# Pipelines ## Search Pipelines `$ llamacloud-prod pipelines list` **get** `/api/v1/pipelines` Search for pipelines by name, type, or project. ### Parameters - `--organization-id: optional string` - `--pipeline-name: optional string` - `--pipeline-type: optional "PLAYGROUND" or "MANAGED"` Enum for representing the type of a pipeline - `--project-id: optional string` - `--project-name: optional string` ### Returns - `Response Search Pipelines Api V1 Pipelines Get: array of Pipeline` - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines list \ --api-key 'My API Key' ``` #### Response ```json [ { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "class_name": "class_name", "embed_batch_size": 1, "model_name": "openai-text-embedding-3-small", "num_workers": 0 }, "type": "MANAGED_OPENAI_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "config_hash": { "embedding_config_hash": "embedding_config_hash", "parsing_config_hash": "parsing_config_hash", "transform_config_hash": "transform_config_hash" }, "created_at": "2019-12-27T18:11:19.117Z", "data_sink": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "component": { "foo": "bar" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "sink_type": "PINECONE", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "additional_kwargs": { "foo": "bar" }, "api_base": "api_base", "api_key": "api_key", "api_version": "api_version", "azure_deployment": "azure_deployment", "azure_endpoint": "azure_endpoint", "class_name": "class_name", "default_headers": { "foo": "string" }, "dimensions": 0, "embed_batch_size": 1, "max_retries": 0, "model_name": "model_name", "num_workers": 0, "reuse_client": true, "timeout": 0 }, "type": "AZURE_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "llama_parse_parameters": { "adaptive_long_table": true, "aggressive_table_extraction": true, "annotate_links": true, "auto_mode": true, "auto_mode_configuration_json": "auto_mode_configuration_json", "auto_mode_trigger_on_image_in_page": true, "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page", "auto_mode_trigger_on_table_in_page": true, "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page", "azure_openai_api_version": "azure_openai_api_version", "azure_openai_deployment_name": "azure_openai_deployment_name", "azure_openai_endpoint": "azure_openai_endpoint", "azure_openai_key": "azure_openai_key", "bbox_bottom": 0, "bbox_left": 0, "bbox_right": 0, "bbox_top": 0, "bounding_box": "bounding_box", "compact_markdown_table": true, "complemental_formatting_instruction": "complemental_formatting_instruction", "content_guideline_instruction": "content_guideline_instruction", "continuous_mode": true, "disable_image_extraction": true, "disable_ocr": true, "disable_reconstruction": true, "do_not_cache": true, "do_not_unroll_columns": true, "enable_cost_optimizer": true, "extract_charts": true, "extract_layout": true, "extract_printed_page_number": true, "fast_mode": true, "formatting_instruction": "formatting_instruction", "gpt4o_api_key": "gpt4o_api_key", "gpt4o_mode": true, "guess_xlsx_sheet_name": true, "hide_footers": true, "hide_headers": true, "high_res_ocr": true, "html_make_all_elements_visible": true, "html_remove_fixed_elements": true, "html_remove_navigation_elements": true, "http_proxy": "http_proxy", "ignore_document_elements_for_layout_detection": true, "images_to_save": [ "screenshot" ], "inline_images_in_markdown": true, "input_s3_path": "input_s3_path", "input_s3_region": "input_s3_region", "input_url": "input_url", "internal_is_screenshot_job": true, "invalidate_cache": true, "is_formatting_instruction": true, "job_timeout_extra_time_per_page_in_seconds": 0, "job_timeout_in_seconds": 0, "keep_page_separator_when_merging_tables": true, "languages": [ "af" ], "layout_aware": true, "line_level_bounding_box": true, "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator", "max_pages": 0, "max_pages_enforced": 0, "merge_tables_across_pages_in_markdown": true, "model": "model", "outlined_table_extraction": true, "output_pdf_of_document": true, "output_s3_path_prefix": "output_s3_path_prefix", "output_s3_region": "output_s3_region", "output_tables_as_HTML": true, "page_error_tolerance": 0, "page_footer_prefix": "page_footer_prefix", "page_footer_suffix": "page_footer_suffix", "page_header_prefix": "page_header_prefix", "page_header_suffix": "page_header_suffix", "page_prefix": "page_prefix", "page_separator": "page_separator", "page_suffix": "page_suffix", "parse_mode": "parse_page_without_llm", "parsing_instruction": "parsing_instruction", "precise_bounding_box": true, "premium_mode": true, "presentation_out_of_bounds_content": true, "presentation_skip_embedded_data": true, "preserve_layout_alignment_across_pages": true, "preserve_very_small_text": true, "preset": "preset", "priority": "low", "project_id": "project_id", "remove_hidden_text": true, "replace_failed_page_mode": "raw_text", "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix", "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix", "save_images": true, "skip_diagonal_text": true, "specialized_chart_parsing_agentic": true, "specialized_chart_parsing_efficient": true, "specialized_chart_parsing_plus": true, "specialized_image_parsing": true, "spreadsheet_extract_sub_tables": true, "spreadsheet_force_formula_computation": true, "spreadsheet_include_hidden_sheets": true, "strict_mode_buggy_font": true, "strict_mode_image_extraction": true, "strict_mode_image_ocr": true, "strict_mode_reconstruction": true, "structured_output": true, "structured_output_json_schema": "structured_output_json_schema", "structured_output_json_schema_name": "structured_output_json_schema_name", "system_prompt": "system_prompt", "system_prompt_append": "system_prompt_append", "take_screenshot": true, "target_pages": "target_pages", "tier": "tier", "use_vendor_multimodal_model": true, "user_prompt": "user_prompt", "vendor_multimodal_api_key": "vendor_multimodal_api_key", "vendor_multimodal_model_name": "vendor_multimodal_model_name", "version": "version", "webhook_configurations": [ { "webhook_events": [ "parse.success", "parse.error" ], "webhook_headers": { "Authorization": "Bearer sk-..." }, "webhook_output_format": "json", "webhook_url": "https://example.com/webhooks/llamacloud" } ], "webhook_url": "webhook_url" }, "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "metadata_config": { "excluded_embed_metadata_keys": [ "string" ], "excluded_llm_metadata_keys": [ "string" ] }, "pipeline_type": "PLAYGROUND", "preset_retrieval_parameters": { "alpha": 0, "class_name": "class_name", "dense_similarity_cutoff": 0, "dense_similarity_top_k": 1, "enable_reranking": true, "files_top_k": 1, "rerank_top_n": 1, "retrieval_mode": "chunks", "retrieve_image_nodes": true, "retrieve_page_figure_nodes": true, "retrieve_page_screenshot_nodes": true, "search_filters": { "filters": [ { "key": "key", "value": 0, "operator": "==" } ], "condition": "and" }, "search_filters_inference_schema": { "foo": { "foo": "bar" } }, "sparse_similarity_top_k": 1 }, "sparse_model_config": { "class_name": "class_name", "model_type": "splade" }, "status": "CREATED", "transform_config": { "chunk_overlap": 0, "chunk_size": 1, "mode": "auto" }, "updated_at": "2019-12-27T18:11:19.117Z" } ] ``` ## Create Pipeline `$ llamacloud-prod pipelines create` **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: string` Body param - `--organization-id: optional string` Query param - `--project-id: optional string` Query param - `--data-sink: optional object { component, name, sink_type }` Body param: Schema for creating a data sink. - `--data-sink-id: optional string` Body param: Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID. - `--embedding-config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` Body param - `--embedding-model-config-id: optional string` Body param: Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID. - `--llama-parse-parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Body param: Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `--managed-pipeline-id: optional string` Body param: The ID of the ManagedPipeline this playground pipeline is linked to. - `--metadata-config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Body param: Metadata configuration for the pipeline. - `--pipeline-type: optional "PLAYGROUND" or "MANAGED"` Body param: Type of pipeline. Either PLAYGROUND or MANAGED. - `--preset-retrieval-parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Body param: Preset retrieval parameters for the pipeline. - `--sparse-model-config: optional object { class_name, model_type }` Body param: 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. - `--status: optional string` Body param: Status of the pipeline deployment. - `--transform-config: optional AutoTransformConfig or AdvancedModeTransformConfig` Body param: Configuration for the transformation. ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines create \ --api-key 'My API Key' \ --name x ``` #### Response ```json { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "class_name": "class_name", "embed_batch_size": 1, "model_name": "openai-text-embedding-3-small", "num_workers": 0 }, "type": "MANAGED_OPENAI_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "config_hash": { "embedding_config_hash": "embedding_config_hash", "parsing_config_hash": "parsing_config_hash", "transform_config_hash": "transform_config_hash" }, "created_at": "2019-12-27T18:11:19.117Z", "data_sink": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "component": { "foo": "bar" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "sink_type": "PINECONE", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "additional_kwargs": { "foo": "bar" }, "api_base": "api_base", "api_key": "api_key", "api_version": "api_version", "azure_deployment": "azure_deployment", "azure_endpoint": "azure_endpoint", "class_name": "class_name", "default_headers": { "foo": "string" }, "dimensions": 0, "embed_batch_size": 1, "max_retries": 0, "model_name": "model_name", "num_workers": 0, "reuse_client": true, "timeout": 0 }, "type": "AZURE_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "llama_parse_parameters": { "adaptive_long_table": true, "aggressive_table_extraction": true, "annotate_links": true, "auto_mode": true, "auto_mode_configuration_json": "auto_mode_configuration_json", "auto_mode_trigger_on_image_in_page": true, "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page", "auto_mode_trigger_on_table_in_page": true, "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page", "azure_openai_api_version": "azure_openai_api_version", "azure_openai_deployment_name": "azure_openai_deployment_name", "azure_openai_endpoint": "azure_openai_endpoint", "azure_openai_key": "azure_openai_key", "bbox_bottom": 0, "bbox_left": 0, "bbox_right": 0, "bbox_top": 0, "bounding_box": "bounding_box", "compact_markdown_table": true, "complemental_formatting_instruction": "complemental_formatting_instruction", "content_guideline_instruction": "content_guideline_instruction", "continuous_mode": true, "disable_image_extraction": true, "disable_ocr": true, "disable_reconstruction": true, "do_not_cache": true, "do_not_unroll_columns": true, "enable_cost_optimizer": true, "extract_charts": true, "extract_layout": true, "extract_printed_page_number": true, "fast_mode": true, "formatting_instruction": "formatting_instruction", "gpt4o_api_key": "gpt4o_api_key", "gpt4o_mode": true, "guess_xlsx_sheet_name": true, "hide_footers": true, "hide_headers": true, "high_res_ocr": true, "html_make_all_elements_visible": true, "html_remove_fixed_elements": true, "html_remove_navigation_elements": true, "http_proxy": "http_proxy", "ignore_document_elements_for_layout_detection": true, "images_to_save": [ "screenshot" ], "inline_images_in_markdown": true, "input_s3_path": "input_s3_path", "input_s3_region": "input_s3_region", "input_url": "input_url", "internal_is_screenshot_job": true, "invalidate_cache": true, "is_formatting_instruction": true, "job_timeout_extra_time_per_page_in_seconds": 0, "job_timeout_in_seconds": 0, "keep_page_separator_when_merging_tables": true, "languages": [ "af" ], "layout_aware": true, "line_level_bounding_box": true, "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator", "max_pages": 0, "max_pages_enforced": 0, "merge_tables_across_pages_in_markdown": true, "model": "model", "outlined_table_extraction": true, "output_pdf_of_document": true, "output_s3_path_prefix": "output_s3_path_prefix", "output_s3_region": "output_s3_region", "output_tables_as_HTML": true, "page_error_tolerance": 0, "page_footer_prefix": "page_footer_prefix", "page_footer_suffix": "page_footer_suffix", "page_header_prefix": "page_header_prefix", "page_header_suffix": "page_header_suffix", "page_prefix": "page_prefix", "page_separator": "page_separator", "page_suffix": "page_suffix", "parse_mode": "parse_page_without_llm", "parsing_instruction": "parsing_instruction", "precise_bounding_box": true, "premium_mode": true, "presentation_out_of_bounds_content": true, "presentation_skip_embedded_data": true, "preserve_layout_alignment_across_pages": true, "preserve_very_small_text": true, "preset": "preset", "priority": "low", "project_id": "project_id", "remove_hidden_text": true, "replace_failed_page_mode": "raw_text", "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix", "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix", "save_images": true, "skip_diagonal_text": true, "specialized_chart_parsing_agentic": true, "specialized_chart_parsing_efficient": true, "specialized_chart_parsing_plus": true, "specialized_image_parsing": true, "spreadsheet_extract_sub_tables": true, "spreadsheet_force_formula_computation": true, "spreadsheet_include_hidden_sheets": true, "strict_mode_buggy_font": true, "strict_mode_image_extraction": true, "strict_mode_image_ocr": true, "strict_mode_reconstruction": true, "structured_output": true, "structured_output_json_schema": "structured_output_json_schema", "structured_output_json_schema_name": "structured_output_json_schema_name", "system_prompt": "system_prompt", "system_prompt_append": "system_prompt_append", "take_screenshot": true, "target_pages": "target_pages", "tier": "tier", "use_vendor_multimodal_model": true, "user_prompt": "user_prompt", "vendor_multimodal_api_key": "vendor_multimodal_api_key", "vendor_multimodal_model_name": "vendor_multimodal_model_name", "version": "version", "webhook_configurations": [ { "webhook_events": [ "parse.success", "parse.error" ], "webhook_headers": { "Authorization": "Bearer sk-..." }, "webhook_output_format": "json", "webhook_url": "https://example.com/webhooks/llamacloud" } ], "webhook_url": "webhook_url" }, "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "metadata_config": { "excluded_embed_metadata_keys": [ "string" ], "excluded_llm_metadata_keys": [ "string" ] }, "pipeline_type": "PLAYGROUND", "preset_retrieval_parameters": { "alpha": 0, "class_name": "class_name", "dense_similarity_cutoff": 0, "dense_similarity_top_k": 1, "enable_reranking": true, "files_top_k": 1, "rerank_top_n": 1, "retrieval_mode": "chunks", "retrieve_image_nodes": true, "retrieve_page_figure_nodes": true, "retrieve_page_screenshot_nodes": true, "search_filters": { "filters": [ { "key": "key", "value": 0, "operator": "==" } ], "condition": "and" }, "search_filters_inference_schema": { "foo": { "foo": "bar" } }, "sparse_similarity_top_k": 1 }, "sparse_model_config": { "class_name": "class_name", "model_type": "splade" }, "status": "CREATED", "transform_config": { "chunk_overlap": 0, "chunk_size": 1, "mode": "auto" }, "updated_at": "2019-12-27T18:11:19.117Z" } ``` ## Get Pipeline `$ llamacloud-prod pipelines get` **get** `/api/v1/pipelines/{pipeline_id}` Get a pipeline by ID. ### Parameters - `--pipeline-id: string` ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines get \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines update` **put** `/api/v1/pipelines/{pipeline_id}` Update an existing pipeline's configuration. ### Parameters - `--pipeline-id: string` - `--data-sink: optional object { component, name, sink_type }` Schema for creating a data sink. - `--data-sink-id: optional string` Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID. - `--embedding-config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` - `--embedding-model-config-id: optional string` Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID. - `--llama-parse-parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `--managed-pipeline-id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `--metadata-config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `--name: optional string` - `--preset-retrieval-parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Schema for the search params for an retrieval execution that can be preset for a pipeline. - `--sparse-model-config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `--status: optional string` Status of the pipeline deployment. - `--transform-config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines update \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines delete` **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: string` ### Example ```cli llamacloud-prod pipelines delete \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` ## Get Pipeline Status `$ llamacloud-prod pipelines get-status` **get** `/api/v1/pipelines/{pipeline_id}/status` Get the ingestion status of a managed pipeline. Returns document counts, sync progress, and the last effective timestamp. Only available for managed pipelines. ### Parameters - `--pipeline-id: string` - `--full-details: optional boolean` ### Returns - `managed_ingestion_status_response: object { status, deployment_date, effective_at, 2 more }` - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more` Status of the ingestion. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"PARTIAL_SUCCESS"` - `"CANCELLED"` - `deployment_date: optional string` Date of the deployment. - `effective_at: optional string` When the status is effective - `error: optional array of object { job_id, message, step }` List of errors that occurred during ingestion. - `job_id: string` ID of the job that failed. - `message: string` List of errors that occurred during ingestion. - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more` Name of the job that failed. - `"MANAGED_INGESTION"` - `"DATA_SOURCE"` - `"FILE_UPDATER"` - `"PARSE"` - `"TRANSFORM"` - `"INGESTION"` - `"METADATA_UPDATE"` - `job_id: optional string` ID of the latest job. ### Example ```cli llamacloud-prod pipelines get-status \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines upsert` **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: string` Body param - `--organization-id: optional string` Query param - `--project-id: optional string` Query param - `--data-sink: optional object { component, name, sink_type }` Body param: Schema for creating a data sink. - `--data-sink-id: optional string` Body param: Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID. - `--embedding-config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` Body param - `--embedding-model-config-id: optional string` Body param: Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID. - `--llama-parse-parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Body param: Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `--managed-pipeline-id: optional string` Body param: The ID of the ManagedPipeline this playground pipeline is linked to. - `--metadata-config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Body param: Metadata configuration for the pipeline. - `--pipeline-type: optional "PLAYGROUND" or "MANAGED"` Body param: Type of pipeline. Either PLAYGROUND or MANAGED. - `--preset-retrieval-parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Body param: Preset retrieval parameters for the pipeline. - `--sparse-model-config: optional object { class_name, model_type }` Body param: 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. - `--status: optional string` Body param: Status of the pipeline deployment. - `--transform-config: optional AutoTransformConfig or AdvancedModeTransformConfig` Body param: Configuration for the transformation. ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines upsert \ --api-key 'My API Key' \ --name x ``` #### Response ```json { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "class_name": "class_name", "embed_batch_size": 1, "model_name": "openai-text-embedding-3-small", "num_workers": 0 }, "type": "MANAGED_OPENAI_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "config_hash": { "embedding_config_hash": "embedding_config_hash", "parsing_config_hash": "parsing_config_hash", "transform_config_hash": "transform_config_hash" }, "created_at": "2019-12-27T18:11:19.117Z", "data_sink": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "component": { "foo": "bar" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "sink_type": "PINECONE", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "additional_kwargs": { "foo": "bar" }, "api_base": "api_base", "api_key": "api_key", "api_version": "api_version", "azure_deployment": "azure_deployment", "azure_endpoint": "azure_endpoint", "class_name": "class_name", "default_headers": { "foo": "string" }, "dimensions": 0, "embed_batch_size": 1, "max_retries": 0, "model_name": "model_name", "num_workers": 0, "reuse_client": true, "timeout": 0 }, "type": "AZURE_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "llama_parse_parameters": { "adaptive_long_table": true, "aggressive_table_extraction": true, "annotate_links": true, "auto_mode": true, "auto_mode_configuration_json": "auto_mode_configuration_json", "auto_mode_trigger_on_image_in_page": true, "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page", "auto_mode_trigger_on_table_in_page": true, "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page", "azure_openai_api_version": "azure_openai_api_version", "azure_openai_deployment_name": "azure_openai_deployment_name", "azure_openai_endpoint": "azure_openai_endpoint", "azure_openai_key": "azure_openai_key", "bbox_bottom": 0, "bbox_left": 0, "bbox_right": 0, "bbox_top": 0, "bounding_box": "bounding_box", "compact_markdown_table": true, "complemental_formatting_instruction": "complemental_formatting_instruction", "content_guideline_instruction": "content_guideline_instruction", "continuous_mode": true, "disable_image_extraction": true, "disable_ocr": true, "disable_reconstruction": true, "do_not_cache": true, "do_not_unroll_columns": true, "enable_cost_optimizer": true, "extract_charts": true, "extract_layout": true, "extract_printed_page_number": true, "fast_mode": true, "formatting_instruction": "formatting_instruction", "gpt4o_api_key": "gpt4o_api_key", "gpt4o_mode": true, "guess_xlsx_sheet_name": true, "hide_footers": true, "hide_headers": true, "high_res_ocr": true, "html_make_all_elements_visible": true, "html_remove_fixed_elements": true, "html_remove_navigation_elements": true, "http_proxy": "http_proxy", "ignore_document_elements_for_layout_detection": true, "images_to_save": [ "screenshot" ], "inline_images_in_markdown": true, "input_s3_path": "input_s3_path", "input_s3_region": "input_s3_region", "input_url": "input_url", "internal_is_screenshot_job": true, "invalidate_cache": true, "is_formatting_instruction": true, "job_timeout_extra_time_per_page_in_seconds": 0, "job_timeout_in_seconds": 0, "keep_page_separator_when_merging_tables": true, "languages": [ "af" ], "layout_aware": true, "line_level_bounding_box": true, "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator", "max_pages": 0, "max_pages_enforced": 0, "merge_tables_across_pages_in_markdown": true, "model": "model", "outlined_table_extraction": true, "output_pdf_of_document": true, "output_s3_path_prefix": "output_s3_path_prefix", "output_s3_region": "output_s3_region", "output_tables_as_HTML": true, "page_error_tolerance": 0, "page_footer_prefix": "page_footer_prefix", "page_footer_suffix": "page_footer_suffix", "page_header_prefix": "page_header_prefix", "page_header_suffix": "page_header_suffix", "page_prefix": "page_prefix", "page_separator": "page_separator", "page_suffix": "page_suffix", "parse_mode": "parse_page_without_llm", "parsing_instruction": "parsing_instruction", "precise_bounding_box": true, "premium_mode": true, "presentation_out_of_bounds_content": true, "presentation_skip_embedded_data": true, "preserve_layout_alignment_across_pages": true, "preserve_very_small_text": true, "preset": "preset", "priority": "low", "project_id": "project_id", "remove_hidden_text": true, "replace_failed_page_mode": "raw_text", "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix", "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix", "save_images": true, "skip_diagonal_text": true, "specialized_chart_parsing_agentic": true, "specialized_chart_parsing_efficient": true, "specialized_chart_parsing_plus": true, "specialized_image_parsing": true, "spreadsheet_extract_sub_tables": true, "spreadsheet_force_formula_computation": true, "spreadsheet_include_hidden_sheets": true, "strict_mode_buggy_font": true, "strict_mode_image_extraction": true, "strict_mode_image_ocr": true, "strict_mode_reconstruction": true, "structured_output": true, "structured_output_json_schema": "structured_output_json_schema", "structured_output_json_schema_name": "structured_output_json_schema_name", "system_prompt": "system_prompt", "system_prompt_append": "system_prompt_append", "take_screenshot": true, "target_pages": "target_pages", "tier": "tier", "use_vendor_multimodal_model": true, "user_prompt": "user_prompt", "vendor_multimodal_api_key": "vendor_multimodal_api_key", "vendor_multimodal_model_name": "vendor_multimodal_model_name", "version": "version", "webhook_configurations": [ { "webhook_events": [ "parse.success", "parse.error" ], "webhook_headers": { "Authorization": "Bearer sk-..." }, "webhook_output_format": "json", "webhook_url": "https://example.com/webhooks/llamacloud" } ], "webhook_url": "webhook_url" }, "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "metadata_config": { "excluded_embed_metadata_keys": [ "string" ], "excluded_llm_metadata_keys": [ "string" ] }, "pipeline_type": "PLAYGROUND", "preset_retrieval_parameters": { "alpha": 0, "class_name": "class_name", "dense_similarity_cutoff": 0, "dense_similarity_top_k": 1, "enable_reranking": true, "files_top_k": 1, "rerank_top_n": 1, "retrieval_mode": "chunks", "retrieve_image_nodes": true, "retrieve_page_figure_nodes": true, "retrieve_page_screenshot_nodes": true, "search_filters": { "filters": [ { "key": "key", "value": 0, "operator": "==" } ], "condition": "and" }, "search_filters_inference_schema": { "foo": { "foo": "bar" } }, "sparse_similarity_top_k": 1 }, "sparse_model_config": { "class_name": "class_name", "model_type": "splade" }, "status": "CREATED", "transform_config": { "chunk_overlap": 0, "chunk_size": 1, "mode": "auto" }, "updated_at": "2019-12-27T18:11:19.117Z" } ``` ## Run Search `$ llamacloud-prod pipelines retrieve` **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: string` Path param - `--query: string` Body param: The query to retrieve against. - `--organization-id: optional string` Query param - `--project-id: optional string` Query param - `--alpha: optional number` Body param: Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `--class-name: optional string` Body param - `--dense-similarity-cutoff: optional number` Body param: Minimum similarity score wrt query for retrieval - `--dense-similarity-top-k: optional number` Body param: Number of nodes for dense retrieval. - `--enable-reranking: optional boolean` Body param: Enable reranking for retrieval - `--files-top-k: optional number` Body param: Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `--rerank-top-n: optional number` Body param: Number of reranked nodes for returning. - `--retrieval-mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` Body param: The retrieval mode for the query. - `--retrieve-image-nodes: optional boolean` Body param: Whether to retrieve image nodes. - `--retrieve-page-figure-nodes: optional boolean` Body param: Whether to retrieve page figure nodes. - `--retrieve-page-screenshot-nodes: optional boolean` Body param: Whether to retrieve page screenshot nodes. - `--search-filters: optional object { filters, condition }` Body param: Metadata filters for vector stores. - `--search-filters-inference-schema: optional map[map[unknown] or array of unknown or string or 2 more]` Body param: JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `--sparse-similarity-top-k: optional number` Body param: Number of nodes for sparse retrieval. ### Returns - `PipelineGetResponse: object { pipeline_id, retrieval_nodes, class_name, 5 more }` Schema for the result of an retrieval execution. - `pipeline_id: string` The ID of the pipeline that the query was retrieved against. - `retrieval_nodes: array of object { node, class_name, score }` The nodes retrieved by the pipeline for the given query. - `node: object { class_name, embedding, end_char_idx, 11 more }` Provided for backward compatibility. - `class_name: optional string` - `embedding: optional array of number` Embedding of the node. - `end_char_idx: optional number` End char index of the node. - `excluded_embed_metadata_keys: optional array of string` Metadata keys that are excluded from text for the embed model. - `excluded_llm_metadata_keys: optional array of string` Metadata keys that are excluded from text for the LLM. - `extra_info: optional map[unknown]` A flat dictionary of metadata fields - `id_: optional string` Unique ID of the node. - `metadata_seperator: optional string` Separator between metadata fields when converting to string. - `metadata_template: optional string` Template for how metadata is formatted, with {key} and {value} placeholders. - `mimetype: optional string` MIME type of the node content. - `relationships: optional map[object { node_id, class_name, hash, 2 more } or array of object { node_id, class_name, hash, 2 more } ]` A mapping of relationships to other node information. - `RelatedNodeInfo: object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `union_member_1: array of object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `start_char_idx: optional number` Start char index of the node. - `text: optional string` Text content of the node. - `text_template: optional string` Template for how text is formatted, with {content} and {metadata_str} placeholders. - `class_name: optional string` - `score: optional number` - `class_name: optional string` - `image_nodes: optional array of PageScreenshotNodeWithScore` The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'. - `node: object { file_id, image_size, page_index, metadata }` - `file_id: string` The ID of the file that the page screenshot was taken from - `image_size: number` The size of the image in bytes - `page_index: number` The index of the page for which the screenshot is taken (0-indexed) - `metadata: optional map[unknown]` Metadata for the screenshot - `score: number` The score of the screenshot node - `class_name: optional string` - `inferred_search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `metadata: optional map[string]` Metadata associated with the retrieval execution - `page_figure_nodes: optional array of PageFigureNodeWithScore` The page figure nodes retrieved by the pipeline for the given query. - `node: object { confidence, figure_name, figure_size, 4 more }` - `confidence: number` The confidence of the figure - `figure_name: string` The name of the figure - `figure_size: number` The size of the figure in bytes - `file_id: string` The ID of the file that the figure was taken from - `page_index: number` The index of the page for which the figure is taken (0-indexed) - `is_likely_noise: optional boolean` Whether the figure is likely to be noise - `metadata: optional map[unknown]` Metadata for the figure - `score: number` The score of the figure node - `class_name: optional string` - `retrieval_latency: optional map[number]` The end-to-end latency for retrieval and reranking. ### Example ```cli llamacloud-prod pipelines retrieve \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --query x ``` #### Response ```json { "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "retrieval_nodes": [ { "node": { "class_name": "class_name", "embedding": [ 0 ], "end_char_idx": 0, "excluded_embed_metadata_keys": [ "string" ], "excluded_llm_metadata_keys": [ "string" ], "extra_info": { "foo": "bar" }, "id_": "id_", "metadata_seperator": "metadata_seperator", "metadata_template": "metadata_template", "mimetype": "mimetype", "relationships": { "foo": { "node_id": "node_id", "class_name": "class_name", "hash": "hash", "metadata": { "foo": "bar" }, "node_type": "1" } }, "start_char_idx": 0, "text": "text", "text_template": "text_template" }, "class_name": "class_name", "score": 0 } ], "class_name": "class_name", "image_nodes": [ { "node": { "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "image_size": 0, "page_index": 0, "metadata": { "foo": "bar" } }, "score": 0, "class_name": "class_name" } ], "inferred_search_filters": { "filters": [ { "key": "key", "value": 0, "operator": "==" } ], "condition": "and" }, "metadata": { "foo": "string" }, "page_figure_nodes": [ { "node": { "confidence": 0, "figure_name": "figure_name", "figure_size": 0, "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "page_index": 0, "is_likely_noise": true, "metadata": { "foo": "bar" } }, "score": 0, "class_name": "class_name" } ], "retrieval_latency": { "foo": 0 } } ``` ## Domain Types ### Advanced Mode Transform Config - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` ### Auto Transform Config - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` ### Azure OpenAI Embedding - `azure_openai_embedding: object { additional_kwargs, api_base, api_key, 12 more }` - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. ### Azure OpenAI Embedding Config - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` ### Bedrock Embedding - `bedrock_embedding: object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. ### Bedrock Embedding Config - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` ### Cohere Embedding - `cohere_embedding: object { api_key, class_name, embed_batch_size, 5 more }` - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE ### Cohere Embedding Config - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` ### Data Sink Create - `data_sink_create: object { component, name, sink_type }` Schema for creating a data sink. - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` ### Gemini Embedding - `gemini_embedding: object { api_base, api_key, class_name, 7 more }` - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. ### Gemini Embedding Config - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` ### Hugging Face Inference API Embedding - `hugging_face_inference_api_embedding: object { token, class_name, cookies, 9 more }` - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. ### Hugging Face Inference API Embedding Config - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` ### Llama Parse Parameters - `llama_parse_parameters: object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` ### Llm Parameters - `llm_parameters: object { class_name, model_name, system_prompt, 3 more }` - `class_name: optional string` - `model_name: optional "GPT_4O" or "GPT_4O_MINI" or "GPT_4_1" or 10 more` The name of the model to use for LLM completions. - `"GPT_4O"` - `"GPT_4O_MINI"` - `"GPT_4_1"` - `"GPT_4_1_NANO"` - `"GPT_4_1_MINI"` - `"AZURE_OPENAI_GPT_4O"` - `"AZURE_OPENAI_GPT_4O_MINI"` - `"AZURE_OPENAI_GPT_4_1"` - `"AZURE_OPENAI_GPT_4_1_MINI"` - `"AZURE_OPENAI_GPT_4_1_NANO"` - `"CLAUDE_4_5_SONNET"` - `"BEDROCK_CLAUDE_3_5_SONNET_V1"` - `"BEDROCK_CLAUDE_3_5_SONNET_V2"` - `system_prompt: optional string` The system prompt to use for the completion. - `temperature: optional number` The temperature value for the model. - `use_chain_of_thought_reasoning: optional boolean` Whether to use chain of thought reasoning. - `use_citation: optional boolean` Whether to show citations in the response. ### Managed Ingestion Status Response - `managed_ingestion_status_response: object { status, deployment_date, effective_at, 2 more }` - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more` Status of the ingestion. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"PARTIAL_SUCCESS"` - `"CANCELLED"` - `deployment_date: optional string` Date of the deployment. - `effective_at: optional string` When the status is effective - `error: optional array of object { job_id, message, step }` List of errors that occurred during ingestion. - `job_id: string` ID of the job that failed. - `message: string` List of errors that occurred during ingestion. - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more` Name of the job that failed. - `"MANAGED_INGESTION"` - `"DATA_SOURCE"` - `"FILE_UPDATER"` - `"PARSE"` - `"TRANSFORM"` - `"INGESTION"` - `"METADATA_UPDATE"` - `job_id: optional string` ID of the latest job. ### Message Role - `message_role: "system" or "developer" or "user" or 5 more` Message role. - `"system"` - `"developer"` - `"user"` - `"assistant"` - `"function"` - `"tool"` - `"chatbot"` - `"model"` ### Metadata Filters - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` ### OpenAI Embedding - `openai_embedding: object { additional_kwargs, api_base, api_key, 10 more }` - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. ### OpenAI Embedding Config - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` ### Page Figure Node With Score - `page_figure_node_with_score: object { node, score, class_name }` Page figure metadata with score - `node: object { confidence, figure_name, figure_size, 4 more }` - `confidence: number` The confidence of the figure - `figure_name: string` The name of the figure - `figure_size: number` The size of the figure in bytes - `file_id: string` The ID of the file that the figure was taken from - `page_index: number` The index of the page for which the figure is taken (0-indexed) - `is_likely_noise: optional boolean` Whether the figure is likely to be noise - `metadata: optional map[unknown]` Metadata for the figure - `score: number` The score of the figure node - `class_name: optional string` ### Page Screenshot Node With Score - `page_screenshot_node_with_score: object { node, score, class_name }` Page screenshot metadata with score - `node: object { file_id, image_size, page_index, metadata }` - `file_id: string` The ID of the file that the page screenshot was taken from - `image_size: number` The size of the image in bytes - `page_index: number` The index of the page for which the screenshot is taken (0-indexed) - `metadata: optional map[unknown]` Metadata for the screenshot - `score: number` The score of the screenshot node - `class_name: optional string` ### Pipeline - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Pipeline Create - `pipeline_create: object { name, data_sink, data_sink_id, 10 more }` Schema for creating a pipeline. - `name: string` - `data_sink: optional object { component, name, sink_type }` Schema for creating a data sink. - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `data_sink_id: optional string` Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID. - `embedding_config: optional AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `embedding_model_config_id: optional string` Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional string` Status of the pipeline deployment. - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` ### Pipeline Metadata Config - `pipeline_metadata_config: object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval ### Pipeline Type - `pipeline_type: "PLAYGROUND" or "MANAGED"` Enum for representing the type of a pipeline - `"PLAYGROUND"` - `"MANAGED"` ### Preset Retrieval Params - `preset_retrieval_params: object { alpha, class_name, dense_similarity_cutoff, 11 more }` Schema for the search params for an retrieval execution that can be preset for a pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. ### Retrieval Mode - `retrieval_mode: "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` ### Sparse Model Config - `sparse_model_config: object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` ### Vertex AI Embedding Config - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` ### Vertex Text Embedding - `vertex_text_embedding: object { client_email, location, private_key, 9 more }` - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. # Sync ## Sync Pipeline `$ llamacloud-prod pipelines:sync create` **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: string` ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines:sync create \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:sync cancel` **post** `/api/v1/pipelines/{pipeline_id}/sync/cancel` Cancel all running sync jobs for a pipeline. ### Parameters - `--pipeline-id: string` ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines:sync cancel \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:data-sources get-data-sources` **get** `/api/v1/pipelines/{pipeline_id}/data-sources` Get data sources for a pipeline. ### Parameters - `--pipeline-id: string` ### Returns - `Response List Pipeline Data Sources Api V1 Pipelines Pipeline Id Data Sources Get: array of PipelineDataSource` - `id: string` Unique identifier - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more` Component that implements the data source - `union_member_0: map[unknown]` - `cloud_s3_data_source: object { bucket, aws_access_id, aws_access_secret, 5 more }` - `bucket: string` The name of the S3 bucket to read from. - `aws_access_id: optional string` The AWS access ID to use for authentication. - `aws_access_secret: optional string` The AWS access secret to use for authentication. - `class_name: optional string` - `prefix: optional string` The prefix of the S3 objects to read from. - `regex_pattern: optional string` The regex pattern to filter S3 objects. Must be a valid regex pattern. - `s3_endpoint_url: optional string` The S3 endpoint URL to use for authentication. - `supports_access_control: optional boolean` - `cloud_az_storage_blob_data_source: object { account_url, container_name, account_key, 8 more }` - `account_url: string` The Azure Storage Blob account URL to use for authentication. - `container_name: string` The name of the Azure Storage Blob container to read from. - `account_key: optional string` The Azure Storage Blob account key to use for authentication. - `account_name: optional string` The Azure Storage Blob account name to use for authentication. - `blob: optional string` The blob name to read from. - `class_name: optional string` - `client_id: optional string` The Azure AD client ID to use for authentication. - `client_secret: optional string` The Azure AD client secret to use for authentication. - `prefix: optional string` The prefix of the Azure Storage Blob objects to read from. - `supports_access_control: optional boolean` - `tenant_id: optional string` The Azure AD tenant ID to use for authentication. - `cloud_google_drive_data_source: object { folder_id, class_name, service_account_key, supports_access_control }` - `folder_id: string` The ID of the Google Drive folder to read from. - `class_name: optional string` - `service_account_key: optional map[string]` A dictionary containing secret values - `supports_access_control: optional boolean` - `cloud_one_drive_data_source: object { client_id, client_secret, tenant_id, 6 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `user_principal_name: string` The user principal name to use for authentication. - `class_name: optional string` - `folder_id: optional string` The ID of the OneDrive folder to read from. - `folder_path: optional string` The path of the OneDrive folder to read from. - `required_exts: optional array of string` The list of required file extensions. - `supports_access_control: optional true` - `true` - `cloud_sharepoint_data_source: object { client_id, client_secret, tenant_id, 11 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `class_name: optional string` - `drive_name: optional string` The name of the Sharepoint drive to read from. - `exclude_path_patterns: optional array of string` List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~'] - `folder_id: optional string` The ID of the Sharepoint folder to read from. - `folder_path: optional string` The path of the Sharepoint folder to read from. - `get_permissions: optional boolean` Whether to get permissions for the sharepoint site. - `include_path_patterns: optional array of string` List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$'] - `required_exts: optional array of string` The list of required file extensions. - `site_id: optional string` The ID of the SharePoint site to download from. - `site_name: optional string` The name of the SharePoint site to download from. - `supports_access_control: optional true` - `true` - `cloud_slack_data_source: object { slack_token, channel_ids, channel_patterns, 6 more }` - `slack_token: string` Slack Bot Token. - `channel_ids: optional string` Slack Channel. - `channel_patterns: optional string` Slack Channel name pattern. - `class_name: optional string` - `earliest_date: optional string` Earliest date. - `earliest_date_timestamp: optional number` Earliest date timestamp. - `latest_date: optional string` Latest date. - `latest_date_timestamp: optional number` Latest date timestamp. - `supports_access_control: optional boolean` - `cloud_notion_page_data_source: object { integration_token, class_name, database_ids, 2 more }` - `integration_token: string` The integration token to use for authentication. - `class_name: optional string` - `database_ids: optional string` The Notion Database Id to read content from. - `page_ids: optional string` The Page ID's of the Notion to read from. - `supports_access_control: optional boolean` - `cloud_confluence_data_source: object { authentication_mechanism, server_url, api_token, 10 more }` - `authentication_mechanism: string` Type of Authentication for connecting to Confluence APIs. - `server_url: string` The server URL of the Confluence instance. - `api_token: optional string` The API token to use for authentication. - `class_name: optional string` - `cql: optional string` The CQL query to use for fetching pages. - `failure_handling: optional object { skip_list_failures }` Configuration for handling failures during processing. Key-value object controlling failure handling behaviors. Example: { "skip_list_failures": true } Currently supports: - skip_list_failures: Skip failed batches/lists and continue processing - `skip_list_failures: optional boolean` Whether to skip failed batches/lists and continue processing - `index_restricted_pages: optional boolean` Whether to index restricted pages. - `keep_markdown_format: optional boolean` Whether to keep the markdown format. - `label: optional string` The label to use for fetching pages. - `page_ids: optional string` The page IDs of the Confluence to read from. - `space_key: optional string` The space key to read from. - `supports_access_control: optional boolean` - `user_name: optional string` The username to use for authentication. - `cloud_jira_data_source: object { authentication_mechanism, query, api_token, 5 more }` Cloud Jira Data Source integrating JiraReader. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `api_token: optional string` The API/ Access Token used for Basic, PAT and OAuth2 authentication. - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `server_url: optional string` The server url for Jira Cloud. - `supports_access_control: optional boolean` - `cloud_jira_data_source_v2: object { authentication_mechanism, query, server_url, 10 more }` Cloud Jira Data Source integrating JiraReaderV2. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `server_url: string` The server url for Jira Cloud. - `api_token: optional string` The API Access Token used for Basic, PAT and OAuth2 authentication. - `api_version: optional "2" or "3"` Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF). - `"2"` - `"3"` - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `expand: optional string` Fields to expand in the response. - `fields: optional array of string` List of fields to retrieve from Jira. If None, retrieves all fields. - `get_permissions: optional boolean` Whether to fetch project role permissions and issue-level security - `requests_per_minute: optional number` Rate limit for Jira API requests per minute. - `supports_access_control: optional boolean` - `cloud_box_data_source: object { authentication_mechanism, class_name, client_id, 6 more }` - `authentication_mechanism: "developer_token" or "ccg"` The type of authentication to use (Developer Token or CCG) - `"developer_token"` - `"ccg"` - `class_name: optional string` - `client_id: optional string` Box API key used for identifying the application the user is authenticating with - `client_secret: optional string` Box API secret used for making auth requests. - `developer_token: optional string` Developer token for authentication if authentication_mechanism is 'developer_token'. - `enterprise_id: optional string` Box Enterprise ID, if provided authenticates as service. - `folder_id: optional string` The ID of the Box folder to read from. - `supports_access_control: optional boolean` - `user_id: optional string` Box User ID, if provided authenticates as user. - `data_source_id: string` The ID of the data source. - `last_synced_at: string` The last time the data source was automatically synced. - `name: string` The name of the data source. - `pipeline_id: string` The ID of the pipeline. - `project_id: string` - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more` - `"S3"` - `"AZURE_STORAGE_BLOB"` - `"GOOGLE_DRIVE"` - `"MICROSOFT_ONEDRIVE"` - `"MICROSOFT_SHAREPOINT"` - `"SLACK"` - `"NOTION_PAGE"` - `"CONFLUENCE"` - `"JIRA"` - `"JIRA_V2"` - `"BOX"` - `created_at: optional string` Creation datetime - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata that will be present on all data loaded from the data source - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` The status of the data source in the pipeline. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `sync_interval: optional number` The interval at which the data source should be synced. - `sync_schedule_set_by: optional string` The id of the user who set the sync schedule. - `updated_at: optional string` Update datetime - `version_metadata: optional object { reader_version }` Version metadata for the data source - `reader_version: optional "1.0" or "2.0" or "2.1"` The version of the reader to use for this data source. - `"1.0"` - `"2.0"` - `"2.1"` ### Example ```cli llamacloud-prod pipelines:data-sources get-data-sources \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:data-sources update-data-sources` **put** `/api/v1/pipelines/{pipeline_id}/data-sources` Add data sources to a pipeline. ### Parameters - `--pipeline-id: string` - `--body: array of object { data_source_id, sync_interval }` ### Returns - `Response Add Data Sources To Pipeline Api V1 Pipelines Pipeline Id Data Sources Put: array of PipelineDataSource` - `id: string` Unique identifier - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more` Component that implements the data source - `union_member_0: map[unknown]` - `cloud_s3_data_source: object { bucket, aws_access_id, aws_access_secret, 5 more }` - `bucket: string` The name of the S3 bucket to read from. - `aws_access_id: optional string` The AWS access ID to use for authentication. - `aws_access_secret: optional string` The AWS access secret to use for authentication. - `class_name: optional string` - `prefix: optional string` The prefix of the S3 objects to read from. - `regex_pattern: optional string` The regex pattern to filter S3 objects. Must be a valid regex pattern. - `s3_endpoint_url: optional string` The S3 endpoint URL to use for authentication. - `supports_access_control: optional boolean` - `cloud_az_storage_blob_data_source: object { account_url, container_name, account_key, 8 more }` - `account_url: string` The Azure Storage Blob account URL to use for authentication. - `container_name: string` The name of the Azure Storage Blob container to read from. - `account_key: optional string` The Azure Storage Blob account key to use for authentication. - `account_name: optional string` The Azure Storage Blob account name to use for authentication. - `blob: optional string` The blob name to read from. - `class_name: optional string` - `client_id: optional string` The Azure AD client ID to use for authentication. - `client_secret: optional string` The Azure AD client secret to use for authentication. - `prefix: optional string` The prefix of the Azure Storage Blob objects to read from. - `supports_access_control: optional boolean` - `tenant_id: optional string` The Azure AD tenant ID to use for authentication. - `cloud_google_drive_data_source: object { folder_id, class_name, service_account_key, supports_access_control }` - `folder_id: string` The ID of the Google Drive folder to read from. - `class_name: optional string` - `service_account_key: optional map[string]` A dictionary containing secret values - `supports_access_control: optional boolean` - `cloud_one_drive_data_source: object { client_id, client_secret, tenant_id, 6 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `user_principal_name: string` The user principal name to use for authentication. - `class_name: optional string` - `folder_id: optional string` The ID of the OneDrive folder to read from. - `folder_path: optional string` The path of the OneDrive folder to read from. - `required_exts: optional array of string` The list of required file extensions. - `supports_access_control: optional true` - `true` - `cloud_sharepoint_data_source: object { client_id, client_secret, tenant_id, 11 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `class_name: optional string` - `drive_name: optional string` The name of the Sharepoint drive to read from. - `exclude_path_patterns: optional array of string` List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~'] - `folder_id: optional string` The ID of the Sharepoint folder to read from. - `folder_path: optional string` The path of the Sharepoint folder to read from. - `get_permissions: optional boolean` Whether to get permissions for the sharepoint site. - `include_path_patterns: optional array of string` List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$'] - `required_exts: optional array of string` The list of required file extensions. - `site_id: optional string` The ID of the SharePoint site to download from. - `site_name: optional string` The name of the SharePoint site to download from. - `supports_access_control: optional true` - `true` - `cloud_slack_data_source: object { slack_token, channel_ids, channel_patterns, 6 more }` - `slack_token: string` Slack Bot Token. - `channel_ids: optional string` Slack Channel. - `channel_patterns: optional string` Slack Channel name pattern. - `class_name: optional string` - `earliest_date: optional string` Earliest date. - `earliest_date_timestamp: optional number` Earliest date timestamp. - `latest_date: optional string` Latest date. - `latest_date_timestamp: optional number` Latest date timestamp. - `supports_access_control: optional boolean` - `cloud_notion_page_data_source: object { integration_token, class_name, database_ids, 2 more }` - `integration_token: string` The integration token to use for authentication. - `class_name: optional string` - `database_ids: optional string` The Notion Database Id to read content from. - `page_ids: optional string` The Page ID's of the Notion to read from. - `supports_access_control: optional boolean` - `cloud_confluence_data_source: object { authentication_mechanism, server_url, api_token, 10 more }` - `authentication_mechanism: string` Type of Authentication for connecting to Confluence APIs. - `server_url: string` The server URL of the Confluence instance. - `api_token: optional string` The API token to use for authentication. - `class_name: optional string` - `cql: optional string` The CQL query to use for fetching pages. - `failure_handling: optional object { skip_list_failures }` Configuration for handling failures during processing. Key-value object controlling failure handling behaviors. Example: { "skip_list_failures": true } Currently supports: - skip_list_failures: Skip failed batches/lists and continue processing - `skip_list_failures: optional boolean` Whether to skip failed batches/lists and continue processing - `index_restricted_pages: optional boolean` Whether to index restricted pages. - `keep_markdown_format: optional boolean` Whether to keep the markdown format. - `label: optional string` The label to use for fetching pages. - `page_ids: optional string` The page IDs of the Confluence to read from. - `space_key: optional string` The space key to read from. - `supports_access_control: optional boolean` - `user_name: optional string` The username to use for authentication. - `cloud_jira_data_source: object { authentication_mechanism, query, api_token, 5 more }` Cloud Jira Data Source integrating JiraReader. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `api_token: optional string` The API/ Access Token used for Basic, PAT and OAuth2 authentication. - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `server_url: optional string` The server url for Jira Cloud. - `supports_access_control: optional boolean` - `cloud_jira_data_source_v2: object { authentication_mechanism, query, server_url, 10 more }` Cloud Jira Data Source integrating JiraReaderV2. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `server_url: string` The server url for Jira Cloud. - `api_token: optional string` The API Access Token used for Basic, PAT and OAuth2 authentication. - `api_version: optional "2" or "3"` Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF). - `"2"` - `"3"` - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `expand: optional string` Fields to expand in the response. - `fields: optional array of string` List of fields to retrieve from Jira. If None, retrieves all fields. - `get_permissions: optional boolean` Whether to fetch project role permissions and issue-level security - `requests_per_minute: optional number` Rate limit for Jira API requests per minute. - `supports_access_control: optional boolean` - `cloud_box_data_source: object { authentication_mechanism, class_name, client_id, 6 more }` - `authentication_mechanism: "developer_token" or "ccg"` The type of authentication to use (Developer Token or CCG) - `"developer_token"` - `"ccg"` - `class_name: optional string` - `client_id: optional string` Box API key used for identifying the application the user is authenticating with - `client_secret: optional string` Box API secret used for making auth requests. - `developer_token: optional string` Developer token for authentication if authentication_mechanism is 'developer_token'. - `enterprise_id: optional string` Box Enterprise ID, if provided authenticates as service. - `folder_id: optional string` The ID of the Box folder to read from. - `supports_access_control: optional boolean` - `user_id: optional string` Box User ID, if provided authenticates as user. - `data_source_id: string` The ID of the data source. - `last_synced_at: string` The last time the data source was automatically synced. - `name: string` The name of the data source. - `pipeline_id: string` The ID of the pipeline. - `project_id: string` - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more` - `"S3"` - `"AZURE_STORAGE_BLOB"` - `"GOOGLE_DRIVE"` - `"MICROSOFT_ONEDRIVE"` - `"MICROSOFT_SHAREPOINT"` - `"SLACK"` - `"NOTION_PAGE"` - `"CONFLUENCE"` - `"JIRA"` - `"JIRA_V2"` - `"BOX"` - `created_at: optional string` Creation datetime - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata that will be present on all data loaded from the data source - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` The status of the data source in the pipeline. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `sync_interval: optional number` The interval at which the data source should be synced. - `sync_schedule_set_by: optional string` The id of the user who set the sync schedule. - `updated_at: optional string` Update datetime - `version_metadata: optional object { reader_version }` Version metadata for the data source - `reader_version: optional "1.0" or "2.0" or "2.1"` The version of the reader to use for this data source. - `"1.0"` - `"2.0"` - `"2.1"` ### Example ```cli llamacloud-prod pipelines:data-sources update-data-sources \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --body '{data_source_id: 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e}' ``` #### 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 `$ llamacloud-prod pipelines:data-sources update` **put** `/api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}` Update the configuration of a data source in a pipeline. ### Parameters - `--pipeline-id: string` Path param - `--data-source-id: string` Path param - `--sync-interval: optional number` Body param: The interval at which the data source should be synced. ### Returns - `pipeline_data_source: object { id, component, data_source_id, 13 more }` Schema for a data source in a pipeline. - `id: string` Unique identifier - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more` Component that implements the data source - `union_member_0: map[unknown]` - `cloud_s3_data_source: object { bucket, aws_access_id, aws_access_secret, 5 more }` - `bucket: string` The name of the S3 bucket to read from. - `aws_access_id: optional string` The AWS access ID to use for authentication. - `aws_access_secret: optional string` The AWS access secret to use for authentication. - `class_name: optional string` - `prefix: optional string` The prefix of the S3 objects to read from. - `regex_pattern: optional string` The regex pattern to filter S3 objects. Must be a valid regex pattern. - `s3_endpoint_url: optional string` The S3 endpoint URL to use for authentication. - `supports_access_control: optional boolean` - `cloud_az_storage_blob_data_source: object { account_url, container_name, account_key, 8 more }` - `account_url: string` The Azure Storage Blob account URL to use for authentication. - `container_name: string` The name of the Azure Storage Blob container to read from. - `account_key: optional string` The Azure Storage Blob account key to use for authentication. - `account_name: optional string` The Azure Storage Blob account name to use for authentication. - `blob: optional string` The blob name to read from. - `class_name: optional string` - `client_id: optional string` The Azure AD client ID to use for authentication. - `client_secret: optional string` The Azure AD client secret to use for authentication. - `prefix: optional string` The prefix of the Azure Storage Blob objects to read from. - `supports_access_control: optional boolean` - `tenant_id: optional string` The Azure AD tenant ID to use for authentication. - `cloud_google_drive_data_source: object { folder_id, class_name, service_account_key, supports_access_control }` - `folder_id: string` The ID of the Google Drive folder to read from. - `class_name: optional string` - `service_account_key: optional map[string]` A dictionary containing secret values - `supports_access_control: optional boolean` - `cloud_one_drive_data_source: object { client_id, client_secret, tenant_id, 6 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `user_principal_name: string` The user principal name to use for authentication. - `class_name: optional string` - `folder_id: optional string` The ID of the OneDrive folder to read from. - `folder_path: optional string` The path of the OneDrive folder to read from. - `required_exts: optional array of string` The list of required file extensions. - `supports_access_control: optional true` - `true` - `cloud_sharepoint_data_source: object { client_id, client_secret, tenant_id, 11 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `class_name: optional string` - `drive_name: optional string` The name of the Sharepoint drive to read from. - `exclude_path_patterns: optional array of string` List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~'] - `folder_id: optional string` The ID of the Sharepoint folder to read from. - `folder_path: optional string` The path of the Sharepoint folder to read from. - `get_permissions: optional boolean` Whether to get permissions for the sharepoint site. - `include_path_patterns: optional array of string` List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$'] - `required_exts: optional array of string` The list of required file extensions. - `site_id: optional string` The ID of the SharePoint site to download from. - `site_name: optional string` The name of the SharePoint site to download from. - `supports_access_control: optional true` - `true` - `cloud_slack_data_source: object { slack_token, channel_ids, channel_patterns, 6 more }` - `slack_token: string` Slack Bot Token. - `channel_ids: optional string` Slack Channel. - `channel_patterns: optional string` Slack Channel name pattern. - `class_name: optional string` - `earliest_date: optional string` Earliest date. - `earliest_date_timestamp: optional number` Earliest date timestamp. - `latest_date: optional string` Latest date. - `latest_date_timestamp: optional number` Latest date timestamp. - `supports_access_control: optional boolean` - `cloud_notion_page_data_source: object { integration_token, class_name, database_ids, 2 more }` - `integration_token: string` The integration token to use for authentication. - `class_name: optional string` - `database_ids: optional string` The Notion Database Id to read content from. - `page_ids: optional string` The Page ID's of the Notion to read from. - `supports_access_control: optional boolean` - `cloud_confluence_data_source: object { authentication_mechanism, server_url, api_token, 10 more }` - `authentication_mechanism: string` Type of Authentication for connecting to Confluence APIs. - `server_url: string` The server URL of the Confluence instance. - `api_token: optional string` The API token to use for authentication. - `class_name: optional string` - `cql: optional string` The CQL query to use for fetching pages. - `failure_handling: optional object { skip_list_failures }` Configuration for handling failures during processing. Key-value object controlling failure handling behaviors. Example: { "skip_list_failures": true } Currently supports: - skip_list_failures: Skip failed batches/lists and continue processing - `skip_list_failures: optional boolean` Whether to skip failed batches/lists and continue processing - `index_restricted_pages: optional boolean` Whether to index restricted pages. - `keep_markdown_format: optional boolean` Whether to keep the markdown format. - `label: optional string` The label to use for fetching pages. - `page_ids: optional string` The page IDs of the Confluence to read from. - `space_key: optional string` The space key to read from. - `supports_access_control: optional boolean` - `user_name: optional string` The username to use for authentication. - `cloud_jira_data_source: object { authentication_mechanism, query, api_token, 5 more }` Cloud Jira Data Source integrating JiraReader. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `api_token: optional string` The API/ Access Token used for Basic, PAT and OAuth2 authentication. - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `server_url: optional string` The server url for Jira Cloud. - `supports_access_control: optional boolean` - `cloud_jira_data_source_v2: object { authentication_mechanism, query, server_url, 10 more }` Cloud Jira Data Source integrating JiraReaderV2. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `server_url: string` The server url for Jira Cloud. - `api_token: optional string` The API Access Token used for Basic, PAT and OAuth2 authentication. - `api_version: optional "2" or "3"` Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF). - `"2"` - `"3"` - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `expand: optional string` Fields to expand in the response. - `fields: optional array of string` List of fields to retrieve from Jira. If None, retrieves all fields. - `get_permissions: optional boolean` Whether to fetch project role permissions and issue-level security - `requests_per_minute: optional number` Rate limit for Jira API requests per minute. - `supports_access_control: optional boolean` - `cloud_box_data_source: object { authentication_mechanism, class_name, client_id, 6 more }` - `authentication_mechanism: "developer_token" or "ccg"` The type of authentication to use (Developer Token or CCG) - `"developer_token"` - `"ccg"` - `class_name: optional string` - `client_id: optional string` Box API key used for identifying the application the user is authenticating with - `client_secret: optional string` Box API secret used for making auth requests. - `developer_token: optional string` Developer token for authentication if authentication_mechanism is 'developer_token'. - `enterprise_id: optional string` Box Enterprise ID, if provided authenticates as service. - `folder_id: optional string` The ID of the Box folder to read from. - `supports_access_control: optional boolean` - `user_id: optional string` Box User ID, if provided authenticates as user. - `data_source_id: string` The ID of the data source. - `last_synced_at: string` The last time the data source was automatically synced. - `name: string` The name of the data source. - `pipeline_id: string` The ID of the pipeline. - `project_id: string` - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more` - `"S3"` - `"AZURE_STORAGE_BLOB"` - `"GOOGLE_DRIVE"` - `"MICROSOFT_ONEDRIVE"` - `"MICROSOFT_SHAREPOINT"` - `"SLACK"` - `"NOTION_PAGE"` - `"CONFLUENCE"` - `"JIRA"` - `"JIRA_V2"` - `"BOX"` - `created_at: optional string` Creation datetime - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata that will be present on all data loaded from the data source - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` The status of the data source in the pipeline. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `sync_interval: optional number` The interval at which the data source should be synced. - `sync_schedule_set_by: optional string` The id of the user who set the sync schedule. - `updated_at: optional string` Update datetime - `version_metadata: optional object { reader_version }` Version metadata for the data source - `reader_version: optional "1.0" or "2.0" or "2.1"` The version of the reader to use for this data source. - `"1.0"` - `"2.0"` - `"2.1"` ### Example ```cli llamacloud-prod pipelines:data-sources update \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --data-source-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:data-sources get-status` **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: string` - `--data-source-id: string` ### Returns - `managed_ingestion_status_response: object { status, deployment_date, effective_at, 2 more }` - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more` Status of the ingestion. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"PARTIAL_SUCCESS"` - `"CANCELLED"` - `deployment_date: optional string` Date of the deployment. - `effective_at: optional string` When the status is effective - `error: optional array of object { job_id, message, step }` List of errors that occurred during ingestion. - `job_id: string` ID of the job that failed. - `message: string` List of errors that occurred during ingestion. - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more` Name of the job that failed. - `"MANAGED_INGESTION"` - `"DATA_SOURCE"` - `"FILE_UPDATER"` - `"PARSE"` - `"TRANSFORM"` - `"INGESTION"` - `"METADATA_UPDATE"` - `job_id: optional string` ID of the latest job. ### Example ```cli llamacloud-prod pipelines:data-sources get-status \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --data-source-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:data-sources sync` **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: string` Path param - `--data-source-id: string` Path param - `--pipeline-file-id: optional array of string` Body param ### Returns - `pipeline: object { id, embedding_config, name, 15 more }` Schema for a pipeline. - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines:data-sources sync \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --data-source-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 - `pipeline_data_source: object { id, component, data_source_id, 13 more }` Schema for a data source in a pipeline. - `id: string` Unique identifier - `component: map[unknown] or CloudS3DataSource or CloudAzStorageBlobDataSource or 9 more` Component that implements the data source - `union_member_0: map[unknown]` - `cloud_s3_data_source: object { bucket, aws_access_id, aws_access_secret, 5 more }` - `bucket: string` The name of the S3 bucket to read from. - `aws_access_id: optional string` The AWS access ID to use for authentication. - `aws_access_secret: optional string` The AWS access secret to use for authentication. - `class_name: optional string` - `prefix: optional string` The prefix of the S3 objects to read from. - `regex_pattern: optional string` The regex pattern to filter S3 objects. Must be a valid regex pattern. - `s3_endpoint_url: optional string` The S3 endpoint URL to use for authentication. - `supports_access_control: optional boolean` - `cloud_az_storage_blob_data_source: object { account_url, container_name, account_key, 8 more }` - `account_url: string` The Azure Storage Blob account URL to use for authentication. - `container_name: string` The name of the Azure Storage Blob container to read from. - `account_key: optional string` The Azure Storage Blob account key to use for authentication. - `account_name: optional string` The Azure Storage Blob account name to use for authentication. - `blob: optional string` The blob name to read from. - `class_name: optional string` - `client_id: optional string` The Azure AD client ID to use for authentication. - `client_secret: optional string` The Azure AD client secret to use for authentication. - `prefix: optional string` The prefix of the Azure Storage Blob objects to read from. - `supports_access_control: optional boolean` - `tenant_id: optional string` The Azure AD tenant ID to use for authentication. - `cloud_google_drive_data_source: object { folder_id, class_name, service_account_key, supports_access_control }` - `folder_id: string` The ID of the Google Drive folder to read from. - `class_name: optional string` - `service_account_key: optional map[string]` A dictionary containing secret values - `supports_access_control: optional boolean` - `cloud_one_drive_data_source: object { client_id, client_secret, tenant_id, 6 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `user_principal_name: string` The user principal name to use for authentication. - `class_name: optional string` - `folder_id: optional string` The ID of the OneDrive folder to read from. - `folder_path: optional string` The path of the OneDrive folder to read from. - `required_exts: optional array of string` The list of required file extensions. - `supports_access_control: optional true` - `true` - `cloud_sharepoint_data_source: object { client_id, client_secret, tenant_id, 11 more }` - `client_id: string` The client ID to use for authentication. - `client_secret: string` The client secret to use for authentication. - `tenant_id: string` The tenant ID to use for authentication. - `class_name: optional string` - `drive_name: optional string` The name of the Sharepoint drive to read from. - `exclude_path_patterns: optional array of string` List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: ['/temp/', '/backup/', '.git/', '.tmp$', '^~'] - `folder_id: optional string` The ID of the Sharepoint folder to read from. - `folder_path: optional string` The path of the Sharepoint folder to read from. - `get_permissions: optional boolean` Whether to get permissions for the sharepoint site. - `include_path_patterns: optional array of string` List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/.*.pdf$', '^Report.*.pdf$'] - `required_exts: optional array of string` The list of required file extensions. - `site_id: optional string` The ID of the SharePoint site to download from. - `site_name: optional string` The name of the SharePoint site to download from. - `supports_access_control: optional true` - `true` - `cloud_slack_data_source: object { slack_token, channel_ids, channel_patterns, 6 more }` - `slack_token: string` Slack Bot Token. - `channel_ids: optional string` Slack Channel. - `channel_patterns: optional string` Slack Channel name pattern. - `class_name: optional string` - `earliest_date: optional string` Earliest date. - `earliest_date_timestamp: optional number` Earliest date timestamp. - `latest_date: optional string` Latest date. - `latest_date_timestamp: optional number` Latest date timestamp. - `supports_access_control: optional boolean` - `cloud_notion_page_data_source: object { integration_token, class_name, database_ids, 2 more }` - `integration_token: string` The integration token to use for authentication. - `class_name: optional string` - `database_ids: optional string` The Notion Database Id to read content from. - `page_ids: optional string` The Page ID's of the Notion to read from. - `supports_access_control: optional boolean` - `cloud_confluence_data_source: object { authentication_mechanism, server_url, api_token, 10 more }` - `authentication_mechanism: string` Type of Authentication for connecting to Confluence APIs. - `server_url: string` The server URL of the Confluence instance. - `api_token: optional string` The API token to use for authentication. - `class_name: optional string` - `cql: optional string` The CQL query to use for fetching pages. - `failure_handling: optional object { skip_list_failures }` Configuration for handling failures during processing. Key-value object controlling failure handling behaviors. Example: { "skip_list_failures": true } Currently supports: - skip_list_failures: Skip failed batches/lists and continue processing - `skip_list_failures: optional boolean` Whether to skip failed batches/lists and continue processing - `index_restricted_pages: optional boolean` Whether to index restricted pages. - `keep_markdown_format: optional boolean` Whether to keep the markdown format. - `label: optional string` The label to use for fetching pages. - `page_ids: optional string` The page IDs of the Confluence to read from. - `space_key: optional string` The space key to read from. - `supports_access_control: optional boolean` - `user_name: optional string` The username to use for authentication. - `cloud_jira_data_source: object { authentication_mechanism, query, api_token, 5 more }` Cloud Jira Data Source integrating JiraReader. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `api_token: optional string` The API/ Access Token used for Basic, PAT and OAuth2 authentication. - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `server_url: optional string` The server url for Jira Cloud. - `supports_access_control: optional boolean` - `cloud_jira_data_source_v2: object { authentication_mechanism, query, server_url, 10 more }` Cloud Jira Data Source integrating JiraReaderV2. - `authentication_mechanism: string` Type of Authentication for connecting to Jira APIs. - `query: string` JQL (Jira Query Language) query to search. - `server_url: string` The server url for Jira Cloud. - `api_token: optional string` The API Access Token used for Basic, PAT and OAuth2 authentication. - `api_version: optional "2" or "3"` Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF). - `"2"` - `"3"` - `class_name: optional string` - `cloud_id: optional string` The cloud ID, used in case of OAuth2. - `email: optional string` The email address to use for authentication. - `expand: optional string` Fields to expand in the response. - `fields: optional array of string` List of fields to retrieve from Jira. If None, retrieves all fields. - `get_permissions: optional boolean` Whether to fetch project role permissions and issue-level security - `requests_per_minute: optional number` Rate limit for Jira API requests per minute. - `supports_access_control: optional boolean` - `cloud_box_data_source: object { authentication_mechanism, class_name, client_id, 6 more }` - `authentication_mechanism: "developer_token" or "ccg"` The type of authentication to use (Developer Token or CCG) - `"developer_token"` - `"ccg"` - `class_name: optional string` - `client_id: optional string` Box API key used for identifying the application the user is authenticating with - `client_secret: optional string` Box API secret used for making auth requests. - `developer_token: optional string` Developer token for authentication if authentication_mechanism is 'developer_token'. - `enterprise_id: optional string` Box Enterprise ID, if provided authenticates as service. - `folder_id: optional string` The ID of the Box folder to read from. - `supports_access_control: optional boolean` - `user_id: optional string` Box User ID, if provided authenticates as user. - `data_source_id: string` The ID of the data source. - `last_synced_at: string` The last time the data source was automatically synced. - `name: string` The name of the data source. - `pipeline_id: string` The ID of the pipeline. - `project_id: string` - `source_type: "S3" or "AZURE_STORAGE_BLOB" or "GOOGLE_DRIVE" or 8 more` - `"S3"` - `"AZURE_STORAGE_BLOB"` - `"GOOGLE_DRIVE"` - `"MICROSOFT_ONEDRIVE"` - `"MICROSOFT_SHAREPOINT"` - `"SLACK"` - `"NOTION_PAGE"` - `"CONFLUENCE"` - `"JIRA"` - `"JIRA_V2"` - `"BOX"` - `created_at: optional string` Creation datetime - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata that will be present on all data loaded from the data source - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` The status of the data source in the pipeline. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `sync_interval: optional number` The interval at which the data source should be synced. - `sync_schedule_set_by: optional string` The id of the user who set the sync schedule. - `updated_at: optional string` Update datetime - `version_metadata: optional object { reader_version }` Version metadata for the data source - `reader_version: optional "1.0" or "2.0" or "2.1"` The version of the reader to use for this data source. - `"1.0"` - `"2.0"` - `"2.1"` # Images ## List File Page Screenshots `$ llamacloud-prod pipelines:images list-page-screenshots` **get** `/api/v1/files/{id}/page_screenshots` List metadata for all screenshots of pages from a file. ### Parameters - `--id: string` - `--organization-id: optional string` - `--project-id: optional string` ### Returns - `Response List File Page Screenshots Api V1 Files Id Page Screenshots Get: array of object { file_id, image_size, page_index, metadata }` - `file_id: string` The ID of the file that the page screenshot was taken from - `image_size: number` The size of the image in bytes - `page_index: number` The index of the page for which the screenshot is taken (0-indexed) - `metadata: optional map[unknown]` Metadata for the screenshot ### Example ```cli llamacloud-prod pipelines:images list-page-screenshots \ --api-key 'My API Key' \ --id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### Response ```json [ { "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "image_size": 0, "page_index": 0, "metadata": { "foo": "bar" } } ] ``` ## Get File Page Screenshot `$ llamacloud-prod pipelines:images get-page-screenshot` **get** `/api/v1/files/{id}/page_screenshots/{page_index}` Get screenshot of a page from a file. ### Parameters - `--id: string` Path param - `--page-index: number` Path param - `--organization-id: optional string` Query param - `--project-id: optional string` Query param ### Returns - `PipelineImageGetPageScreenshotResponse: unknown` ### Example ```cli llamacloud-prod pipelines:images get-page-screenshot \ --api-key 'My API Key' \ --id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --page-index 0 ``` #### Response ```json {} ``` ## Get File Page Figure `$ llamacloud-prod pipelines:images get-page-figure` **get** `/api/v1/files/{id}/page-figures/{page_index}/{figure_name}` Get a specific figure from a page of a file. ### Parameters - `--id: string` Path param - `--page-index: number` Path param - `--figure-name: string` Path param - `--organization-id: optional string` Query param - `--project-id: optional string` Query param ### Returns - `PipelineImageGetPageFigureResponse: unknown` ### Example ```cli llamacloud-prod pipelines:images get-page-figure \ --api-key 'My API Key' \ --id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --page-index 0 \ --figure-name figure_name ``` #### Response ```json {} ``` ## List File Pages Figures `$ llamacloud-prod pipelines:images list-page-figures` **get** `/api/v1/files/{id}/page-figures` List metadata for all figures from all pages of a file. ### Parameters - `--id: string` - `--organization-id: optional string` - `--project-id: optional string` ### Returns - `Response List File Pages Figures Api V1 Files Id Page Figures Get: array of object { confidence, figure_name, figure_size, 4 more }` - `confidence: number` The confidence of the figure - `figure_name: string` The name of the figure - `figure_size: number` The size of the figure in bytes - `file_id: string` The ID of the file that the figure was taken from - `page_index: number` The index of the page for which the figure is taken (0-indexed) - `is_likely_noise: optional boolean` Whether the figure is likely to be noise - `metadata: optional map[unknown]` Metadata for the figure ### Example ```cli llamacloud-prod pipelines:images list-page-figures \ --api-key 'My API Key' \ --id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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" } } ] ``` # Files ## Get Pipeline File Status Counts `$ llamacloud-prod pipelines:files get-status-counts` **get** `/api/v1/pipelines/{pipeline_id}/files/status-counts` Get files for a pipeline. ### Parameters - `--pipeline-id: string` - `--data-source-id: optional string` - `--only-manually-uploaded: optional boolean` ### Returns - `PipelineFileGetStatusCountsResponse: object { counts, total_count, data_source_id, 2 more }` - `counts: map[number]` The counts of files by status - `total_count: number` The total number of files - `data_source_id: optional string` The ID of the data source that the files belong to - `only_manually_uploaded: optional boolean` Whether to only count manually uploaded files - `pipeline_id: optional string` The ID of the pipeline that the files belong to ### Example ```cli llamacloud-prod pipelines:files get-status-counts \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:files get-status` **get** `/api/v1/pipelines/{pipeline_id}/files/{file_id}/status` Get status of a file for a pipeline. ### Parameters - `--pipeline-id: string` - `--file-id: string` ### Returns - `managed_ingestion_status_response: object { status, deployment_date, effective_at, 2 more }` - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more` Status of the ingestion. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"PARTIAL_SUCCESS"` - `"CANCELLED"` - `deployment_date: optional string` Date of the deployment. - `effective_at: optional string` When the status is effective - `error: optional array of object { job_id, message, step }` List of errors that occurred during ingestion. - `job_id: string` ID of the job that failed. - `message: string` List of errors that occurred during ingestion. - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more` Name of the job that failed. - `"MANAGED_INGESTION"` - `"DATA_SOURCE"` - `"FILE_UPDATER"` - `"PARSE"` - `"TRANSFORM"` - `"INGESTION"` - `"METADATA_UPDATE"` - `job_id: optional string` ID of the latest job. ### Example ```cli llamacloud-prod pipelines:files get-status \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --file-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:files create` **put** `/api/v1/pipelines/{pipeline_id}/files` Add files to a pipeline. ### Parameters - `--pipeline-id: string` - `--body: array of object { file_id, custom_metadata }` ### Returns - `Response Add Files To Pipeline Api Api V1 Pipelines Pipeline Id Files Put: array of PipelineFile` - `id: string` Unique identifier for the pipeline file. - `pipeline_id: string` The ID of the pipeline that the file is associated with. - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]` Hashes for the configuration of the pipeline. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `created_at: optional string` When the pipeline file was created. - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `data_source_id: optional string` The ID of the data source that the file belongs to. - `external_file_id: optional string` The ID of the file in the external system. - `file_id: optional string` The ID of the file. - `file_size: optional number` Size of the file in bytes. - `file_type: optional string` File type (e.g. pdf, docx, etc.). - `indexed_page_count: optional number` The number of pages that have been indexed for this file. - `last_modified_at: optional string` The last modified time of the file. - `name: optional string` Name of the file. - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]` Permission information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `project_id: optional string` The ID of the project that the file belongs to. - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]` Resource information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` Status of the pipeline file. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `updated_at: optional string` When the pipeline file was last updated. ### Example ```cli llamacloud-prod pipelines:files create \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --body '{file_id: 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e}' ``` #### 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 `$ llamacloud-prod pipelines:files update` **put** `/api/v1/pipelines/{pipeline_id}/files/{file_id}` Update a file for a pipeline. ### Parameters - `--pipeline-id: string` Path param - `--file-id: string` Path param - `--custom-metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Body param: Custom metadata for the file ### Returns - `pipeline_file: object { id, pipeline_id, config_hash, 16 more }` A file associated with a pipeline. - `id: string` Unique identifier for the pipeline file. - `pipeline_id: string` The ID of the pipeline that the file is associated with. - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]` Hashes for the configuration of the pipeline. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `created_at: optional string` When the pipeline file was created. - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `data_source_id: optional string` The ID of the data source that the file belongs to. - `external_file_id: optional string` The ID of the file in the external system. - `file_id: optional string` The ID of the file. - `file_size: optional number` Size of the file in bytes. - `file_type: optional string` File type (e.g. pdf, docx, etc.). - `indexed_page_count: optional number` The number of pages that have been indexed for this file. - `last_modified_at: optional string` The last modified time of the file. - `name: optional string` Name of the file. - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]` Permission information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `project_id: optional string` The ID of the project that the file belongs to. - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]` Resource information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` Status of the pipeline file. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `updated_at: optional string` When the pipeline file was last updated. ### Example ```cli llamacloud-prod pipelines:files update \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --file-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:files delete` **delete** `/api/v1/pipelines/{pipeline_id}/files/{file_id}` Delete a file from a pipeline. ### Parameters - `--pipeline-id: string` - `--file-id: string` ### Example ```cli llamacloud-prod pipelines:files delete \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --file-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` ## List Pipeline Files2 `$ llamacloud-prod pipelines:files list` **get** `/api/v1/pipelines/{pipeline_id}/files2` List files for a pipeline with optional filtering, sorting, and pagination. ### Parameters - `--pipeline-id: string` - `--data-source-id: optional string` - `--file-name-contains: optional string` - `--limit: optional number` - `--offset: optional number` - `--only-manually-uploaded: optional boolean` - `--order-by: optional string` - `--status: optional array of "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` Filter by file statuses ### Returns - `PaginatedListPipelineFilesResponse: object { files, limit, offset, total_count }` Paginated list of pipeline files. - `files: array of PipelineFile` The files to list - `id: string` Unique identifier for the pipeline file. - `pipeline_id: string` The ID of the pipeline that the file is associated with. - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]` Hashes for the configuration of the pipeline. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `created_at: optional string` When the pipeline file was created. - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `data_source_id: optional string` The ID of the data source that the file belongs to. - `external_file_id: optional string` The ID of the file in the external system. - `file_id: optional string` The ID of the file. - `file_size: optional number` Size of the file in bytes. - `file_type: optional string` File type (e.g. pdf, docx, etc.). - `indexed_page_count: optional number` The number of pages that have been indexed for this file. - `last_modified_at: optional string` The last modified time of the file. - `name: optional string` Name of the file. - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]` Permission information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `project_id: optional string` The ID of the project that the file belongs to. - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]` Resource information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` Status of the pipeline file. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `updated_at: optional string` When the pipeline file was last updated. - `limit: number` The limit of the files - `offset: number` The offset of the files - `total_count: number` The total number of files ### Example ```cli llamacloud-prod pipelines:files list \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 - `pipeline_file: object { id, pipeline_id, config_hash, 16 more }` A file associated with a pipeline. - `id: string` Unique identifier for the pipeline file. - `pipeline_id: string` The ID of the pipeline that the file is associated with. - `config_hash: optional map[map[unknown] or array of unknown or string or 2 more]` Hashes for the configuration of the pipeline. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `created_at: optional string` When the pipeline file was created. - `custom_metadata: optional map[map[unknown] or array of unknown or string or 2 more]` Custom metadata for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `data_source_id: optional string` The ID of the data source that the file belongs to. - `external_file_id: optional string` The ID of the file in the external system. - `file_id: optional string` The ID of the file. - `file_size: optional number` Size of the file in bytes. - `file_type: optional string` File type (e.g. pdf, docx, etc.). - `indexed_page_count: optional number` The number of pages that have been indexed for this file. - `last_modified_at: optional string` The last modified time of the file. - `name: optional string` Name of the file. - `permission_info: optional map[map[unknown] or array of unknown or string or 2 more]` Permission information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `project_id: optional string` The ID of the project that the file belongs to. - `resource_info: optional map[map[unknown] or array of unknown or string or 2 more]` Resource information for the file. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `status: optional "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 2 more` Status of the pipeline file. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"CANCELLED"` - `status_updated_at: optional string` The last time the status was updated. - `updated_at: optional string` When the pipeline file was last updated. # Metadata ## Import Pipeline Metadata `$ llamacloud-prod pipelines:metadata create` **put** `/api/v1/pipelines/{pipeline_id}/metadata` Import metadata for a pipeline. ### Parameters - `--pipeline-id: string` - `--upload-file: string` ### Returns - `PipelineMetadataNewResponse: map[string]` ### Example ```cli llamacloud-prod pipelines:metadata create \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --upload-file 'Example data' ``` #### Response ```json { "foo": "string" } ``` ## Delete Pipeline Files Metadata `$ llamacloud-prod pipelines:metadata delete-all` **delete** `/api/v1/pipelines/{pipeline_id}/metadata` Delete metadata for all files in a pipeline. ### Parameters - `--pipeline-id: string` ### Example ```cli llamacloud-prod pipelines:metadata delete-all \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` # Documents ## Create Batch Pipeline Documents `$ llamacloud-prod pipelines:documents create` **post** `/api/v1/pipelines/{pipeline_id}/documents` Batch create documents for a pipeline. ### Parameters - `--pipeline-id: string` - `--body: array of CloudDocumentCreate` ### Returns - `Response Create Batch Pipeline Documents Api V1 Pipelines Pipeline Id Documents Post: array of CloudDocument` - `id: string` - `metadata: map[unknown]` - `text: string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. - `status_metadata: optional map[unknown]` ### Example ```cli llamacloud-prod pipelines:documents create \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --body '{metadata: {foo: bar}, text: text}' ``` #### 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 `$ llamacloud-prod pipelines:documents list` **get** `/api/v1/pipelines/{pipeline_id}/documents/paginated` Return a list of documents for a pipeline. ### Parameters - `--pipeline-id: string` - `--file-id: optional string` - `--limit: optional number` - `--only-api-data-source-documents: optional boolean` - `--only-direct-upload: optional boolean` - `--skip: optional number` - `--status-refresh-policy: optional "cached" or "ttl"` ### Returns - `PaginatedListCloudDocumentsResponse: object { documents, limit, offset, total_count }` - `documents: array of CloudDocument` The documents to list - `id: string` - `metadata: map[unknown]` - `text: string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. - `status_metadata: optional map[unknown]` - `limit: number` The limit of the documents - `offset: number` The offset of the documents - `total_count: number` The total number of documents ### Example ```cli llamacloud-prod pipelines:documents list \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### 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 `$ llamacloud-prod pipelines:documents get` **get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}` Return a single document for a pipeline. ### Parameters - `--pipeline-id: string` - `--document-id: string` ### Returns - `cloud_document: object { id, metadata, text, 4 more }` Cloud document stored in S3. - `id: string` - `metadata: map[unknown]` - `text: string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. - `status_metadata: optional map[unknown]` ### Example ```cli llamacloud-prod pipelines:documents get \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --document-id 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 `$ llamacloud-prod pipelines:documents delete` **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: string` - `--document-id: string` ### Example ```cli llamacloud-prod pipelines:documents delete \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --document-id document_id ``` ## Get Pipeline Document Status `$ llamacloud-prod pipelines:documents get-status` **get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/status` Return a single document for a pipeline. ### Parameters - `--pipeline-id: string` - `--document-id: string` ### Returns - `managed_ingestion_status_response: object { status, deployment_date, effective_at, 2 more }` - `status: "NOT_STARTED" or "IN_PROGRESS" or "SUCCESS" or 3 more` Status of the ingestion. - `"NOT_STARTED"` - `"IN_PROGRESS"` - `"SUCCESS"` - `"ERROR"` - `"PARTIAL_SUCCESS"` - `"CANCELLED"` - `deployment_date: optional string` Date of the deployment. - `effective_at: optional string` When the status is effective - `error: optional array of object { job_id, message, step }` List of errors that occurred during ingestion. - `job_id: string` ID of the job that failed. - `message: string` List of errors that occurred during ingestion. - `step: "MANAGED_INGESTION" or "DATA_SOURCE" or "FILE_UPDATER" or 4 more` Name of the job that failed. - `"MANAGED_INGESTION"` - `"DATA_SOURCE"` - `"FILE_UPDATER"` - `"PARSE"` - `"TRANSFORM"` - `"INGESTION"` - `"METADATA_UPDATE"` - `job_id: optional string` ID of the latest job. ### Example ```cli llamacloud-prod pipelines:documents get-status \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --document-id document_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 `$ llamacloud-prod pipelines:documents sync` **post** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/sync` Sync a specific document for a pipeline. ### Parameters - `--pipeline-id: string` - `--document-id: string` ### Returns - `PipelineDocumentSyncResponse: unknown` ### Example ```cli llamacloud-prod pipelines:documents sync \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --document-id document_id ``` #### Response ```json {} ``` ## List Pipeline Document Chunks `$ llamacloud-prod pipelines:documents get-chunks` **get** `/api/v1/pipelines/{pipeline_id}/documents/{document_id}/chunks` Return a list of chunks for a pipeline document. ### Parameters - `--pipeline-id: string` - `--document-id: string` ### Returns - `Response List Pipeline Document Chunks Api V1 Pipelines Pipeline Id Documents Document Id Chunks Get: array of TextNode` - `class_name: optional string` - `embedding: optional array of number` Embedding of the node. - `end_char_idx: optional number` End char index of the node. - `excluded_embed_metadata_keys: optional array of string` Metadata keys that are excluded from text for the embed model. - `excluded_llm_metadata_keys: optional array of string` Metadata keys that are excluded from text for the LLM. - `extra_info: optional map[unknown]` A flat dictionary of metadata fields - `id_: optional string` Unique ID of the node. - `metadata_seperator: optional string` Separator between metadata fields when converting to string. - `metadata_template: optional string` Template for how metadata is formatted, with {key} and {value} placeholders. - `mimetype: optional string` MIME type of the node content. - `relationships: optional map[object { node_id, class_name, hash, 2 more } or array of object { node_id, class_name, hash, 2 more } ]` A mapping of relationships to other node information. - `RelatedNodeInfo: object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `union_member_1: array of object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `start_char_idx: optional number` Start char index of the node. - `text: optional string` Text content of the node. - `text_template: optional string` Template for how text is formatted, with {content} and {metadata_str} placeholders. ### Example ```cli llamacloud-prod pipelines:documents get-chunks \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --document-id document_id ``` #### 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 `$ llamacloud-prod pipelines:documents upsert` **put** `/api/v1/pipelines/{pipeline_id}/documents` Batch create or update a document for a pipeline. ### Parameters - `--pipeline-id: string` - `--body: array of CloudDocumentCreate` ### Returns - `Response Upsert Batch Pipeline Documents Api V1 Pipelines Pipeline Id Documents Put: array of CloudDocument` - `id: string` - `metadata: map[unknown]` - `text: string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. - `status_metadata: optional map[unknown]` ### Example ```cli llamacloud-prod pipelines:documents upsert \ --api-key 'My API Key' \ --pipeline-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \ --body '{metadata: {foo: bar}, text: text}' ``` #### 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 - `cloud_document: object { id, metadata, text, 4 more }` Cloud document stored in S3. - `id: string` - `metadata: map[unknown]` - `text: string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. - `status_metadata: optional map[unknown]` ### Cloud Document Create - `cloud_document_create: object { metadata, text, id, 3 more }` Create a new cloud document. - `metadata: map[unknown]` - `text: string` - `id: optional string` - `excluded_embed_metadata_keys: optional array of string` - `excluded_llm_metadata_keys: optional array of string` - `page_positions: optional array of number` indices in the CloudDocument.text where a new page begins. e.g. Second page starts at index specified by page_positions[1]. ### Text Node - `text_node: object { class_name, embedding, end_char_idx, 11 more }` Provided for backward compatibility. - `class_name: optional string` - `embedding: optional array of number` Embedding of the node. - `end_char_idx: optional number` End char index of the node. - `excluded_embed_metadata_keys: optional array of string` Metadata keys that are excluded from text for the embed model. - `excluded_llm_metadata_keys: optional array of string` Metadata keys that are excluded from text for the LLM. - `extra_info: optional map[unknown]` A flat dictionary of metadata fields - `id_: optional string` Unique ID of the node. - `metadata_seperator: optional string` Separator between metadata fields when converting to string. - `metadata_template: optional string` Template for how metadata is formatted, with {key} and {value} placeholders. - `mimetype: optional string` MIME type of the node content. - `relationships: optional map[object { node_id, class_name, hash, 2 more } or array of object { node_id, class_name, hash, 2 more } ]` A mapping of relationships to other node information. - `RelatedNodeInfo: object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `union_member_1: array of object { node_id, class_name, hash, 2 more }` - `node_id: string` - `class_name: optional string` - `hash: optional string` - `metadata: optional map[unknown]` - `node_type: optional "1" or "2" or "3" or 2 more or string` - `"1"` - `"2"` - `"3"` - `"4"` - `"5"` - `start_char_idx: optional number` Start char index of the node. - `text: optional string` Text content of the node. - `text_template: optional string` Template for how text is formatted, with {content} and {metadata_str} placeholders.