---
title: "Upsert Pipeline"
method: PUT
path: "/api/v1/pipelines"
tags: ["Pipelines"]
---

# Upsert Pipeline

`PUT /api/v1/pipelines`

Upsert a pipeline.

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

## Query parameters

- `project_id` string, uuid, nullable
- `organization_id` string, uuid, nullable

## Cookies

- `session` string, nullable

## Request body

- PipelineCreate — Schema for creating a pipeline.
  - `embedding_config` union
    - AzureOpenAIEmbeddingConfig
      - `type` 'AZURE_EMBEDDING' — Type of the embedding model.
      - `component` AzureOpenAIEmbedding
        - `model_name` string — The name of the OpenAI embedding model.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `additional_kwargs` object — Additional kwargs for the OpenAI API.
        - `api_key` string, nullable — The OpenAI API key.
        - `api_base` string — The base URL for Azure deployment.
        - `api_version` string — The version for Azure OpenAI API.
        - `max_retries` integer — Maximum number of retries.
        - `timeout` number — Timeout for each request.
        - `default_headers` object, nullable — The default headers for API requests.
        - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
        - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
        - `azure_endpoint` string, nullable — The Azure endpoint to use.
        - `azure_deployment` string, nullable — The Azure deployment to use.
        - `class_name` string
    - CohereEmbeddingConfig
      - `type` 'COHERE_EMBEDDING' — Type of the embedding model.
      - `component` CohereEmbedding
        - `model_name` string — The modelId of the Cohere model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `api_key` string, nullable, required — The Cohere API key.
        - `truncate` string — Truncation type - START/ END/ NONE
        - `input_type` string, nullable — Model Input type. If not provided, search_document and search_query are used when needed.
        - `embedding_type` string — Embedding type. If not provided float embedding_type is used when needed.
        - `class_name` string
    - GeminiEmbeddingConfig
      - `type` 'GEMINI_EMBEDDING' — Type of the embedding model.
      - `component` GeminiEmbedding
        - `model_name` string — The modelId of the Gemini model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `title` string, nullable — Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.
        - `task_type` string, nullable — The task for embedding model.
        - `api_key` string, nullable — API key to access the model. Defaults to None.
        - `api_base` string, nullable — API base to access the model. Defaults to None.
        - `transport` string, nullable — Transport to access the model. Defaults to None.
        - `output_dimensionality` integer, nullable — 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.
        - `class_name` string
    - HuggingFaceInferenceAPIEmbeddingConfig
      - `type` 'HUGGINGFACE_API_EMBEDDING' — Type of the embedding model.
      - `component` HuggingFaceInferenceAPIEmbedding
        - `model_name` string, nullable — Hugging Face model name. If None, the task will be used.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `pooling` 'cls' | 'mean' | 'last' — Enum of possible pooling choices with pooling behaviors.
        - `query_instruction` string, nullable — Instruction to prepend during query embedding.
        - `text_instruction` string, nullable — Instruction to prepend during text embedding.
        - `token` union — Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.
          - string
          - boolean
        - `timeout` number, nullable — 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.
        - `headers` object, nullable — 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.
        - `cookies` object, nullable — Additional cookies to send to the server.
        - `task` string, nullable — Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.
        - `class_name` string
    - OpenAIEmbeddingConfig
      - `type` 'OPENAI_EMBEDDING' — Type of the embedding model.
      - `component` OpenAIEmbedding
        - `model_name` string — The name of the OpenAI embedding model.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `additional_kwargs` object — Additional kwargs for the OpenAI API.
        - `api_key` string, nullable — The OpenAI API key.
        - `api_base` string, nullable — The base URL for OpenAI API.
        - `api_version` string, nullable — The version for OpenAI API.
        - `max_retries` integer — Maximum number of retries.
        - `timeout` number — Timeout for each request.
        - `default_headers` object, nullable — The default headers for API requests.
        - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
        - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
        - `class_name` string
    - VertexAIEmbeddingConfig
      - `type` 'VERTEXAI_EMBEDDING' — Type of the embedding model.
      - `component` VertexTextEmbedding
        - `model_name` string — The modelId of the VertexAI model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `location` string, required — The default location to use when making API calls.
        - `project` string, required — The default GCP project to use when making Vertex API calls.
        - `embed_mode` 'default' | 'classification' | 'clustering' | 'similarity' | 'retrieval' — Copied from llama_index.embeddings.vertex.base.VertexEmbeddingMode since importing llama_index.embeddings.vertex.base incurs a lot of memory usage.
        - `additional_kwargs` object — Additional kwargs for the Vertex.
        - `client_email` string, nullable, required — The client email for the VertexAI credentials.
        - `token_uri` string, nullable, required — The token URI for the VertexAI credentials.
        - `private_key_id` string, nullable, required — The private key ID for the VertexAI credentials.
        - `private_key` string, nullable, required — The private key for the VertexAI credentials.
        - `class_name` string
    - BedrockEmbeddingConfig
      - `type` 'BEDROCK_EMBEDDING' — Type of the embedding model.
      - `component` BedrockEmbedding
        - `model_name` string — The modelId of the Bedrock model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `profile_name` string, nullable — The name of aws profile to use. If not given, then the default profile is used.
        - `aws_access_key_id` string, nullable — AWS Access Key ID to use
        - `aws_secret_access_key` string, nullable — AWS Secret Access Key to use
        - `aws_session_token` string, nullable — AWS Session Token to use
        - `region_name` string, nullable — AWS region name to use. Uses region configured in AWS CLI if not passed
        - `max_retries` integer — The maximum number of API retries.
        - `timeout` number — The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.
        - `additional_kwargs` object — Additional kwargs for the bedrock client.
        - `class_name` string
  - `transform_config` union — Configuration for the transformation.
    - AutoTransformConfig
      - `mode` 'auto'
      - `chunk_size` integer — Chunk size for the transformation.
      - `chunk_overlap` integer — Chunk overlap for the transformation.
    - AdvancedModeTransformConfig
      - `mode` 'advanced'
      - `segmentation_config` union — Configuration for the segmentation.
        - NoneSegmentationConfig
          - `mode` 'none'
        - PageSegmentationConfig
          - `mode` 'page'
          - `page_separator` string
        - ElementSegmentationConfig
          - `mode` 'element'
      - `chunking_config` union — Configuration for the chunking.
        - NoneChunkingConfig
          - `mode` 'none'
        - CharacterChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'character'
        - TokenChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'token'
          - `separator` string
        - SentenceChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'sentence'
          - `separator` string
          - `paragraph_separator` string
        - SemanticChunkingConfig
          - `mode` 'semantic'
          - `buffer_size` integer
          - `breakpoint_percentile_threshold` integer
  - `sparse_model_config` SparseModelConfig — Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks.
    - `model_type` 'splade' | 'bm25' | 'auto' — Enum for sparse model types supported in LlamaCloud. SPLADE: Uses HuggingFace Splade model for sparse embeddings BM25: Uses Qdrant's FastEmbed BM25 model for sparse embeddings AUTO: Automatically selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade)
    - `class_name` string
  - `data_sink_id` string, uuid, nullable — Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.
  - `embedding_model_config_id` string, uuid, nullable — Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.
  - `data_sink` DataSinkCreate — Schema for creating a data sink.
    - `name` string, required — The name of the data sink.
    - `sink_type` 'PINECONE' | 'POSTGRES' | 'QDRANT' | 'AZUREAI_SEARCH' | 'MONGODB_ATLAS' | 'MILVUS' | 'ASTRA_DB', required
    - `component` union, required — Component that implements the data sink
      - object
      - CloudPineconeVectorStore — Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion
        - `supports_nested_metadata_filters` true
        - `api_key` string, password, required — The API key for authenticating with Pinecone
        - `index_name` string, required
        - `namespace` string, nullable
        - `insert_kwargs` object, nullable
        - `class_name` string
      - CloudPostgresVectorStore
        - `supports_nested_metadata_filters` boolean
        - `database` string, required
        - `host` string, required
        - `password` string, password, required
        - `port` integer, required
        - `user` string, required
        - `table_name` string, required
        - `schema_name` string, required
        - `embed_dim` integer, required
        - `hybrid_search` boolean, nullable
        - `perform_setup` boolean
        - `hnsw_settings` PGVectorHNSWSettings — HNSW settings for PGVector.
          - `ef_construction` integer — The number of edges to use during the construction phase.
          - `ef_search` integer — The number of edges to use during the search phase.
          - `m` integer — The number of bi-directional links created for each new element.
          - `vector_type` 'vector' | 'half_vec' | 'bit' | 'sparse_vec' — Vector storage formats for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options
          - `distance_method` 'l2' | 'ip' | 'cosine' | 'l1' | 'hamming' | 'jaccard' — Distance methods for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options
        - `class_name` string
      - CloudQdrantVectorStore — Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client
        - `supports_nested_metadata_filters` true
        - `collection_name` string, required
        - `url` string, required
        - `api_key` string, password, required
        - `max_retries` integer
        - `client_kwargs` object
        - `class_name` string
      - CloudAzureAISearchVectorStore — Cloud Azure AI Search Vector Store.
        - `supports_nested_metadata_filters` true
        - `search_service_api_key` string, password, required
        - `search_service_endpoint` string, required
        - `search_service_api_version` string, nullable
        - `index_name` string, nullable
        - `filterable_metadata_field_keys` object, nullable
        - `embedding_dimension` integer, nullable
        - `client_id` string, nullable
        - `client_secret` string, password, nullable
        - `tenant_id` string, nullable
        - `class_name` string
      - CloudMongoDBAtlasVectorSearch — Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index
        - `supports_nested_metadata_filters` boolean
        - `mongodb_uri` string, password, required
        - `db_name` string, required
        - `collection_name` string, required
        - `vector_index_name` string, nullable
        - `fulltext_index_name` string, nullable
        - `embedding_dimension` integer, nullable
        - `class_name` string
      - CloudMilvusVectorStore — Cloud Milvus Vector Store.
        - `supports_nested_metadata_filters` boolean
        - `uri` string, required
        - `collection_name` string, nullable
        - `token` string, password, nullable
        - `embedding_dimension` integer, nullable
        - `class_name` string
      - CloudAstraDBVectorStore — Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'
        - `supports_nested_metadata_filters` true
        - `token` string, password, required — The Astra DB Application Token to use
        - `api_endpoint` string, required — The Astra DB JSON API endpoint for your database
        - `collection_name` string, required — Collection name to use. If not existing, it will be created
        - `embedding_dimension` integer, required — Length of the embedding vectors in use
        - `keyspace` string, nullable — The keyspace to use. If not provided, 'default_keyspace'
        - `class_name` string
  - `preset_retrieval_parameters` PresetRetrievalParams — Schema for the search params for an retrieval execution that can be preset for a pipeline.
    - `dense_similarity_top_k` integer, nullable — Number of nodes for dense retrieval.
    - `dense_similarity_cutoff` number, nullable — Minimum similarity score wrt query for retrieval
    - `sparse_similarity_top_k` integer, nullable — Number of nodes for sparse retrieval.
    - `enable_reranking` boolean, nullable — Enable reranking for retrieval
    - `rerank_top_n` integer, nullable — Number of reranked nodes for returning.
    - `alpha` number, nullable — Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
    - `search_filters` MetadataFilters — Metadata filters for vector stores.
      - `filters` union[], required
        - union
          - MetadataFilter — 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, required
            - `value` union, required
              - …
            - `operator` '==' | '>' | '<' | '!=' | '>=' | '<=' | 'in' | 'nin' | 'any' | 'all' | 'text_match' | 'text_match_insensitive' | 'contains' | 'is_empty' — Vector store filter operator.
          - MetadataFilters — recursive
      - `condition` 'and' | 'or' | 'not' — Vector store filter conditions to combine different filters.
    - `search_filters_inference_schema` object, nullable — JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.
    - `files_top_k` integer, nullable — Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
    - `retrieval_mode` 'chunks' | 'files_via_metadata' | 'files_via_content' | 'auto_routed'
    - `retrieve_image_nodes` boolean — Whether to retrieve image nodes.
    - `retrieve_page_screenshot_nodes` boolean — Whether to retrieve page screenshot nodes.
    - `retrieve_page_figure_nodes` boolean — Whether to retrieve page figure nodes.
    - `class_name` string
  - `llama_parse_parameters` LlamaParseParameters
    - `webhook_configurations` WebhookConfiguration[], nullable — Outbound webhook endpoints to notify on job status changes
      - `webhook_url` string, nullable — URL to receive webhook POST notifications
      - `webhook_headers` object, nullable — Custom HTTP headers sent with each webhook request (e.g. auth tokens)
      - `webhook_events` string[], nullable — Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.
      - `webhook_output_format` string, nullable — Response format sent to the webhook: 'string' (default) or 'json'
    - `priority` 'low' | 'medium' | 'high' | 'critical', nullable — The priority for the request. This field may be ignored or overwritten depending on the organization tier.
    - `languages` ParserLanguages[]
    - `parsing_instruction` string, nullable
    - `disable_ocr` boolean, nullable
    - `annotate_links` boolean, nullable
    - `adaptive_long_table` boolean, nullable
    - `compact_markdown_table` boolean, nullable
    - `disable_reconstruction` boolean, nullable
    - `disable_image_extraction` boolean, nullable
    - `invalidate_cache` boolean, nullable
    - `outlined_table_extraction` boolean, nullable
    - `aggressive_table_extraction` boolean, nullable
    - `merge_tables_across_pages_in_markdown` boolean, nullable
    - `output_pdf_of_document` boolean, nullable
    - `do_not_cache` boolean, nullable
    - `fast_mode` boolean, nullable
    - `skip_diagonal_text` boolean, nullable
    - `preserve_layout_alignment_across_pages` boolean, nullable
    - `preserve_very_small_text` boolean, nullable
    - `gpt4o_mode` boolean, nullable
    - `gpt4o_api_key` string, nullable
    - `do_not_unroll_columns` boolean, nullable
    - `extract_layout` boolean, nullable
    - `high_res_ocr` boolean, nullable
    - `html_make_all_elements_visible` boolean, nullable
    - `layout_aware` boolean, nullable
    - `specialized_chart_parsing_agentic` boolean, nullable
    - `specialized_chart_parsing_plus` boolean, nullable
    - `specialized_chart_parsing_efficient` boolean, nullable
    - `specialized_image_parsing` boolean, nullable
    - `precise_bounding_box` boolean, nullable
    - `line_level_bounding_box` boolean, nullable
    - `html_remove_navigation_elements` boolean, nullable
    - `html_remove_fixed_elements` boolean, nullable
    - `guess_xlsx_sheet_name` boolean, nullable
    - `page_separator` string, nullable
    - `bounding_box` string, nullable
    - `bbox_top` number, nullable
    - `bbox_right` number, nullable
    - `bbox_bottom` number, nullable
    - `bbox_left` number, nullable
    - `target_pages` string, nullable
    - `use_vendor_multimodal_model` boolean, nullable
    - `vendor_multimodal_model_name` string, nullable
    - `model` string, nullable
    - `vendor_multimodal_api_key` string, nullable
    - `page_prefix` string, nullable
    - `page_suffix` string, nullable
    - `webhook_url` string, nullable
    - `preset` string, nullable
    - `take_screenshot` boolean, nullable
    - `is_formatting_instruction` boolean, nullable
    - `premium_mode` boolean, nullable
    - `continuous_mode` boolean, nullable
    - `input_s3_path` string, nullable
    - `input_s3_region` string, nullable
    - `output_s3_path_prefix` string, nullable
    - `output_s3_region` string, nullable
    - `project_id` string, nullable
    - `azure_openai_deployment_name` string, nullable
    - `azure_openai_endpoint` string, nullable
    - `azure_openai_api_version` string, nullable
    - `azure_openai_key` string, nullable
    - `input_url` string, nullable
    - `http_proxy` string, nullable
    - `auto_mode` boolean, nullable
    - `auto_mode_trigger_on_regexp_in_page` string, nullable
    - `auto_mode_trigger_on_text_in_page` string, nullable
    - `auto_mode_trigger_on_table_in_page` boolean, nullable
    - `auto_mode_trigger_on_image_in_page` boolean, nullable
    - `auto_mode_configuration_json` string, nullable
    - `structured_output` boolean, nullable
    - `structured_output_json_schema` string, nullable
    - `structured_output_json_schema_name` string, nullable
    - `max_pages` integer, nullable
    - `max_pages_enforced` integer, nullable
    - `extract_charts` boolean, nullable
    - `formatting_instruction` string, nullable
    - `complemental_formatting_instruction` string, nullable
    - `content_guideline_instruction` string, nullable
    - `spreadsheet_extract_sub_tables` boolean, nullable
    - `spreadsheet_force_formula_computation` boolean, nullable
    - `spreadsheet_include_hidden_sheets` boolean, nullable
    - `inline_images_in_markdown` boolean, nullable
    - `job_timeout_in_seconds` number, nullable
    - `job_timeout_extra_time_per_page_in_seconds` number, nullable
    - `strict_mode_image_extraction` boolean, nullable
    - `strict_mode_image_ocr` boolean, nullable
    - `strict_mode_reconstruction` boolean, nullable
    - `strict_mode_buggy_font` boolean, nullable
    - `save_images` boolean, nullable
    - `images_to_save` string[], nullable
    - `hide_headers` boolean, nullable
    - `hide_footers` boolean, nullable
    - `page_header_prefix` string, nullable
    - `page_header_suffix` string, nullable
    - `page_footer_prefix` string, nullable
    - `page_footer_suffix` string, nullable
    - `remove_hidden_text` boolean, nullable
    - `keep_page_separator_when_merging_tables` boolean, nullable
    - `ignore_document_elements_for_layout_detection` boolean, nullable
    - `output_tables_as_HTML` boolean, nullable
    - `internal_is_screenshot_job` boolean, nullable
    - `parse_mode` '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' — Enum for representing the mode of parsing to be used.
    - `system_prompt` string, nullable
    - `system_prompt_append` string, nullable
    - `user_prompt` string, nullable
    - `page_error_tolerance` number, nullable
    - `replace_failed_page_mode` 'raw_text' | 'blank_page' | 'error_message' — Enum for representing the different available page error handling modes.
    - `replace_failed_page_with_error_message_prefix` string, nullable
    - `replace_failed_page_with_error_message_suffix` string, nullable
    - `markdown_table_multiline_header_separator` string, nullable
    - `presentation_out_of_bounds_content` boolean, nullable
    - `presentation_skip_embedded_data` boolean, nullable
    - `tier` string, nullable
    - `version` string, nullable
    - `extract_printed_page_number` boolean, nullable
    - `enable_cost_optimizer` boolean, nullable
  - `status` string, nullable — Status of the pipeline deployment.
  - `metadata_config` PipelineMetadataConfig
    - `excluded_embed_metadata_keys` string[] — List of metadata keys to exclude from embeddings
    - `excluded_llm_metadata_keys` string[] — List of metadata keys to exclude from LLM during retrieval
  - `name` string, required
  - `pipeline_type` 'PLAYGROUND' | 'MANAGED' — Enum for representing the type of a pipeline
  - `managed_pipeline_id` string, uuid, nullable — The ID of the ManagedPipeline this playground pipeline is linked to.

## Response `200`

Successful Response

- Pipeline — Schema for a pipeline.
  - `id` string, uuid, required — Unique identifier
  - `created_at` string, date-time, nullable — Creation datetime
  - `updated_at` string, date-time, nullable — Update datetime
  - `name` string, required
  - `project_id` string, uuid, required
  - `embedding_model_config_id` string, uuid, nullable — The ID of the EmbeddingModelConfig this pipeline is using.
  - `embedding_model_config` EmbeddingModelConfig — Schema for an embedding model config.
    - `id` string, uuid, required — Unique identifier
    - `created_at` string, date-time, nullable — Creation datetime
    - `updated_at` string, date-time, nullable — Update datetime
    - `name` string, required — The name of the embedding model config.
    - `embedding_config` union, required — The embedding configuration for the embedding model config.
      - AzureOpenAIEmbeddingConfig
        - `type` 'AZURE_EMBEDDING' — Type of the embedding model.
        - `component` AzureOpenAIEmbedding
          - `model_name` string — The name of the OpenAI embedding model.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `additional_kwargs` object — Additional kwargs for the OpenAI API.
          - `api_key` string, nullable — The OpenAI API key.
          - `api_base` string — The base URL for Azure deployment.
          - `api_version` string — The version for Azure OpenAI API.
          - `max_retries` integer — Maximum number of retries.
          - `timeout` number — Timeout for each request.
          - `default_headers` object, nullable — The default headers for API requests.
          - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
          - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
          - `azure_endpoint` string, nullable — The Azure endpoint to use.
          - `azure_deployment` string, nullable — The Azure deployment to use.
          - `class_name` string
      - CohereEmbeddingConfig
        - `type` 'COHERE_EMBEDDING' — Type of the embedding model.
        - `component` CohereEmbedding
          - `model_name` string — The modelId of the Cohere model to use.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `api_key` string, nullable, required — The Cohere API key.
          - `truncate` string — Truncation type - START/ END/ NONE
          - `input_type` string, nullable — Model Input type. If not provided, search_document and search_query are used when needed.
          - `embedding_type` string — Embedding type. If not provided float embedding_type is used when needed.
          - `class_name` string
      - GeminiEmbeddingConfig
        - `type` 'GEMINI_EMBEDDING' — Type of the embedding model.
        - `component` GeminiEmbedding
          - `model_name` string — The modelId of the Gemini model to use.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `title` string, nullable — Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.
          - `task_type` string, nullable — The task for embedding model.
          - `api_key` string, nullable — API key to access the model. Defaults to None.
          - `api_base` string, nullable — API base to access the model. Defaults to None.
          - `transport` string, nullable — Transport to access the model. Defaults to None.
          - `output_dimensionality` integer, nullable — 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.
          - `class_name` string
      - HuggingFaceInferenceAPIEmbeddingConfig
        - `type` 'HUGGINGFACE_API_EMBEDDING' — Type of the embedding model.
        - `component` HuggingFaceInferenceAPIEmbedding
          - `model_name` string, nullable — Hugging Face model name. If None, the task will be used.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `pooling` 'cls' | 'mean' | 'last' — Enum of possible pooling choices with pooling behaviors.
          - `query_instruction` string, nullable — Instruction to prepend during query embedding.
          - `text_instruction` string, nullable — Instruction to prepend during text embedding.
          - `token` union — Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.
            - string
            - boolean
          - `timeout` number, nullable — 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.
          - `headers` object, nullable — 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.
          - `cookies` object, nullable — Additional cookies to send to the server.
          - `task` string, nullable — Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.
          - `class_name` string
      - OpenAIEmbeddingConfig
        - `type` 'OPENAI_EMBEDDING' — Type of the embedding model.
        - `component` OpenAIEmbedding
          - `model_name` string — The name of the OpenAI embedding model.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `additional_kwargs` object — Additional kwargs for the OpenAI API.
          - `api_key` string, nullable — The OpenAI API key.
          - `api_base` string, nullable — The base URL for OpenAI API.
          - `api_version` string, nullable — The version for OpenAI API.
          - `max_retries` integer — Maximum number of retries.
          - `timeout` number — Timeout for each request.
          - `default_headers` object, nullable — The default headers for API requests.
          - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
          - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
          - `class_name` string
      - VertexAIEmbeddingConfig
        - `type` 'VERTEXAI_EMBEDDING' — Type of the embedding model.
        - `component` VertexTextEmbedding
          - `model_name` string — The modelId of the VertexAI model to use.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `location` string, required — The default location to use when making API calls.
          - `project` string, required — The default GCP project to use when making Vertex API calls.
          - `embed_mode` 'default' | 'classification' | 'clustering' | 'similarity' | 'retrieval' — Copied from llama_index.embeddings.vertex.base.VertexEmbeddingMode since importing llama_index.embeddings.vertex.base incurs a lot of memory usage.
          - `additional_kwargs` object — Additional kwargs for the Vertex.
          - `client_email` string, nullable, required — The client email for the VertexAI credentials.
          - `token_uri` string, nullable, required — The token URI for the VertexAI credentials.
          - `private_key_id` string, nullable, required — The private key ID for the VertexAI credentials.
          - `private_key` string, nullable, required — The private key for the VertexAI credentials.
          - `class_name` string
      - BedrockEmbeddingConfig
        - `type` 'BEDROCK_EMBEDDING' — Type of the embedding model.
        - `component` BedrockEmbedding
          - `model_name` string — The modelId of the Bedrock model to use.
          - `embed_batch_size` integer — The batch size for embedding calls.
          - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
          - `profile_name` string, nullable — The name of aws profile to use. If not given, then the default profile is used.
          - `aws_access_key_id` string, nullable — AWS Access Key ID to use
          - `aws_secret_access_key` string, nullable — AWS Secret Access Key to use
          - `aws_session_token` string, nullable — AWS Session Token to use
          - `region_name` string, nullable — AWS region name to use. Uses region configured in AWS CLI if not passed
          - `max_retries` integer — The maximum number of API retries.
          - `timeout` number — The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.
          - `additional_kwargs` object — Additional kwargs for the bedrock client.
          - `class_name` string
    - `project_id` string, uuid, required
  - `pipeline_type` 'PLAYGROUND' | 'MANAGED' — Enum for representing the type of a pipeline
  - `managed_pipeline_id` string, uuid, nullable — The ID of the ManagedPipeline this playground pipeline is linked to.
  - `embedding_config` union, required
    - ManagedOpenAIEmbeddingConfig
      - `type` 'MANAGED_OPENAI_EMBEDDING' — Type of the embedding model.
      - `component` ManagedOpenAIEmbedding
        - `model_name` 'openai-text-embedding-3-small' — The name of the OpenAI embedding model.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `class_name` string
    - AzureOpenAIEmbeddingConfig
      - `type` 'AZURE_EMBEDDING' — Type of the embedding model.
      - `component` AzureOpenAIEmbedding
        - `model_name` string — The name of the OpenAI embedding model.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `additional_kwargs` object — Additional kwargs for the OpenAI API.
        - `api_key` string, nullable — The OpenAI API key.
        - `api_base` string — The base URL for Azure deployment.
        - `api_version` string — The version for Azure OpenAI API.
        - `max_retries` integer — Maximum number of retries.
        - `timeout` number — Timeout for each request.
        - `default_headers` object, nullable — The default headers for API requests.
        - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
        - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
        - `azure_endpoint` string, nullable — The Azure endpoint to use.
        - `azure_deployment` string, nullable — The Azure deployment to use.
        - `class_name` string
    - CohereEmbeddingConfig
      - `type` 'COHERE_EMBEDDING' — Type of the embedding model.
      - `component` CohereEmbedding
        - `model_name` string — The modelId of the Cohere model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `api_key` string, nullable, required — The Cohere API key.
        - `truncate` string — Truncation type - START/ END/ NONE
        - `input_type` string, nullable — Model Input type. If not provided, search_document and search_query are used when needed.
        - `embedding_type` string — Embedding type. If not provided float embedding_type is used when needed.
        - `class_name` string
    - GeminiEmbeddingConfig
      - `type` 'GEMINI_EMBEDDING' — Type of the embedding model.
      - `component` GeminiEmbedding
        - `model_name` string — The modelId of the Gemini model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `title` string, nullable — Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.
        - `task_type` string, nullable — The task for embedding model.
        - `api_key` string, nullable — API key to access the model. Defaults to None.
        - `api_base` string, nullable — API base to access the model. Defaults to None.
        - `transport` string, nullable — Transport to access the model. Defaults to None.
        - `output_dimensionality` integer, nullable — 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.
        - `class_name` string
    - HuggingFaceInferenceAPIEmbeddingConfig
      - `type` 'HUGGINGFACE_API_EMBEDDING' — Type of the embedding model.
      - `component` HuggingFaceInferenceAPIEmbedding
        - `model_name` string, nullable — Hugging Face model name. If None, the task will be used.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `pooling` 'cls' | 'mean' | 'last' — Enum of possible pooling choices with pooling behaviors.
        - `query_instruction` string, nullable — Instruction to prepend during query embedding.
        - `text_instruction` string, nullable — Instruction to prepend during text embedding.
        - `token` union — Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.
          - string
          - boolean
        - `timeout` number, nullable — 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.
        - `headers` object, nullable — 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.
        - `cookies` object, nullable — Additional cookies to send to the server.
        - `task` string, nullable — Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.
        - `class_name` string
    - OpenAIEmbeddingConfig
      - `type` 'OPENAI_EMBEDDING' — Type of the embedding model.
      - `component` OpenAIEmbedding
        - `model_name` string — The name of the OpenAI embedding model.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `additional_kwargs` object — Additional kwargs for the OpenAI API.
        - `api_key` string, nullable — The OpenAI API key.
        - `api_base` string, nullable — The base URL for OpenAI API.
        - `api_version` string, nullable — The version for OpenAI API.
        - `max_retries` integer — Maximum number of retries.
        - `timeout` number — Timeout for each request.
        - `default_headers` object, nullable — The default headers for API requests.
        - `reuse_client` boolean — Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.
        - `dimensions` integer, nullable — The number of dimensions on the output embedding vectors. Works only with v3 embedding models.
        - `class_name` string
    - VertexAIEmbeddingConfig
      - `type` 'VERTEXAI_EMBEDDING' — Type of the embedding model.
      - `component` VertexTextEmbedding
        - `model_name` string — The modelId of the VertexAI model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `location` string, required — The default location to use when making API calls.
        - `project` string, required — The default GCP project to use when making Vertex API calls.
        - `embed_mode` 'default' | 'classification' | 'clustering' | 'similarity' | 'retrieval' — Copied from llama_index.embeddings.vertex.base.VertexEmbeddingMode since importing llama_index.embeddings.vertex.base incurs a lot of memory usage.
        - `additional_kwargs` object — Additional kwargs for the Vertex.
        - `client_email` string, nullable, required — The client email for the VertexAI credentials.
        - `token_uri` string, nullable, required — The token URI for the VertexAI credentials.
        - `private_key_id` string, nullable, required — The private key ID for the VertexAI credentials.
        - `private_key` string, nullable, required — The private key for the VertexAI credentials.
        - `class_name` string
    - BedrockEmbeddingConfig
      - `type` 'BEDROCK_EMBEDDING' — Type of the embedding model.
      - `component` BedrockEmbedding
        - `model_name` string — The modelId of the Bedrock model to use.
        - `embed_batch_size` integer — The batch size for embedding calls.
        - `num_workers` integer, nullable — The number of workers to use for async embedding calls.
        - `profile_name` string, nullable — The name of aws profile to use. If not given, then the default profile is used.
        - `aws_access_key_id` string, nullable — AWS Access Key ID to use
        - `aws_secret_access_key` string, nullable — AWS Secret Access Key to use
        - `aws_session_token` string, nullable — AWS Session Token to use
        - `region_name` string, nullable — AWS region name to use. Uses region configured in AWS CLI if not passed
        - `max_retries` integer — The maximum number of API retries.
        - `timeout` number — The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.
        - `additional_kwargs` object — Additional kwargs for the bedrock client.
        - `class_name` string
  - `sparse_model_config` SparseModelConfig — Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks.
    - `model_type` 'splade' | 'bm25' | 'auto' — Enum for sparse model types supported in LlamaCloud. SPLADE: Uses HuggingFace Splade model for sparse embeddings BM25: Uses Qdrant's FastEmbed BM25 model for sparse embeddings AUTO: Automatically selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade)
    - `class_name` string
  - `config_hash` PipelineConfigurationHashes — Hashes for the configuration of a pipeline.
    - `embedding_config_hash` string, nullable — Hash of the embedding config.
    - `parsing_config_hash` string, nullable — Hash of the llama parse parameters.
    - `transform_config_hash` string, nullable — Hash of the transform config.
  - `transform_config` union — Configuration for the transformation.
    - AutoTransformConfig
      - `mode` 'auto'
      - `chunk_size` integer — Chunk size for the transformation.
      - `chunk_overlap` integer — Chunk overlap for the transformation.
    - AdvancedModeTransformConfig
      - `mode` 'advanced'
      - `segmentation_config` union — Configuration for the segmentation.
        - NoneSegmentationConfig
          - `mode` 'none'
        - PageSegmentationConfig
          - `mode` 'page'
          - `page_separator` string
        - ElementSegmentationConfig
          - `mode` 'element'
      - `chunking_config` union — Configuration for the chunking.
        - NoneChunkingConfig
          - `mode` 'none'
        - CharacterChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'character'
        - TokenChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'token'
          - `separator` string
        - SentenceChunkingConfig
          - `chunk_size` integer
          - `chunk_overlap` integer
          - `mode` 'sentence'
          - `separator` string
          - `paragraph_separator` string
        - SemanticChunkingConfig
          - `mode` 'semantic'
          - `buffer_size` integer
          - `breakpoint_percentile_threshold` integer
  - `preset_retrieval_parameters` PresetRetrievalParams — Schema for the search params for an retrieval execution that can be preset for a pipeline.
    - `dense_similarity_top_k` integer, nullable — Number of nodes for dense retrieval.
    - `dense_similarity_cutoff` number, nullable — Minimum similarity score wrt query for retrieval
    - `sparse_similarity_top_k` integer, nullable — Number of nodes for sparse retrieval.
    - `enable_reranking` boolean, nullable — Enable reranking for retrieval
    - `rerank_top_n` integer, nullable — Number of reranked nodes for returning.
    - `alpha` number, nullable — Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
    - `search_filters` MetadataFilters — Metadata filters for vector stores.
      - `filters` union[], required
        - union
          - MetadataFilter — 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, required
            - `value` union, required
              - …
            - `operator` '==' | '>' | '<' | '!=' | '>=' | '<=' | 'in' | 'nin' | 'any' | 'all' | 'text_match' | 'text_match_insensitive' | 'contains' | 'is_empty' — Vector store filter operator.
          - MetadataFilters — recursive
      - `condition` 'and' | 'or' | 'not' — Vector store filter conditions to combine different filters.
    - `search_filters_inference_schema` object, nullable — JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.
    - `files_top_k` integer, nullable — Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
    - `retrieval_mode` 'chunks' | 'files_via_metadata' | 'files_via_content' | 'auto_routed'
    - `retrieve_image_nodes` boolean — Whether to retrieve image nodes.
    - `retrieve_page_screenshot_nodes` boolean — Whether to retrieve page screenshot nodes.
    - `retrieve_page_figure_nodes` boolean — Whether to retrieve page figure nodes.
    - `class_name` string
  - `llama_parse_parameters` LlamaParseParameters
    - `webhook_configurations` WebhookConfiguration[], nullable — Outbound webhook endpoints to notify on job status changes
      - `webhook_url` string, nullable — URL to receive webhook POST notifications
      - `webhook_headers` object, nullable — Custom HTTP headers sent with each webhook request (e.g. auth tokens)
      - `webhook_events` string[], nullable — Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.
      - `webhook_output_format` string, nullable — Response format sent to the webhook: 'string' (default) or 'json'
    - `priority` 'low' | 'medium' | 'high' | 'critical', nullable — The priority for the request. This field may be ignored or overwritten depending on the organization tier.
    - `languages` ParserLanguages[]
    - `parsing_instruction` string, nullable
    - `disable_ocr` boolean, nullable
    - `annotate_links` boolean, nullable
    - `adaptive_long_table` boolean, nullable
    - `compact_markdown_table` boolean, nullable
    - `disable_reconstruction` boolean, nullable
    - `disable_image_extraction` boolean, nullable
    - `invalidate_cache` boolean, nullable
    - `outlined_table_extraction` boolean, nullable
    - `aggressive_table_extraction` boolean, nullable
    - `merge_tables_across_pages_in_markdown` boolean, nullable
    - `output_pdf_of_document` boolean, nullable
    - `do_not_cache` boolean, nullable
    - `fast_mode` boolean, nullable
    - `skip_diagonal_text` boolean, nullable
    - `preserve_layout_alignment_across_pages` boolean, nullable
    - `preserve_very_small_text` boolean, nullable
    - `gpt4o_mode` boolean, nullable
    - `gpt4o_api_key` string, nullable
    - `do_not_unroll_columns` boolean, nullable
    - `extract_layout` boolean, nullable
    - `high_res_ocr` boolean, nullable
    - `html_make_all_elements_visible` boolean, nullable
    - `layout_aware` boolean, nullable
    - `specialized_chart_parsing_agentic` boolean, nullable
    - `specialized_chart_parsing_plus` boolean, nullable
    - `specialized_chart_parsing_efficient` boolean, nullable
    - `specialized_image_parsing` boolean, nullable
    - `precise_bounding_box` boolean, nullable
    - `line_level_bounding_box` boolean, nullable
    - `html_remove_navigation_elements` boolean, nullable
    - `html_remove_fixed_elements` boolean, nullable
    - `guess_xlsx_sheet_name` boolean, nullable
    - `page_separator` string, nullable
    - `bounding_box` string, nullable
    - `bbox_top` number, nullable
    - `bbox_right` number, nullable
    - `bbox_bottom` number, nullable
    - `bbox_left` number, nullable
    - `target_pages` string, nullable
    - `use_vendor_multimodal_model` boolean, nullable
    - `vendor_multimodal_model_name` string, nullable
    - `model` string, nullable
    - `vendor_multimodal_api_key` string, nullable
    - `page_prefix` string, nullable
    - `page_suffix` string, nullable
    - `webhook_url` string, nullable
    - `preset` string, nullable
    - `take_screenshot` boolean, nullable
    - `is_formatting_instruction` boolean, nullable
    - `premium_mode` boolean, nullable
    - `continuous_mode` boolean, nullable
    - `input_s3_path` string, nullable
    - `input_s3_region` string, nullable
    - `output_s3_path_prefix` string, nullable
    - `output_s3_region` string, nullable
    - `project_id` string, nullable
    - `azure_openai_deployment_name` string, nullable
    - `azure_openai_endpoint` string, nullable
    - `azure_openai_api_version` string, nullable
    - `azure_openai_key` string, nullable
    - `input_url` string, nullable
    - `http_proxy` string, nullable
    - `auto_mode` boolean, nullable
    - `auto_mode_trigger_on_regexp_in_page` string, nullable
    - `auto_mode_trigger_on_text_in_page` string, nullable
    - `auto_mode_trigger_on_table_in_page` boolean, nullable
    - `auto_mode_trigger_on_image_in_page` boolean, nullable
    - `auto_mode_configuration_json` string, nullable
    - `structured_output` boolean, nullable
    - `structured_output_json_schema` string, nullable
    - `structured_output_json_schema_name` string, nullable
    - `max_pages` integer, nullable
    - `max_pages_enforced` integer, nullable
    - `extract_charts` boolean, nullable
    - `formatting_instruction` string, nullable
    - `complemental_formatting_instruction` string, nullable
    - `content_guideline_instruction` string, nullable
    - `spreadsheet_extract_sub_tables` boolean, nullable
    - `spreadsheet_force_formula_computation` boolean, nullable
    - `spreadsheet_include_hidden_sheets` boolean, nullable
    - `inline_images_in_markdown` boolean, nullable
    - `job_timeout_in_seconds` number, nullable
    - `job_timeout_extra_time_per_page_in_seconds` number, nullable
    - `strict_mode_image_extraction` boolean, nullable
    - `strict_mode_image_ocr` boolean, nullable
    - `strict_mode_reconstruction` boolean, nullable
    - `strict_mode_buggy_font` boolean, nullable
    - `save_images` boolean, nullable
    - `images_to_save` string[], nullable
    - `hide_headers` boolean, nullable
    - `hide_footers` boolean, nullable
    - `page_header_prefix` string, nullable
    - `page_header_suffix` string, nullable
    - `page_footer_prefix` string, nullable
    - `page_footer_suffix` string, nullable
    - `remove_hidden_text` boolean, nullable
    - `keep_page_separator_when_merging_tables` boolean, nullable
    - `ignore_document_elements_for_layout_detection` boolean, nullable
    - `output_tables_as_HTML` boolean, nullable
    - `internal_is_screenshot_job` boolean, nullable
    - `parse_mode` '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' — Enum for representing the mode of parsing to be used.
    - `system_prompt` string, nullable
    - `system_prompt_append` string, nullable
    - `user_prompt` string, nullable
    - `page_error_tolerance` number, nullable
    - `replace_failed_page_mode` 'raw_text' | 'blank_page' | 'error_message' — Enum for representing the different available page error handling modes.
    - `replace_failed_page_with_error_message_prefix` string, nullable
    - `replace_failed_page_with_error_message_suffix` string, nullable
    - `markdown_table_multiline_header_separator` string, nullable
    - `presentation_out_of_bounds_content` boolean, nullable
    - `presentation_skip_embedded_data` boolean, nullable
    - `tier` string, nullable
    - `version` string, nullable
    - `extract_printed_page_number` boolean, nullable
    - `enable_cost_optimizer` boolean, nullable
  - `data_sink` DataSink — Schema for a data sink.
    - `id` string, uuid, required — Unique identifier
    - `created_at` string, date-time, nullable — Creation datetime
    - `updated_at` string, date-time, nullable — Update datetime
    - `name` string, required — The name of the data sink.
    - `sink_type` 'PINECONE' | 'POSTGRES' | 'QDRANT' | 'AZUREAI_SEARCH' | 'MONGODB_ATLAS' | 'MILVUS' | 'ASTRA_DB', required
    - `component` union, required — Component that implements the data sink
      - object
      - CloudPineconeVectorStore — Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion
        - `supports_nested_metadata_filters` true
        - `api_key` string, password, required — The API key for authenticating with Pinecone
        - `index_name` string, required
        - `namespace` string, nullable
        - `insert_kwargs` object, nullable
        - `class_name` string
      - CloudPostgresVectorStore
        - `supports_nested_metadata_filters` boolean
        - `database` string, required
        - `host` string, required
        - `password` string, password, required
        - `port` integer, required
        - `user` string, required
        - `table_name` string, required
        - `schema_name` string, required
        - `embed_dim` integer, required
        - `hybrid_search` boolean, nullable
        - `perform_setup` boolean
        - `hnsw_settings` PGVectorHNSWSettings — HNSW settings for PGVector.
          - `ef_construction` integer — The number of edges to use during the construction phase.
          - `ef_search` integer — The number of edges to use during the search phase.
          - `m` integer — The number of bi-directional links created for each new element.
          - `vector_type` 'vector' | 'half_vec' | 'bit' | 'sparse_vec' — Vector storage formats for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options
          - `distance_method` 'l2' | 'ip' | 'cosine' | 'l1' | 'hamming' | 'jaccard' — Distance methods for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options
        - `class_name` string
      - CloudQdrantVectorStore — Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client
        - `supports_nested_metadata_filters` true
        - `collection_name` string, required
        - `url` string, required
        - `api_key` string, password, required
        - `max_retries` integer
        - `client_kwargs` object
        - `class_name` string
      - CloudAzureAISearchVectorStore — Cloud Azure AI Search Vector Store.
        - `supports_nested_metadata_filters` true
        - `search_service_api_key` string, password, required
        - `search_service_endpoint` string, required
        - `search_service_api_version` string, nullable
        - `index_name` string, nullable
        - `filterable_metadata_field_keys` object, nullable
        - `embedding_dimension` integer, nullable
        - `client_id` string, nullable
        - `client_secret` string, password, nullable
        - `tenant_id` string, nullable
        - `class_name` string
      - CloudMongoDBAtlasVectorSearch — Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index
        - `supports_nested_metadata_filters` boolean
        - `mongodb_uri` string, password, required
        - `db_name` string, required
        - `collection_name` string, required
        - `vector_index_name` string, nullable
        - `fulltext_index_name` string, nullable
        - `embedding_dimension` integer, nullable
        - `class_name` string
      - CloudMilvusVectorStore — Cloud Milvus Vector Store.
        - `supports_nested_metadata_filters` boolean
        - `uri` string, required
        - `collection_name` string, nullable
        - `token` string, password, nullable
        - `embedding_dimension` integer, nullable
        - `class_name` string
      - CloudAstraDBVectorStore — Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'
        - `supports_nested_metadata_filters` true
        - `token` string, password, required — The Astra DB Application Token to use
        - `api_endpoint` string, required — The Astra DB JSON API endpoint for your database
        - `collection_name` string, required — Collection name to use. If not existing, it will be created
        - `embedding_dimension` integer, required — Length of the embedding vectors in use
        - `keyspace` string, nullable — The keyspace to use. If not provided, 'default_keyspace'
        - `class_name` string
    - `project_id` string, uuid, required
  - `status` 'CREATED' | 'DELETING', nullable — Status of the pipeline.
  - `metadata_config` PipelineMetadataConfig
    - `excluded_embed_metadata_keys` string[] — List of metadata keys to exclude from embeddings
    - `excluded_llm_metadata_keys` string[] — List of metadata keys to exclude from LLM during retrieval

## Other responses

- `422` — Validation Error

---

[API](https://skmtc.net/run-llama/apis/llama-platform.md) · [All operations](https://skmtc.net/run-llama/apis/llama-platform/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/run-llama/llama-platform/versions/b17341164de9/schema)