot: Optional[bool]`

  - `target_pages: Optional[str]`

  - `tier: Optional[str]`

  - `use_vendor_multimodal_model: Optional[bool]`

  - `user_prompt: Optional[str]`

  - `vendor_multimodal_api_key: Optional[str]`

  - `vendor_multimodal_model_name: Optional[str]`

  - `version: Optional[str]`

  - `webhook_configurations: Optional[List[WebhookConfiguration]]`

    Outbound webhook endpoints to notify on job status changes

    - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

    - `webhook_output_format: Optional[str]`

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

    - `webhook_url: Optional[str]`

      URL to receive webhook POST notifications

  - `webhook_url: Optional[str]`

### Llm Parameters

- `class LlmParameters: …`

  - `class_name: Optional[str]`

  - `model_name: Optional[Literal["GPT_4O", "GPT_4O_MINI", "GPT_4_1", 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[str]`

    The system prompt to use for the completion.

  - `temperature: Optional[float]`

    The temperature value for the model.

  - `use_chain_of_thought_reasoning: Optional[bool]`

    Whether to use chain of thought reasoning.

  - `use_citation: Optional[bool]`

    Whether to show citations in the response.

### Managed Ingestion Status Response

- `class ManagedIngestionStatusResponse: …`

  - `status: Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 3 more]`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: Optional[datetime]`

    Date of the deployment.

  - `effective_at: Optional[datetime]`

    When the status is effective

  - `error: Optional[List[Error]]`

    List of errors that occurred during ingestion.

    - `job_id: str`

      ID of the job that failed.

    - `message: str`

      List of errors that occurred during ingestion.

    - `step: Literal["MANAGED_INGESTION", "DATA_SOURCE", "FILE_UPDATER", 4 more]`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: Optional[str]`

    ID of the latest job.

### Message Role

- `Literal["system", "developer", "user", 5 more]`

  Message role.

  - `"system"`

  - `"developer"`

  - `"user"`

  - `"assistant"`

  - `"function"`

  - `"tool"`

  - `"chatbot"`

  - `"model"`

### Metadata Filters

- `class MetadataFilters: …`

  Metadata filters for vector stores.

  - `filters: List[Filter]`

    - `class FilterMetadataFilter: …`

      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: str`

      - `value: Union[float, str, List[str], 3 more]`

        - `float`

        - `str`

        - `List[str]`

        - `List[float]`

        - `List[int]`

      - `operator: Optional[Literal["==", ">", "<", 11 more]]`

        Vector store filter operator.

        - `"=="`

        - `">"`

        - `"<"`

        - `"!="`

        - `">="`

        - `"<="`

        - `"in"`

        - `"nin"`

        - `"any"`

        - `"all"`

        - `"text_match"`

        - `"text_match_insensitive"`

        - `"contains"`

        - `"is_empty"`

    - `class MetadataFilters: …`

      Metadata filters for vector stores.

  - `condition: Optional[Literal["and", "or", "not"]]`

    Vector store filter conditions to combine different filters.

    - `"and"`

    - `"or"`

    - `"not"`

### OpenAI Embedding

- `class OpenAIEmbedding: …`

  - `additional_kwargs: Optional[Dict[str, object]]`

    Additional kwargs for the OpenAI API.

  - `api_base: Optional[str]`

    The base URL for OpenAI API.

  - `api_key: Optional[str]`

    The OpenAI API key.

  - `api_version: Optional[str]`

    The version for OpenAI API.

  - `class_name: Optional[str]`

  - `default_headers: Optional[Dict[str, str]]`

    The default headers for API requests.

  - `dimensions: Optional[int]`

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

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `max_retries: Optional[int]`

    Maximum number of retries.

  - `model_name: Optional[str]`

    The name of the OpenAI embedding model.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

  - `reuse_client: Optional[bool]`

    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[float]`

    Timeout for each request.

### OpenAI Embedding Config

- `class OpenAIEmbeddingConfig: …`

  - `component: Optional[OpenAIEmbedding]`

    Configuration for the OpenAI embedding model.

    - `additional_kwargs: Optional[Dict[str, object]]`

      Additional kwargs for the OpenAI API.

    - `api_base: Optional[str]`

      The base URL for OpenAI API.

    - `api_key: Optional[str]`

      The OpenAI API key.

    - `api_version: Optional[str]`

      The version for OpenAI API.

    - `class_name: Optional[str]`

    - `default_headers: Optional[Dict[str, str]]`

      The default headers for API requests.

    - `dimensions: Optional[int]`

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

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `max_retries: Optional[int]`

      Maximum number of retries.

    - `model_name: Optional[str]`

      The name of the OpenAI embedding model.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

    - `reuse_client: Optional[bool]`

      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[float]`

      Timeout for each request.

  - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

    Type of the embedding model.

    - `"OPENAI_EMBEDDING"`

### Page Figure Node With Score

- `class PageFigureNodeWithScore: …`

  Page figure metadata with score

  - `node: Node`

    - `confidence: float`

      The confidence of the figure

    - `figure_name: str`

      The name of the figure

    - `figure_size: int`

      The size of the figure in bytes

    - `file_id: str`

      The ID of the file that the figure was taken from

    - `page_index: int`

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

    - `is_likely_noise: Optional[bool]`

      Whether the figure is likely to be noise

    - `metadata: Optional[Dict[str, object]]`

      Metadata for the figure

  - `score: float`

    The score of the figure node

  - `class_name: Optional[str]`

### Page Screenshot Node With Score

- `class PageScreenshotNodeWithScore: …`

  Page screenshot metadata with score

  - `node: Node`

    - `file_id: str`

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

    - `image_size: int`

      The size of the image in bytes

    - `page_index: int`

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

    - `metadata: Optional[Dict[str, object]]`

      Metadata for the screenshot

  - `score: float`

    The score of the screenshot node

  - `class_name: Optional[str]`

### Pipeline

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Pipeline Create

- `class PipelineCreate: …`

  Schema for creating a pipeline.

  - `name: str`

  - `data_sink: Optional[DataSinkCreate]`

    Schema for creating a data sink.

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

  - `data_sink_id: Optional[str]`

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

  - `embedding_config: Optional[EmbeddingConfig]`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[str]`

    Status of the pipeline deployment.

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

### Pipeline Metadata Config

- `class PipelineMetadataConfig: …`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from embeddings

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    List of metadata keys to exclude from LLM during retrieval

### Pipeline Type

- `Literal["PLAYGROUND", "MANAGED"]`

  Enum for representing the type of a pipeline

  - `"PLAYGROUND"`

  - `"MANAGED"`

### Preset Retrieval Params

- `class PresetRetrievalParams: …`

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

  - `alpha: Optional[float]`

    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[str]`

  - `dense_similarity_cutoff: Optional[float]`

    Minimum similarity score wrt query for retrieval

  - `dense_similarity_top_k: Optional[int]`

    Number of nodes for dense retrieval.

  - `enable_reranking: Optional[bool]`

    Enable reranking for retrieval

  - `files_top_k: Optional[int]`

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

  - `rerank_top_n: Optional[int]`

    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[bool]`

    Whether to retrieve image nodes.

  - `retrieve_page_figure_nodes: Optional[bool]`

    Whether to retrieve page figure nodes.

  - `retrieve_page_screenshot_nodes: Optional[bool]`

    Whether to retrieve page screenshot nodes.

  - `search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `sparse_similarity_top_k: Optional[int]`

    Number of nodes for sparse retrieval.

### Retrieval Mode

- `Literal["chunks", "files_via_metadata", "files_via_content", "auto_routed"]`

  - `"chunks"`

  - `"files_via_metadata"`

  - `"files_via_content"`

  - `"auto_routed"`

### Sparse Model Config

- `class 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[str]`

  - `model_type: Optional[Literal["splade", "bm25", "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

- `class VertexAIEmbeddingConfig: …`

  - `component: Optional[VertexTextEmbedding]`

    Configuration for the VertexAI embedding model.

    - `client_email: Optional[str]`

      The client email for the VertexAI credentials.

    - `location: str`

      The default location to use when making API calls.

    - `private_key: Optional[str]`

      The private key for the VertexAI credentials.

    - `private_key_id: Optional[str]`

      The private key ID for the VertexAI credentials.

    - `project: str`

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

    - `token_uri: Optional[str]`

      The token URI for the VertexAI credentials.

    - `additional_kwargs: Optional[Dict[str, object]]`

      Additional kwargs for the Vertex.

    - `class_name: Optional[str]`

    - `embed_batch_size: Optional[int]`

      The batch size for embedding calls.

    - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

      The embedding mode to use.

      - `"default"`

      - `"classification"`

      - `"clustering"`

      - `"similarity"`

      - `"retrieval"`

    - `model_name: Optional[str]`

      The modelId of the VertexAI model to use.

    - `num_workers: Optional[int]`

      The number of workers to use for async embedding calls.

  - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

    Type of the embedding model.

    - `"VERTEXAI_EMBEDDING"`

### Vertex Text Embedding

- `class VertexTextEmbedding: …`

  - `client_email: Optional[str]`

    The client email for the VertexAI credentials.

  - `location: str`

    The default location to use when making API calls.

  - `private_key: Optional[str]`

    The private key for the VertexAI credentials.

  - `private_key_id: Optional[str]`

    The private key ID for the VertexAI credentials.

  - `project: str`

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

  - `token_uri: Optional[str]`

    The token URI for the VertexAI credentials.

  - `additional_kwargs: Optional[Dict[str, object]]`

    Additional kwargs for the Vertex.

  - `class_name: Optional[str]`

  - `embed_batch_size: Optional[int]`

    The batch size for embedding calls.

  - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

    The embedding mode to use.

    - `"default"`

    - `"classification"`

    - `"clustering"`

    - `"similarity"`

    - `"retrieval"`

  - `model_name: Optional[str]`

    The modelId of the VertexAI model to use.

  - `num_workers: Optional[int]`

    The number of workers to use for async embedding calls.

### Pipeline List Response

- `List[Pipeline]`

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Pipeline Retrieve Response

- `class PipelineRetrieveResponse: …`

  Schema for the result of an retrieval execution.

  - `pipeline_id: str`

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

  - `retrieval_nodes: List[RetrievalNode]`

    The nodes retrieved by the pipeline for the given query.

    - `node: TextNode`

      Provided for backward compatibility.

      - `class_name: Optional[str]`

      - `embedding: Optional[List[float]]`

        Embedding of the node.

      - `end_char_idx: Optional[int]`

        End char index of the node.

      - `excluded_embed_metadata_keys: Optional[List[str]]`

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

      - `excluded_llm_metadata_keys: Optional[List[str]]`

        Metadata keys that are excluded from text for the LLM.

      - `extra_info: Optional[Dict[str, object]]`

        A flat dictionary of metadata fields

      - `id: Optional[str]`

        Unique ID of the node.

      - `metadata_seperator: Optional[str]`

        Separator between metadata fields when converting to string.

      - `metadata_template: Optional[str]`

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

      - `mimetype: Optional[str]`

        MIME type of the node content.

      - `relationships: Optional[Dict[str, Relationships]]`

        A mapping of relationships to other node information.

        - `class RelationshipsRelatedNodeInfo: …`

          - `node_id: str`

          - `class_name: Optional[str]`

          - `hash: Optional[str]`

          - `metadata: Optional[Dict[str, object]]`

          - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

            - `Literal["1", "2", "3", 2 more]`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `str`

        - `List[RelationshipsUnionMember1]`

          - `node_id: str`

          - `class_name: Optional[str]`

          - `hash: Optional[str]`

          - `metadata: Optional[Dict[str, object]]`

          - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

            - `Literal["1", "2", "3", 2 more]`

              - `"1"`

              - `"2"`

              - `"3"`

              - `"4"`

              - `"5"`

            - `str`

      - `start_char_idx: Optional[int]`

        Start char index of the node.

      - `text: Optional[str]`

        Text content of the node.

      - `text_template: Optional[str]`

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

    - `class_name: Optional[str]`

    - `score: Optional[float]`

  - `class_name: Optional[str]`

  - `image_nodes: Optional[List[PageScreenshotNodeWithScore]]`

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

    - `node: Node`

      - `file_id: str`

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

      - `image_size: int`

        The size of the image in bytes

      - `page_index: int`

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

      - `metadata: Optional[Dict[str, object]]`

        Metadata for the screenshot

    - `score: float`

      The score of the screenshot node

    - `class_name: Optional[str]`

  - `inferred_search_filters: Optional[MetadataFilters]`

    Metadata filters for vector stores.

    - `filters: List[Filter]`

      - `class FilterMetadataFilter: …`

        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: str`

        - `value: Union[float, str, List[str], 3 more]`

          - `float`

          - `str`

          - `List[str]`

          - `List[float]`

          - `List[int]`

        - `operator: Optional[Literal["==", ">", "<", 11 more]]`

          Vector store filter operator.

          - `"=="`

          - `">"`

          - `"<"`

          - `"!="`

          - `">="`

          - `"<="`

          - `"in"`

          - `"nin"`

          - `"any"`

          - `"all"`

          - `"text_match"`

          - `"text_match_insensitive"`

          - `"contains"`

          - `"is_empty"`

      - `class MetadataFilters: …`

        Metadata filters for vector stores.

    - `condition: Optional[Literal["and", "or", "not"]]`

      Vector store filter conditions to combine different filters.

      - `"and"`

      - `"or"`

      - `"not"`

  - `metadata: Optional[Dict[str, str]]`

    Metadata associated with the retrieval execution

  - `page_figure_nodes: Optional[List[PageFigureNodeWithScore]]`

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

    - `node: Node`

      - `confidence: float`

        The confidence of the figure

      - `figure_name: str`

        The name of the figure

      - `figure_size: int`

        The size of the figure in bytes

      - `file_id: str`

        The ID of the file that the figure was taken from

      - `page_index: int`

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

      - `is_likely_noise: Optional[bool]`

        Whether the figure is likely to be noise

      - `metadata: Optional[Dict[str, object]]`

        Metadata for the figure

    - `score: float`

      The score of the figure node

    - `class_name: Optional[str]`

  - `retrieval_latency: Optional[Dict[str, float]]`

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

# Sync

## Sync Pipeline

`pipelines.sync.create(strpipeline_id)  -> 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.

### Parameters

- `pipeline_id: str`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.sync.create(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline.id)
```

#### 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

`pipelines.sync.cancel(strpipeline_id)  -> Pipeline`

**post** `/api/v1/pipelines/{pipeline_id}/sync/cancel`

Cancel all running sync jobs for a pipeline.

### Parameters

- `pipeline_id: str`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.sync.cancel(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline.id)
```

#### 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

`pipelines.data_sources.get_data_sources(strpipeline_id)  -> DataSourceGetDataSourcesResponse`

**get** `/api/v1/pipelines/{pipeline_id}/data-sources`

Get data sources for a pipeline.

### Parameters

- `pipeline_id: str`

### Returns

- `List[PipelineDataSource]`

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "2.1"]]`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline_data_sources = client.pipelines.data_sources.get_data_sources(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline_data_sources)
```

#### 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

`pipelines.data_sources.update_data_sources(strpipeline_id, DataSourceUpdateDataSourcesParams**kwargs)  -> DataSourceUpdateDataSourcesResponse`

**put** `/api/v1/pipelines/{pipeline_id}/data-sources`

Add data sources to a pipeline.

### Parameters

- `pipeline_id: str`

- `body: Iterable[Body]`

  - `data_source_id: str`

    The ID of the data source.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced. Valid values are: 21600, 43200, 86400

### Returns

- `List[PipelineDataSource]`

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "2.1"]]`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline_data_sources = client.pipelines.data_sources.update_data_sources(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    body=[{
        "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
    }],
)
print(pipeline_data_sources)
```

#### 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

`pipelines.data_sources.update(strdata_source_id, DataSourceUpdateParams**kwargs)  -> PipelineDataSource`

**put** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}`

Update the configuration of a data source in a pipeline.

### Parameters

- `pipeline_id: str`

- `data_source_id: str`

- `sync_interval: Optional[float]`

  The interval at which the data source should be synced.

### Returns

- `class PipelineDataSource: …`

  Schema for a data source in a pipeline.

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "2.1"]]`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline_data_source = client.pipelines.data_sources.update(
    data_source_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline_data_source.id)
```

#### 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

`pipelines.data_sources.get_status(strdata_source_id, DataSourceGetStatusParams**kwargs)  -> ManagedIngestionStatusResponse`

**get** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/status`

Get the status of a data source for a pipeline.

### Parameters

- `pipeline_id: str`

- `data_source_id: str`

### Returns

- `class ManagedIngestionStatusResponse: …`

  - `status: Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 3 more]`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: Optional[datetime]`

    Date of the deployment.

  - `effective_at: Optional[datetime]`

    When the status is effective

  - `error: Optional[List[Error]]`

    List of errors that occurred during ingestion.

    - `job_id: str`

      ID of the job that failed.

    - `message: str`

      List of errors that occurred during ingestion.

    - `step: Literal["MANAGED_INGESTION", "DATA_SOURCE", "FILE_UPDATER", 4 more]`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: Optional[str]`

    ID of the latest job.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
managed_ingestion_status_response = client.pipelines.data_sources.get_status(
    data_source_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(managed_ingestion_status_response.job_id)
```

#### 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

`pipelines.data_sources.sync(strdata_source_id, DataSourceSyncParams**kwargs)  -> Pipeline`

**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.

### Parameters

- `pipeline_id: str`

- `data_source_id: str`

- `pipeline_file_ids: Optional[SequenceNotStr[str]]`

### Returns

- `class Pipeline: …`

  Schema for a pipeline.

  - `id: str`

    Unique identifier

  - `embedding_config: EmbeddingConfig`

    - `class EmbeddingConfigManagedOpenAIEmbeddingConfig: …`

      - `component: Optional[EmbeddingConfigManagedOpenAIEmbeddingConfigComponent]`

        Configuration for the Managed OpenAI embedding model.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[Literal["openai-text-embedding-3-small"]]`

          The name of the OpenAI embedding model.

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["MANAGED_OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"MANAGED_OPENAI_EMBEDDING"`

    - `class AzureOpenAIEmbeddingConfig: …`

      - `component: Optional[AzureOpenAIEmbedding]`

        Configuration for the Azure OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for Azure deployment.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for Azure OpenAI API.

        - `azure_deployment: Optional[str]`

          The Azure deployment to use.

        - `azure_endpoint: Optional[str]`

          The Azure endpoint to use.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["AZURE_EMBEDDING"]]`

        Type of the embedding model.

        - `"AZURE_EMBEDDING"`

    - `class CohereEmbeddingConfig: …`

      - `component: Optional[CohereEmbedding]`

        Configuration for the Cohere embedding model.

        - `api_key: Optional[str]`

          The Cohere API key.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embedding_type: Optional[str]`

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

        - `input_type: Optional[str]`

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

        - `model_name: Optional[str]`

          The modelId of the Cohere model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `truncate: Optional[str]`

          Truncation type - START/ END/ NONE

      - `type: Optional[Literal["COHERE_EMBEDDING"]]`

        Type of the embedding model.

        - `"COHERE_EMBEDDING"`

    - `class GeminiEmbeddingConfig: …`

      - `component: Optional[GeminiEmbedding]`

        Configuration for the Gemini embedding model.

        - `api_base: Optional[str]`

          API base to access the model. Defaults to None.

        - `api_key: Optional[str]`

          API key to access the model. Defaults to None.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `model_name: Optional[str]`

          The modelId of the Gemini model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `output_dimensionality: Optional[int]`

          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[str]`

          The task for embedding model.

        - `title: Optional[str]`

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

        - `transport: Optional[str]`

          Transport to access the model. Defaults to None.

      - `type: Optional[Literal["GEMINI_EMBEDDING"]]`

        Type of the embedding model.

        - `"GEMINI_EMBEDDING"`

    - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `component: Optional[HuggingFaceInferenceAPIEmbedding]`

        Configuration for the HuggingFace Inference API embedding model.

        - `token: Optional[Union[str, bool, null]]`

          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.

          - `str`

          - `bool`

        - `class_name: Optional[str]`

        - `cookies: Optional[Dict[str, str]]`

          Additional cookies to send to the server.

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `headers: Optional[Dict[str, str]]`

          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[str]`

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

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `pooling: Optional[Literal["cls", "mean", "last"]]`

          Enum of possible pooling choices with pooling behaviors.

          - `"cls"`

          - `"mean"`

          - `"last"`

        - `query_instruction: Optional[str]`

          Instruction to prepend during query embedding.

        - `task: Optional[str]`

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

        - `text_instruction: Optional[str]`

          Instruction to prepend during text embedding.

        - `timeout: Optional[float]`

          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[Literal["HUGGINGFACE_API_EMBEDDING"]]`

        Type of the embedding model.

        - `"HUGGINGFACE_API_EMBEDDING"`

    - `class OpenAIEmbeddingConfig: …`

      - `component: Optional[OpenAIEmbedding]`

        Configuration for the OpenAI embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the OpenAI API.

        - `api_base: Optional[str]`

          The base URL for OpenAI API.

        - `api_key: Optional[str]`

          The OpenAI API key.

        - `api_version: Optional[str]`

          The version for OpenAI API.

        - `class_name: Optional[str]`

        - `default_headers: Optional[Dict[str, str]]`

          The default headers for API requests.

        - `dimensions: Optional[int]`

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

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          Maximum number of retries.

        - `model_name: Optional[str]`

          The name of the OpenAI embedding model.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `reuse_client: Optional[bool]`

          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[float]`

          Timeout for each request.

      - `type: Optional[Literal["OPENAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"OPENAI_EMBEDDING"`

    - `class VertexAIEmbeddingConfig: …`

      - `component: Optional[VertexTextEmbedding]`

        Configuration for the VertexAI embedding model.

        - `client_email: Optional[str]`

          The client email for the VertexAI credentials.

        - `location: str`

          The default location to use when making API calls.

        - `private_key: Optional[str]`

          The private key for the VertexAI credentials.

        - `private_key_id: Optional[str]`

          The private key ID for the VertexAI credentials.

        - `project: str`

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

        - `token_uri: Optional[str]`

          The token URI for the VertexAI credentials.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the Vertex.

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `embed_mode: Optional[Literal["default", "classification", "clustering", 2 more]]`

          The embedding mode to use.

          - `"default"`

          - `"classification"`

          - `"clustering"`

          - `"similarity"`

          - `"retrieval"`

        - `model_name: Optional[str]`

          The modelId of the VertexAI model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

      - `type: Optional[Literal["VERTEXAI_EMBEDDING"]]`

        Type of the embedding model.

        - `"VERTEXAI_EMBEDDING"`

    - `class BedrockEmbeddingConfig: …`

      - `component: Optional[BedrockEmbedding]`

        Configuration for the Bedrock embedding model.

        - `additional_kwargs: Optional[Dict[str, object]]`

          Additional kwargs for the bedrock client.

        - `aws_access_key_id: Optional[str]`

          AWS Access Key ID to use

        - `aws_secret_access_key: Optional[str]`

          AWS Secret Access Key to use

        - `aws_session_token: Optional[str]`

          AWS Session Token to use

        - `class_name: Optional[str]`

        - `embed_batch_size: Optional[int]`

          The batch size for embedding calls.

        - `max_retries: Optional[int]`

          The maximum number of API retries.

        - `model_name: Optional[str]`

          The modelId of the Bedrock model to use.

        - `num_workers: Optional[int]`

          The number of workers to use for async embedding calls.

        - `profile_name: Optional[str]`

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

        - `region_name: Optional[str]`

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

        - `timeout: Optional[float]`

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

      - `type: Optional[Literal["BEDROCK_EMBEDDING"]]`

        Type of the embedding model.

        - `"BEDROCK_EMBEDDING"`

  - `name: str`

  - `project_id: str`

  - `config_hash: Optional[ConfigHash]`

    Hashes for the configuration of a pipeline.

    - `embedding_config_hash: Optional[str]`

      Hash of the embedding config.

    - `parsing_config_hash: Optional[str]`

      Hash of the llama parse parameters.

    - `transform_config_hash: Optional[str]`

      Hash of the transform config.

  - `created_at: Optional[datetime]`

    Creation datetime

  - `data_sink: Optional[DataSink]`

    Schema for a data sink.

    - `id: str`

      Unique identifier

    - `component: Component`

      Component that implements the data sink

      - `Dict[str, object]`

      - `class CloudPineconeVectorStore: …`

        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: str`

          The API key for authenticating with Pinecone

        - `index_name: str`

        - `class_name: Optional[str]`

        - `insert_kwargs: Optional[Dict[str, object]]`

        - `namespace: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudPostgresVectorStore: …`

        - `database: str`

        - `embed_dim: int`

        - `host: str`

        - `password: str`

        - `port: int`

        - `schema_name: str`

        - `table_name: str`

        - `user: str`

        - `class_name: Optional[str]`

        - `hnsw_settings: Optional[PgVectorHnswSettings]`

          HNSW settings for PGVector.

          - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]`

            The distance method to use.

            - `"l2"`

            - `"ip"`

            - `"cosine"`

            - `"l1"`

            - `"hamming"`

            - `"jaccard"`

          - `ef_construction: Optional[int]`

            The number of edges to use during the construction phase.

          - `ef_search: Optional[int]`

            The number of edges to use during the search phase.

          - `m: Optional[int]`

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

          - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]`

            The type of vector to use.

            - `"vector"`

            - `"half_vec"`

            - `"bit"`

            - `"sparse_vec"`

        - `hybrid_search: Optional[bool]`

        - `perform_setup: Optional[bool]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudQdrantVectorStore: …`

        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: str`

        - `collection_name: str`

        - `url: str`

        - `class_name: Optional[str]`

        - `client_kwargs: Optional[Dict[str, object]]`

        - `max_retries: Optional[int]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

      - `class CloudAzureAISearchVectorStore: …`

        Cloud Azure AI Search Vector Store.

        - `search_service_api_key: str`

        - `search_service_endpoint: str`

        - `class_name: Optional[str]`

        - `client_id: Optional[str]`

        - `client_secret: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `filterable_metadata_field_keys: Optional[Dict[str, object]]`

        - `index_name: Optional[str]`

        - `search_service_api_version: Optional[str]`

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

        - `tenant_id: Optional[str]`

      - `class CloudMongoDBAtlasVectorSearch: …`

        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: str`

        - `db_name: str`

        - `mongodb_uri: str`

        - `class_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `fulltext_index_name: Optional[str]`

        - `supports_nested_metadata_filters: Optional[bool]`

        - `vector_index_name: Optional[str]`

      - `class CloudMilvusVectorStore: …`

        Cloud Milvus Vector Store.

        - `uri: str`

        - `token: Optional[str]`

        - `class_name: Optional[str]`

        - `collection_name: Optional[str]`

        - `embedding_dimension: Optional[int]`

        - `supports_nested_metadata_filters: Optional[bool]`

      - `class CloudAstraDBVectorStore: …`

        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: 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

        - `class_name: Optional[str]`

        - `keyspace: Optional[str]`

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

        - `supports_nested_metadata_filters: Optional[Literal[true]]`

          - `true`

    - `name: str`

      The name of the data sink.

    - `project_id: str`

    - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]`

      - `"PINECONE"`

      - `"POSTGRES"`

      - `"QDRANT"`

      - `"AZUREAI_SEARCH"`

      - `"MONGODB_ATLAS"`

      - `"MILVUS"`

      - `"ASTRA_DB"`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config: Optional[EmbeddingModelConfig]`

    Schema for an embedding model config.

    - `id: str`

      Unique identifier

    - `embedding_config: EmbeddingModelConfigEmbeddingConfig`

      The embedding configuration for the embedding model config.

      - `class AzureOpenAIEmbeddingConfig: …`

      - `class CohereEmbeddingConfig: …`

      - `class GeminiEmbeddingConfig: …`

      - `class HuggingFaceInferenceAPIEmbeddingConfig: …`

      - `class OpenAIEmbeddingConfig: …`

      - `class VertexAIEmbeddingConfig: …`

      - `class BedrockEmbeddingConfig: …`

    - `name: str`

      The name of the embedding model config.

    - `project_id: str`

    - `created_at: Optional[datetime]`

      Creation datetime

    - `updated_at: Optional[datetime]`

      Update datetime

  - `embedding_model_config_id: Optional[str]`

    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[bool]`

    - `aggressive_table_extraction: Optional[bool]`

    - `annotate_links: Optional[bool]`

    - `auto_mode: Optional[bool]`

    - `auto_mode_configuration_json: Optional[str]`

    - `auto_mode_trigger_on_image_in_page: Optional[bool]`

    - `auto_mode_trigger_on_regexp_in_page: Optional[str]`

    - `auto_mode_trigger_on_table_in_page: Optional[bool]`

    - `auto_mode_trigger_on_text_in_page: Optional[str]`

    - `azure_openai_api_version: Optional[str]`

    - `azure_openai_deployment_name: Optional[str]`

    - `azure_openai_endpoint: Optional[str]`

    - `azure_openai_key: Optional[str]`

    - `bbox_bottom: Optional[float]`

    - `bbox_left: Optional[float]`

    - `bbox_right: Optional[float]`

    - `bbox_top: Optional[float]`

    - `bounding_box: Optional[str]`

    - `compact_markdown_table: Optional[bool]`

    - `complemental_formatting_instruction: Optional[str]`

    - `content_guideline_instruction: Optional[str]`

    - `continuous_mode: Optional[bool]`

    - `disable_image_extraction: Optional[bool]`

    - `disable_ocr: Optional[bool]`

    - `disable_reconstruction: Optional[bool]`

    - `do_not_cache: Optional[bool]`

    - `do_not_unroll_columns: Optional[bool]`

    - `enable_cost_optimizer: Optional[bool]`

    - `extract_charts: Optional[bool]`

    - `extract_layout: Optional[bool]`

    - `extract_printed_page_number: Optional[bool]`

    - `fast_mode: Optional[bool]`

    - `formatting_instruction: Optional[str]`

    - `gpt4o_api_key: Optional[str]`

    - `gpt4o_mode: Optional[bool]`

    - `guess_xlsx_sheet_name: Optional[bool]`

    - `hide_footers: Optional[bool]`

    - `hide_headers: Optional[bool]`

    - `high_res_ocr: Optional[bool]`

    - `html_make_all_elements_visible: Optional[bool]`

    - `html_remove_fixed_elements: Optional[bool]`

    - `html_remove_navigation_elements: Optional[bool]`

    - `http_proxy: Optional[str]`

    - `ignore_document_elements_for_layout_detection: Optional[bool]`

    - `images_to_save: Optional[List[Literal["screenshot", "embedded", "layout"]]]`

      - `"screenshot"`

      - `"embedded"`

      - `"layout"`

    - `inline_images_in_markdown: Optional[bool]`

    - `input_s3_path: Optional[str]`

    - `input_s3_region: Optional[str]`

    - `input_url: Optional[str]`

    - `internal_is_screenshot_job: Optional[bool]`

    - `invalidate_cache: Optional[bool]`

    - `is_formatting_instruction: Optional[bool]`

    - `job_timeout_extra_time_per_page_in_seconds: Optional[float]`

    - `job_timeout_in_seconds: Optional[float]`

    - `keep_page_separator_when_merging_tables: Optional[bool]`

    - `languages: Optional[List[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[bool]`

    - `line_level_bounding_box: Optional[bool]`

    - `markdown_table_multiline_header_separator: Optional[str]`

    - `max_pages: Optional[int]`

    - `max_pages_enforced: Optional[int]`

    - `merge_tables_across_pages_in_markdown: Optional[bool]`

    - `model: Optional[str]`

    - `outlined_table_extraction: Optional[bool]`

    - `output_pdf_of_document: Optional[bool]`

    - `output_s3_path_prefix: Optional[str]`

    - `output_s3_region: Optional[str]`

    - `output_tables_as_html: Optional[bool]`

    - `page_error_tolerance: Optional[float]`

    - `page_footer_prefix: Optional[str]`

    - `page_footer_suffix: Optional[str]`

    - `page_header_prefix: Optional[str]`

    - `page_header_suffix: Optional[str]`

    - `page_prefix: Optional[str]`

    - `page_separator: Optional[str]`

    - `page_suffix: Optional[str]`

    - `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[str]`

    - `precise_bounding_box: Optional[bool]`

    - `premium_mode: Optional[bool]`

    - `presentation_out_of_bounds_content: Optional[bool]`

    - `presentation_skip_embedded_data: Optional[bool]`

    - `preserve_layout_alignment_across_pages: Optional[bool]`

    - `preserve_very_small_text: Optional[bool]`

    - `preset: Optional[str]`

    - `priority: Optional[Literal["low", "medium", "high", "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[str]`

    - `remove_hidden_text: Optional[bool]`

    - `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[str]`

    - `replace_failed_page_with_error_message_suffix: Optional[str]`

    - `save_images: Optional[bool]`

    - `skip_diagonal_text: Optional[bool]`

    - `specialized_chart_parsing_agentic: Optional[bool]`

    - `specialized_chart_parsing_efficient: Optional[bool]`

    - `specialized_chart_parsing_plus: Optional[bool]`

    - `specialized_image_parsing: Optional[bool]`

    - `spreadsheet_extract_sub_tables: Optional[bool]`

    - `spreadsheet_force_formula_computation: Optional[bool]`

    - `spreadsheet_include_hidden_sheets: Optional[bool]`

    - `strict_mode_buggy_font: Optional[bool]`

    - `strict_mode_image_extraction: Optional[bool]`

    - `strict_mode_image_ocr: Optional[bool]`

    - `strict_mode_reconstruction: Optional[bool]`

    - `structured_output: Optional[bool]`

    - `structured_output_json_schema: Optional[str]`

    - `structured_output_json_schema_name: Optional[str]`

    - `system_prompt: Optional[str]`

    - `system_prompt_append: Optional[str]`

    - `take_screenshot: Optional[bool]`

    - `target_pages: Optional[str]`

    - `tier: Optional[str]`

    - `use_vendor_multimodal_model: Optional[bool]`

    - `user_prompt: Optional[str]`

    - `vendor_multimodal_api_key: Optional[str]`

    - `vendor_multimodal_model_name: Optional[str]`

    - `version: Optional[str]`

    - `webhook_configurations: Optional[List[WebhookConfiguration]]`

      Outbound webhook endpoints to notify on job status changes

      - `webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 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[Dict[str, str]]`

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

      - `webhook_output_format: Optional[str]`

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

      - `webhook_url: Optional[str]`

        URL to receive webhook POST notifications

    - `webhook_url: Optional[str]`

  - `managed_pipeline_id: Optional[str]`

    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[List[str]]`

      List of metadata keys to exclude from embeddings

    - `excluded_llm_metadata_keys: Optional[List[str]]`

      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[float]`

      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[str]`

    - `dense_similarity_cutoff: Optional[float]`

      Minimum similarity score wrt query for retrieval

    - `dense_similarity_top_k: Optional[int]`

      Number of nodes for dense retrieval.

    - `enable_reranking: Optional[bool]`

      Enable reranking for retrieval

    - `files_top_k: Optional[int]`

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

    - `rerank_top_n: Optional[int]`

      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[bool]`

      Whether to retrieve image nodes.

    - `retrieve_page_figure_nodes: Optional[bool]`

      Whether to retrieve page figure nodes.

    - `retrieve_page_screenshot_nodes: Optional[bool]`

      Whether to retrieve page screenshot nodes.

    - `search_filters: Optional[MetadataFilters]`

      Metadata filters for vector stores.

      - `filters: List[Filter]`

        - `class FilterMetadataFilter: …`

          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: str`

          - `value: Union[float, str, List[str], 3 more]`

            - `float`

            - `str`

            - `List[str]`

            - `List[float]`

            - `List[int]`

          - `operator: Optional[Literal["==", ">", "<", 11 more]]`

            Vector store filter operator.

            - `"=="`

            - `">"`

            - `"<"`

            - `"!="`

            - `">="`

            - `"<="`

            - `"in"`

            - `"nin"`

            - `"any"`

            - `"all"`

            - `"text_match"`

            - `"text_match_insensitive"`

            - `"contains"`

            - `"is_empty"`

        - `class MetadataFilters: …`

          Metadata filters for vector stores.

      - `condition: Optional[Literal["and", "or", "not"]]`

        Vector store filter conditions to combine different filters.

        - `"and"`

        - `"or"`

        - `"not"`

    - `search_filters_inference_schema: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

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

      - `Dict[str, object]`

      - `List[object]`

      - `str`

      - `float`

      - `bool`

    - `sparse_similarity_top_k: Optional[int]`

      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[str]`

    - `model_type: Optional[Literal["splade", "bm25", "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[Literal["CREATED", "DELETING"]]`

    Status of the pipeline.

    - `"CREATED"`

    - `"DELETING"`

  - `transform_config: Optional[TransformConfig]`

    Configuration for the transformation.

    - `class AutoTransformConfig: …`

      - `chunk_overlap: Optional[int]`

        Chunk overlap for the transformation.

      - `chunk_size: Optional[int]`

        Chunk size for the transformation.

      - `mode: Optional[Literal["auto"]]`

        - `"auto"`

    - `class AdvancedModeTransformConfig: …`

      - `chunking_config: Optional[ChunkingConfig]`

        Configuration for the chunking.

        - `class ChunkingConfigNoneChunkingConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class ChunkingConfigCharacterChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["character"]]`

            - `"character"`

        - `class ChunkingConfigTokenChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["token"]]`

            - `"token"`

          - `separator: Optional[str]`

        - `class ChunkingConfigSentenceChunkingConfig: …`

          - `chunk_overlap: Optional[int]`

          - `chunk_size: Optional[int]`

          - `mode: Optional[Literal["sentence"]]`

            - `"sentence"`

          - `paragraph_separator: Optional[str]`

          - `separator: Optional[str]`

        - `class ChunkingConfigSemanticChunkingConfig: …`

          - `breakpoint_percentile_threshold: Optional[int]`

          - `buffer_size: Optional[int]`

          - `mode: Optional[Literal["semantic"]]`

            - `"semantic"`

      - `mode: Optional[Literal["advanced"]]`

        - `"advanced"`

      - `segmentation_config: Optional[SegmentationConfig]`

        Configuration for the segmentation.

        - `class SegmentationConfigNoneSegmentationConfig: …`

          - `mode: Optional[Literal["none"]]`

            - `"none"`

        - `class SegmentationConfigPageSegmentationConfig: …`

          - `mode: Optional[Literal["page"]]`

            - `"page"`

          - `page_separator: Optional[str]`

        - `class SegmentationConfigElementSegmentationConfig: …`

          - `mode: Optional[Literal["element"]]`

            - `"element"`

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.data_sources.sync(
    data_source_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline.id)
```

#### 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

- `class PipelineDataSource: …`

  Schema for a data source in a pipeline.

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "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

- `List[PipelineDataSource]`

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "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

- `List[PipelineDataSource]`

  - `id: str`

    Unique identifier

  - `component: Component`

    Component that implements the data source

    - `Dict[str, object]`

    - `class CloudS3DataSource: …`

      - `bucket: str`

        The name of the S3 bucket to read from.

      - `aws_access_id: Optional[str]`

        The AWS access ID to use for authentication.

      - `aws_access_secret: Optional[str]`

        The AWS access secret to use for authentication.

      - `class_name: Optional[str]`

      - `prefix: Optional[str]`

        The prefix of the S3 objects to read from.

      - `regex_pattern: Optional[str]`

        The regex pattern to filter S3 objects. Must be a valid regex pattern.

      - `s3_endpoint_url: Optional[str]`

        The S3 endpoint URL to use for authentication.

      - `supports_access_control: Optional[bool]`

    - `class CloudAzStorageBlobDataSource: …`

      - `account_url: str`

        The Azure Storage Blob account URL to use for authentication.

      - `container_name: str`

        The name of the Azure Storage Blob container to read from.

      - `account_key: Optional[str]`

        The Azure Storage Blob account key to use for authentication.

      - `account_name: Optional[str]`

        The Azure Storage Blob account name to use for authentication.

      - `blob: Optional[str]`

        The blob name to read from.

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        The Azure AD client ID to use for authentication.

      - `client_secret: Optional[str]`

        The Azure AD client secret to use for authentication.

      - `prefix: Optional[str]`

        The prefix of the Azure Storage Blob objects to read from.

      - `supports_access_control: Optional[bool]`

      - `tenant_id: Optional[str]`

        The Azure AD tenant ID to use for authentication.

    - `class CloudGoogleDriveDataSource: …`

      - `folder_id: str`

        The ID of the Google Drive folder to read from.

      - `class_name: Optional[str]`

      - `service_account_key: Optional[Dict[str, str]]`

        A dictionary containing secret values

      - `supports_access_control: Optional[bool]`

    - `class CloudOneDriveDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `user_principal_name: str`

        The user principal name to use for authentication.

      - `class_name: Optional[str]`

      - `folder_id: Optional[str]`

        The ID of the OneDrive folder to read from.

      - `folder_path: Optional[str]`

        The path of the OneDrive folder to read from.

      - `required_exts: Optional[List[str]]`

        The list of required file extensions.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSharepointDataSource: …`

      - `client_id: str`

        The client ID to use for authentication.

      - `client_secret: str`

        The client secret to use for authentication.

      - `tenant_id: str`

        The tenant ID to use for authentication.

      - `class_name: Optional[str]`

      - `drive_name: Optional[str]`

        The name of the Sharepoint drive to read from.

      - `exclude_path_patterns: Optional[List[str]]`

        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[str]`

        The ID of the Sharepoint folder to read from.

      - `folder_path: Optional[str]`

        The path of the Sharepoint folder to read from.

      - `get_permissions: Optional[bool]`

        Whether to get permissions for the sharepoint site.

      - `include_path_patterns: Optional[List[str]]`

        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[List[str]]`

        The list of required file extensions.

      - `site_id: Optional[str]`

        The ID of the SharePoint site to download from.

      - `site_name: Optional[str]`

        The name of the SharePoint site to download from.

      - `supports_access_control: Optional[Literal[true]]`

        - `true`

    - `class CloudSlackDataSource: …`

      - `slack_token: str`

        Slack Bot Token.

      - `channel_ids: Optional[str]`

        Slack Channel.

      - `channel_patterns: Optional[str]`

        Slack Channel name pattern.

      - `class_name: Optional[str]`

      - `earliest_date: Optional[str]`

        Earliest date.

      - `earliest_date_timestamp: Optional[float]`

        Earliest date timestamp.

      - `latest_date: Optional[str]`

        Latest date.

      - `latest_date_timestamp: Optional[float]`

        Latest date timestamp.

      - `supports_access_control: Optional[bool]`

    - `class CloudNotionPageDataSource: …`

      - `integration_token: str`

        The integration token to use for authentication.

      - `class_name: Optional[str]`

      - `database_ids: Optional[str]`

        The Notion Database Id to read content from.

      - `page_ids: Optional[str]`

        The Page ID's of the Notion to read from.

      - `supports_access_control: Optional[bool]`

    - `class CloudConfluenceDataSource: …`

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Confluence APIs.

      - `server_url: str`

        The server URL of the Confluence instance.

      - `api_token: Optional[str]`

        The API token to use for authentication.

      - `class_name: Optional[str]`

      - `cql: Optional[str]`

        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[bool]`

          Whether to skip failed batches/lists and continue processing

      - `index_restricted_pages: Optional[bool]`

        Whether to index restricted pages.

      - `keep_markdown_format: Optional[bool]`

        Whether to keep the markdown format.

      - `label: Optional[str]`

        The label to use for fetching pages.

      - `page_ids: Optional[str]`

        The page IDs of the Confluence to read from.

      - `space_key: Optional[str]`

        The space key to read from.

      - `supports_access_control: Optional[bool]`

      - `user_name: Optional[str]`

        The username to use for authentication.

    - `class CloudJiraDataSource: …`

      Cloud Jira Data Source integrating JiraReader.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `api_token: Optional[str]`

        The API/ Access Token used for Basic, PAT and OAuth2 authentication.

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `server_url: Optional[str]`

        The server url for Jira Cloud.

      - `supports_access_control: Optional[bool]`

    - `class CloudJiraDataSourceV2: …`

      Cloud Jira Data Source integrating JiraReaderV2.

      - `authentication_mechanism: str`

        Type of Authentication for connecting to Jira APIs.

      - `query: str`

        JQL (Jira Query Language) query to search.

      - `server_url: str`

        The server url for Jira Cloud.

      - `api_token: Optional[str]`

        The API Access Token used for Basic, PAT and OAuth2 authentication.

      - `api_version: Optional[Literal["2", "3"]]`

        Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

        - `"2"`

        - `"3"`

      - `class_name: Optional[str]`

      - `cloud_id: Optional[str]`

        The cloud ID, used in case of OAuth2.

      - `email: Optional[str]`

        The email address to use for authentication.

      - `expand: Optional[str]`

        Fields to expand in the response.

      - `fields: Optional[List[str]]`

        List of fields to retrieve from Jira. If None, retrieves all fields.

      - `get_permissions: Optional[bool]`

        Whether to fetch project role permissions and issue-level security

      - `requests_per_minute: Optional[int]`

        Rate limit for Jira API requests per minute.

      - `supports_access_control: Optional[bool]`

    - `class CloudBoxDataSource: …`

      - `authentication_mechanism: Literal["developer_token", "ccg"]`

        The type of authentication to use (Developer Token or CCG)

        - `"developer_token"`

        - `"ccg"`

      - `class_name: Optional[str]`

      - `client_id: Optional[str]`

        Box API key used for identifying the application the user is authenticating with

      - `client_secret: Optional[str]`

        Box API secret used for making auth requests.

      - `developer_token: Optional[str]`

        Developer token for authentication if authentication_mechanism is 'developer_token'.

      - `enterprise_id: Optional[str]`

        Box Enterprise ID, if provided authenticates as service.

      - `folder_id: Optional[str]`

        The ID of the Box folder to read from.

      - `supports_access_control: Optional[bool]`

      - `user_id: Optional[str]`

        Box User ID, if provided authenticates as user.

  - `data_source_id: str`

    The ID of the data source.

  - `last_synced_at: datetime`

    The last time the data source was automatically synced.

  - `name: str`

    The name of the data source.

  - `pipeline_id: str`

    The ID of the pipeline.

  - `project_id: str`

  - `source_type: Literal["S3", "AZURE_STORAGE_BLOB", "GOOGLE_DRIVE", 8 more]`

    - `"S3"`

    - `"AZURE_STORAGE_BLOB"`

    - `"GOOGLE_DRIVE"`

    - `"MICROSOFT_ONEDRIVE"`

    - `"MICROSOFT_SHAREPOINT"`

    - `"SLACK"`

    - `"NOTION_PAGE"`

    - `"CONFLUENCE"`

    - `"JIRA"`

    - `"JIRA_V2"`

    - `"BOX"`

  - `created_at: Optional[datetime]`

    Creation datetime

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata that will be present on all data loaded from the data source

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    The status of the data source in the pipeline.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `sync_interval: Optional[float]`

    The interval at which the data source should be synced.

  - `sync_schedule_set_by: Optional[str]`

    The id of the user who set the sync schedule.

  - `updated_at: Optional[datetime]`

    Update datetime

  - `version_metadata: Optional[DataSourceReaderVersionMetadata]`

    Version metadata for the data source

    - `reader_version: Optional[Literal["1.0", "2.0", "2.1"]]`

      The version of the reader to use for this data source.

      - `"1.0"`

      - `"2.0"`

      - `"2.1"`

# Images

## List File Page Screenshots

`pipelines.images.list_page_screenshots(strid, ImageListPageScreenshotsParams**kwargs)  -> ImageListPageScreenshotsResponse`

**get** `/api/v1/files/{id}/page_screenshots`

List metadata for all screenshots of pages from a file.

### Parameters

- `id: str`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

### Returns

- `List[ImageListPageScreenshotsResponseItem]`

  - `file_id: str`

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

  - `image_size: int`

    The size of the image in bytes

  - `page_index: int`

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

  - `metadata: Optional[Dict[str, object]]`

    Metadata for the screenshot

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.images.list_page_screenshots(
    id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(response)
```

#### Response

```json
[
  {
    "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "image_size": 0,
    "page_index": 0,
    "metadata": {
      "foo": "bar"
    }
  }
]
```

## Get File Page Screenshot

`pipelines.images.get_page_screenshot(intpage_index, ImageGetPageScreenshotParams**kwargs)  -> object`

**get** `/api/v1/files/{id}/page_screenshots/{page_index}`

Get screenshot of a page from a file.

### Parameters

- `id: str`

- `page_index: int`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

### Returns

- `object`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.images.get_page_screenshot(
    page_index=0,
    id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(response)
```

#### Response

```json
{}
```

## Get File Page Figure

`pipelines.images.get_page_figure(strfigure_name, ImageGetPageFigureParams**kwargs)  -> object`

**get** `/api/v1/files/{id}/page-figures/{page_index}/{figure_name}`

Get a specific figure from a page of a file.

### Parameters

- `id: str`

- `page_index: int`

- `figure_name: str`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

### Returns

- `object`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.images.get_page_figure(
    figure_name="figure_name",
    id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    page_index=0,
)
print(response)
```

#### Response

```json
{}
```

## List File Pages Figures

`pipelines.images.list_page_figures(strid, ImageListPageFiguresParams**kwargs)  -> ImageListPageFiguresResponse`

**get** `/api/v1/files/{id}/page-figures`

List metadata for all figures from all pages of a file.

### Parameters

- `id: str`

- `organization_id: Optional[str]`

- `project_id: Optional[str]`

### Returns

- `List[ImageListPageFiguresResponseItem]`

  - `confidence: float`

    The confidence of the figure

  - `figure_name: str`

    The name of the figure

  - `figure_size: int`

    The size of the figure in bytes

  - `file_id: str`

    The ID of the file that the figure was taken from

  - `page_index: int`

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

  - `is_likely_noise: Optional[bool]`

    Whether the figure is likely to be noise

  - `metadata: Optional[Dict[str, object]]`

    Metadata for the figure

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.images.list_page_figures(
    id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(response)
```

#### 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

- `List[ImageListPageScreenshotsResponseItem]`

  - `file_id: str`

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

  - `image_size: int`

    The size of the image in bytes

  - `page_index: int`

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

  - `metadata: Optional[Dict[str, object]]`

    Metadata for the screenshot

### Image List Page Figures Response

- `List[ImageListPageFiguresResponseItem]`

  - `confidence: float`

    The confidence of the figure

  - `figure_name: str`

    The name of the figure

  - `figure_size: int`

    The size of the figure in bytes

  - `file_id: str`

    The ID of the file that the figure was taken from

  - `page_index: int`

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

  - `is_likely_noise: Optional[bool]`

    Whether the figure is likely to be noise

  - `metadata: Optional[Dict[str, object]]`

    Metadata for the figure

# Files

## Get Pipeline File Status Counts

`pipelines.files.get_status_counts(strpipeline_id, FileGetStatusCountsParams**kwargs)  -> FileGetStatusCountsResponse`

**get** `/api/v1/pipelines/{pipeline_id}/files/status-counts`

Get files for a pipeline.

### Parameters

- `pipeline_id: str`

- `data_source_id: Optional[str]`

- `only_manually_uploaded: Optional[bool]`

### Returns

- `class FileGetStatusCountsResponse: …`

  - `counts: Dict[str, int]`

    The counts of files by status

  - `total_count: int`

    The total number of files

  - `data_source_id: Optional[str]`

    The ID of the data source that the files belong to

  - `only_manually_uploaded: Optional[bool]`

    Whether to only count manually uploaded files

  - `pipeline_id: Optional[str]`

    The ID of the pipeline that the files belong to

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.files.get_status_counts(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(response.data_source_id)
```

#### 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

`pipelines.files.get_status(strfile_id, FileGetStatusParams**kwargs)  -> ManagedIngestionStatusResponse`

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

Get status of a file for a pipeline.

### Parameters

- `pipeline_id: str`

- `file_id: str`

### Returns

- `class ManagedIngestionStatusResponse: …`

  - `status: Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 3 more]`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: Optional[datetime]`

    Date of the deployment.

  - `effective_at: Optional[datetime]`

    When the status is effective

  - `error: Optional[List[Error]]`

    List of errors that occurred during ingestion.

    - `job_id: str`

      ID of the job that failed.

    - `message: str`

      List of errors that occurred during ingestion.

    - `step: Literal["MANAGED_INGESTION", "DATA_SOURCE", "FILE_UPDATER", 4 more]`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: Optional[str]`

    ID of the latest job.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
managed_ingestion_status_response = client.pipelines.files.get_status(
    file_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(managed_ingestion_status_response.job_id)
```

#### 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

`pipelines.files.create(strpipeline_id, FileCreateParams**kwargs)  -> FileCreateResponse`

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

Add files to a pipeline.

### Parameters

- `pipeline_id: str`

- `body: Iterable[Body]`

  - `file_id: str`

    The ID of the file

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], Iterable[object], str, 3 more]]]`

    Custom metadata for the file

    - `Dict[str, object]`

    - `Iterable[object]`

    - `str`

    - `float`

    - `bool`

### Returns

- `List[PipelineFile]`

  - `id: str`

    Unique identifier for the pipeline file.

  - `pipeline_id: str`

    The ID of the pipeline that the file is associated with.

  - `config_hash: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Hashes for the configuration of the pipeline.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `created_at: Optional[datetime]`

    When the pipeline file was created.

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `data_source_id: Optional[str]`

    The ID of the data source that the file belongs to.

  - `external_file_id: Optional[str]`

    The ID of the file in the external system.

  - `file_id: Optional[str]`

    The ID of the file.

  - `file_size: Optional[int]`

    Size of the file in bytes.

  - `file_type: Optional[str]`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: Optional[int]`

    The number of pages that have been indexed for this file.

  - `last_modified_at: Optional[datetime]`

    The last modified time of the file.

  - `name: Optional[str]`

    Name of the file.

  - `permission_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Permission information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `project_id: Optional[str]`

    The ID of the project that the file belongs to.

  - `resource_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Resource information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `updated_at: Optional[datetime]`

    When the pipeline file was last updated.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline_files = client.pipelines.files.create(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    body=[{
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
    }],
)
print(pipeline_files)
```

#### 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

`pipelines.files.update(strfile_id, FileUpdateParams**kwargs)  -> PipelineFile`

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

Update a file for a pipeline.

### Parameters

- `pipeline_id: str`

- `file_id: str`

- `custom_metadata: Optional[Dict[str, Union[Dict[str, object], Iterable[object], str, 3 more]]]`

  Custom metadata for the file

  - `Dict[str, object]`

  - `Iterable[object]`

  - `str`

  - `float`

  - `bool`

### Returns

- `class PipelineFile: …`

  A file associated with a pipeline.

  - `id: str`

    Unique identifier for the pipeline file.

  - `pipeline_id: str`

    The ID of the pipeline that the file is associated with.

  - `config_hash: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Hashes for the configuration of the pipeline.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `created_at: Optional[datetime]`

    When the pipeline file was created.

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `data_source_id: Optional[str]`

    The ID of the data source that the file belongs to.

  - `external_file_id: Optional[str]`

    The ID of the file in the external system.

  - `file_id: Optional[str]`

    The ID of the file.

  - `file_size: Optional[int]`

    Size of the file in bytes.

  - `file_type: Optional[str]`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: Optional[int]`

    The number of pages that have been indexed for this file.

  - `last_modified_at: Optional[datetime]`

    The last modified time of the file.

  - `name: Optional[str]`

    Name of the file.

  - `permission_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Permission information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `project_id: Optional[str]`

    The ID of the project that the file belongs to.

  - `resource_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Resource information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `updated_at: Optional[datetime]`

    When the pipeline file was last updated.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline_file = client.pipelines.files.update(
    file_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(pipeline_file.id)
```

#### 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

`pipelines.files.delete(strfile_id, FileDeleteParams**kwargs)`

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

Delete a file from a pipeline.

### Parameters

- `pipeline_id: str`

- `file_id: str`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
client.pipelines.files.delete(
    file_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## List Pipeline Files2

`pipelines.files.list(strpipeline_id, FileListParams**kwargs)  -> SyncPaginatedPipelineFiles[PipelineFile]`

**get** `/api/v1/pipelines/{pipeline_id}/files2`

List files for a pipeline with optional filtering, sorting, and pagination.

### Parameters

- `pipeline_id: str`

- `data_source_id: Optional[str]`

- `file_name_contains: Optional[str]`

- `limit: Optional[int]`

- `offset: Optional[int]`

- `only_manually_uploaded: Optional[bool]`

- `order_by: Optional[str]`

- `statuses: Optional[List[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]]`

  Filter by file statuses

  - `"NOT_STARTED"`

  - `"IN_PROGRESS"`

  - `"SUCCESS"`

  - `"ERROR"`

  - `"CANCELLED"`

### Returns

- `class PipelineFile: …`

  A file associated with a pipeline.

  - `id: str`

    Unique identifier for the pipeline file.

  - `pipeline_id: str`

    The ID of the pipeline that the file is associated with.

  - `config_hash: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Hashes for the configuration of the pipeline.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `created_at: Optional[datetime]`

    When the pipeline file was created.

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `data_source_id: Optional[str]`

    The ID of the data source that the file belongs to.

  - `external_file_id: Optional[str]`

    The ID of the file in the external system.

  - `file_id: Optional[str]`

    The ID of the file.

  - `file_size: Optional[int]`

    Size of the file in bytes.

  - `file_type: Optional[str]`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: Optional[int]`

    The number of pages that have been indexed for this file.

  - `last_modified_at: Optional[datetime]`

    The last modified time of the file.

  - `name: Optional[str]`

    Name of the file.

  - `permission_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Permission information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `project_id: Optional[str]`

    The ID of the project that the file belongs to.

  - `resource_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Resource information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `updated_at: Optional[datetime]`

    When the pipeline file was last updated.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
page = client.pipelines.files.list(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
page = page.files[0]
print(page.id)
```

#### 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

- `class PipelineFile: …`

  A file associated with a pipeline.

  - `id: str`

    Unique identifier for the pipeline file.

  - `pipeline_id: str`

    The ID of the pipeline that the file is associated with.

  - `config_hash: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Hashes for the configuration of the pipeline.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `created_at: Optional[datetime]`

    When the pipeline file was created.

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `data_source_id: Optional[str]`

    The ID of the data source that the file belongs to.

  - `external_file_id: Optional[str]`

    The ID of the file in the external system.

  - `file_id: Optional[str]`

    The ID of the file.

  - `file_size: Optional[int]`

    Size of the file in bytes.

  - `file_type: Optional[str]`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: Optional[int]`

    The number of pages that have been indexed for this file.

  - `last_modified_at: Optional[datetime]`

    The last modified time of the file.

  - `name: Optional[str]`

    Name of the file.

  - `permission_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Permission information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `project_id: Optional[str]`

    The ID of the project that the file belongs to.

  - `resource_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Resource information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `updated_at: Optional[datetime]`

    When the pipeline file was last updated.

### File Get Status Counts Response

- `class FileGetStatusCountsResponse: …`

  - `counts: Dict[str, int]`

    The counts of files by status

  - `total_count: int`

    The total number of files

  - `data_source_id: Optional[str]`

    The ID of the data source that the files belong to

  - `only_manually_uploaded: Optional[bool]`

    Whether to only count manually uploaded files

  - `pipeline_id: Optional[str]`

    The ID of the pipeline that the files belong to

### File Create Response

- `List[PipelineFile]`

  - `id: str`

    Unique identifier for the pipeline file.

  - `pipeline_id: str`

    The ID of the pipeline that the file is associated with.

  - `config_hash: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Hashes for the configuration of the pipeline.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `created_at: Optional[datetime]`

    When the pipeline file was created.

  - `custom_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Custom metadata for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `data_source_id: Optional[str]`

    The ID of the data source that the file belongs to.

  - `external_file_id: Optional[str]`

    The ID of the file in the external system.

  - `file_id: Optional[str]`

    The ID of the file.

  - `file_size: Optional[int]`

    Size of the file in bytes.

  - `file_type: Optional[str]`

    File type (e.g. pdf, docx, etc.).

  - `indexed_page_count: Optional[int]`

    The number of pages that have been indexed for this file.

  - `last_modified_at: Optional[datetime]`

    The last modified time of the file.

  - `name: Optional[str]`

    Name of the file.

  - `permission_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Permission information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `project_id: Optional[str]`

    The ID of the project that the file belongs to.

  - `resource_info: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]`

    Resource information for the file.

    - `Dict[str, object]`

    - `List[object]`

    - `str`

    - `float`

    - `bool`

  - `status: Optional[Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 2 more]]`

    Status of the pipeline file.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"CANCELLED"`

  - `status_updated_at: Optional[datetime]`

    The last time the status was updated.

  - `updated_at: Optional[datetime]`

    When the pipeline file was last updated.

# Metadata

## Import Pipeline Metadata

`pipelines.metadata.create(strpipeline_id, MetadataCreateParams**kwargs)  -> MetadataCreateResponse`

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

Import metadata for a pipeline.

### Parameters

- `pipeline_id: str`

- `upload_file: FileTypes`

### Returns

- `Dict[str, str]`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
metadata = client.pipelines.metadata.create(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    upload_file=b"Example data",
)
print(metadata)
```

#### Response

```json
{
  "foo": "string"
}
```

## Delete Pipeline Files Metadata

`pipelines.metadata.delete_all(strpipeline_id)`

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

Delete metadata for all files in a pipeline.

### Parameters

- `pipeline_id: str`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
client.pipelines.metadata.delete_all(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Domain Types

### Metadata Create Response

- `Dict[str, str]`

# Documents

## Create Batch Pipeline Documents

`pipelines.documents.create(strpipeline_id, DocumentCreateParams**kwargs)  -> DocumentCreateResponse`

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

Batch create documents for a pipeline.

### Parameters

- `pipeline_id: str`

- `body: Iterable[CloudDocumentCreateParam]`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `id: Optional[str]`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Returns

- `List[CloudDocument]`

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
cloud_documents = client.pipelines.documents.create(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    body=[{
        "metadata": {
            "foo": "bar"
        },
        "text": "text",
    }],
)
print(cloud_documents)
```

#### 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

`pipelines.documents.list(strpipeline_id, DocumentListParams**kwargs)  -> SyncPaginatedCloudDocuments[CloudDocument]`

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

Return a list of documents for a pipeline.

### Parameters

- `pipeline_id: str`

- `file_id: Optional[str]`

- `limit: Optional[int]`

- `only_api_data_source_documents: Optional[bool]`

- `only_direct_upload: Optional[bool]`

- `skip: Optional[int]`

- `status_refresh_policy: Optional[Literal["cached", "ttl"]]`

  - `"cached"`

  - `"ttl"`

### Returns

- `class CloudDocument: …`

  Cloud document stored in S3.

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
page = client.pipelines.documents.list(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
page = page.documents[0]
print(page.id)
```

#### 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

`pipelines.documents.get(strdocument_id, DocumentGetParams**kwargs)  -> CloudDocument`

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

Return a single document for a pipeline.

### Parameters

- `pipeline_id: str`

- `document_id: str`

### Returns

- `class CloudDocument: …`

  Cloud document stored in S3.

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
cloud_document = client.pipelines.documents.get(
    document_id="document_id",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(cloud_document.id)
```

#### 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

`pipelines.documents.delete(strdocument_id, DocumentDeleteParams**kwargs)`

**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

### Parameters

- `pipeline_id: str`

- `document_id: str`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
client.pipelines.documents.delete(
    document_id="document_id",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Get Pipeline Document Status

`pipelines.documents.get_status(strdocument_id, DocumentGetStatusParams**kwargs)  -> ManagedIngestionStatusResponse`

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

Return a single document for a pipeline.

### Parameters

- `pipeline_id: str`

- `document_id: str`

### Returns

- `class ManagedIngestionStatusResponse: …`

  - `status: Literal["NOT_STARTED", "IN_PROGRESS", "SUCCESS", 3 more]`

    Status of the ingestion.

    - `"NOT_STARTED"`

    - `"IN_PROGRESS"`

    - `"SUCCESS"`

    - `"ERROR"`

    - `"PARTIAL_SUCCESS"`

    - `"CANCELLED"`

  - `deployment_date: Optional[datetime]`

    Date of the deployment.

  - `effective_at: Optional[datetime]`

    When the status is effective

  - `error: Optional[List[Error]]`

    List of errors that occurred during ingestion.

    - `job_id: str`

      ID of the job that failed.

    - `message: str`

      List of errors that occurred during ingestion.

    - `step: Literal["MANAGED_INGESTION", "DATA_SOURCE", "FILE_UPDATER", 4 more]`

      Name of the job that failed.

      - `"MANAGED_INGESTION"`

      - `"DATA_SOURCE"`

      - `"FILE_UPDATER"`

      - `"PARSE"`

      - `"TRANSFORM"`

      - `"INGESTION"`

      - `"METADATA_UPDATE"`

  - `job_id: Optional[str]`

    ID of the latest job.

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
managed_ingestion_status_response = client.pipelines.documents.get_status(
    document_id="document_id",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(managed_ingestion_status_response.job_id)
```

#### 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

`pipelines.documents.sync(strdocument_id, DocumentSyncParams**kwargs)  -> object`

**post** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/sync`

Sync a specific document for a pipeline.

### Parameters

- `pipeline_id: str`

- `document_id: str`

### Returns

- `object`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
response = client.pipelines.documents.sync(
    document_id="document_id",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(response)
```

#### Response

```json
{}
```

## List Pipeline Document Chunks

`pipelines.documents.get_chunks(strdocument_id, DocumentGetChunksParams**kwargs)  -> DocumentGetChunksResponse`

**get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/chunks`

Return a list of chunks for a pipeline document.

### Parameters

- `pipeline_id: str`

- `document_id: str`

### Returns

- `List[TextNode]`

  - `class_name: Optional[str]`

  - `embedding: Optional[List[float]]`

    Embedding of the node.

  - `end_char_idx: Optional[int]`

    End char index of the node.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

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

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    Metadata keys that are excluded from text for the LLM.

  - `extra_info: Optional[Dict[str, object]]`

    A flat dictionary of metadata fields

  - `id: Optional[str]`

    Unique ID of the node.

  - `metadata_seperator: Optional[str]`

    Separator between metadata fields when converting to string.

  - `metadata_template: Optional[str]`

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

  - `mimetype: Optional[str]`

    MIME type of the node content.

  - `relationships: Optional[Dict[str, Relationships]]`

    A mapping of relationships to other node information.

    - `class RelationshipsRelatedNodeInfo: …`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

    - `List[RelationshipsUnionMember1]`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

  - `start_char_idx: Optional[int]`

    Start char index of the node.

  - `text: Optional[str]`

    Text content of the node.

  - `text_template: Optional[str]`

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

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
text_nodes = client.pipelines.documents.get_chunks(
    document_id="document_id",
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(text_nodes)
```

#### 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

`pipelines.documents.upsert(strpipeline_id, DocumentUpsertParams**kwargs)  -> DocumentUpsertResponse`

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

Batch create or update a document for a pipeline.

### Parameters

- `pipeline_id: str`

- `body: Iterable[CloudDocumentCreateParam]`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `id: Optional[str]`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Returns

- `List[CloudDocument]`

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
cloud_documents = client.pipelines.documents.upsert(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    body=[{
        "metadata": {
            "foo": "bar"
        },
        "text": "text",
    }],
)
print(cloud_documents)
```

#### 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

- `class CloudDocument: …`

  Cloud document stored in S3.

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Cloud Document Create

- `class CloudDocumentCreate: …`

  Create a new cloud document.

  - `metadata: Dict[str, object]`

  - `text: str`

  - `id: Optional[str]`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1].

### Text Node

- `class TextNode: …`

  Provided for backward compatibility.

  - `class_name: Optional[str]`

  - `embedding: Optional[List[float]]`

    Embedding of the node.

  - `end_char_idx: Optional[int]`

    End char index of the node.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

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

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    Metadata keys that are excluded from text for the LLM.

  - `extra_info: Optional[Dict[str, object]]`

    A flat dictionary of metadata fields

  - `id: Optional[str]`

    Unique ID of the node.

  - `metadata_seperator: Optional[str]`

    Separator between metadata fields when converting to string.

  - `metadata_template: Optional[str]`

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

  - `mimetype: Optional[str]`

    MIME type of the node content.

  - `relationships: Optional[Dict[str, Relationships]]`

    A mapping of relationships to other node information.

    - `class RelationshipsRelatedNodeInfo: …`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

    - `List[RelationshipsUnionMember1]`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

  - `start_char_idx: Optional[int]`

    Start char index of the node.

  - `text: Optional[str]`

    Text content of the node.

  - `text_template: Optional[str]`

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

### Document Create Response

- `List[CloudDocument]`

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`

### Document Get Chunks Response

- `List[TextNode]`

  - `class_name: Optional[str]`

  - `embedding: Optional[List[float]]`

    Embedding of the node.

  - `end_char_idx: Optional[int]`

    End char index of the node.

  - `excluded_embed_metadata_keys: Optional[List[str]]`

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

  - `excluded_llm_metadata_keys: Optional[List[str]]`

    Metadata keys that are excluded from text for the LLM.

  - `extra_info: Optional[Dict[str, object]]`

    A flat dictionary of metadata fields

  - `id: Optional[str]`

    Unique ID of the node.

  - `metadata_seperator: Optional[str]`

    Separator between metadata fields when converting to string.

  - `metadata_template: Optional[str]`

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

  - `mimetype: Optional[str]`

    MIME type of the node content.

  - `relationships: Optional[Dict[str, Relationships]]`

    A mapping of relationships to other node information.

    - `class RelationshipsRelatedNodeInfo: …`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

    - `List[RelationshipsUnionMember1]`

      - `node_id: str`

      - `class_name: Optional[str]`

      - `hash: Optional[str]`

      - `metadata: Optional[Dict[str, object]]`

      - `node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]`

        - `Literal["1", "2", "3", 2 more]`

          - `"1"`

          - `"2"`

          - `"3"`

          - `"4"`

          - `"5"`

        - `str`

  - `start_char_idx: Optional[int]`

    Start char index of the node.

  - `text: Optional[str]`

    Text content of the node.

  - `text_template: Optional[str]`

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

### Document Upsert Response

- `List[CloudDocument]`

  - `id: str`

  - `metadata: Dict[str, object]`

  - `text: str`

  - `excluded_embed_metadata_keys: Optional[List[str]]`

  - `excluded_llm_metadata_keys: Optional[List[str]]`

  - `page_positions: Optional[List[int]]`

    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[Dict[str, object]]`