---
title: "Get Data Sink"
method: GET
path: "/api/v1/data-sinks/{data_sink_id}"
tags: ["Data Sinks"]
---

# Get Data Sink

`GET /api/v1/data-sinks/{data_sink_id}`

Get a data sink by ID.

## Path parameters

- `data_sink_id` string, uuid, required

## Cookies

- `session` string, nullable

## Response `200`

Successful Response

- 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

## Other responses

- `422` — Validation Error

---

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