---
title: "List Data Sinks"
method: GET
path: "/api/v1/beta/data-sinks"
tags: ["Beta", "Data Sinks"]
---

# List Data Sinks

`GET /api/v1/beta/data-sinks`

## Request

- Base URL: `https://api.cloud.llamaindex.ai` (the document declares no server; this is the origin it was published from)
- URL: `https://api.cloud.llamaindex.ai/api/v1/beta/data-sinks`
- Auth: HTTP bearer

List the data sinks in a project, newest first.

## Query parameters

- `page_size` integer: Number of items per page
- `page_token` string, nullable: Cursor from the previous page's `next_page_token`.
- `include_total` boolean: Return `total_size`, a count of every row matching the filter. It is a second query on every page, so it is off unless asked for.
- `project_id` string, uuid, nullable
- `organization_id` string, uuid, nullable

## Cookies

- `session` string, nullable

## Response `200`

Successful Response

- DataSinkQueryResponse: Paginated list of data sinks.
  - `items` DataSink[], required: The list of items.
    - `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
  - `next_page_token` string, nullable: A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
  - `total_size` integer, nullable: The total number of items available. This is only populated when specifically requested. The value may be an estimate and can be used for display purposes only.

## Other responses

- `422`: Validation Error

## Changes

- **2026-10-16** `a38b684f8e1b`: 1 info
  - endpoint added

[Operation history](https://skmtc.dev/run-llama/apis/llama-platform/changes/api/v1/beta/data-sinks/get.md)

---

[API](https://skmtc.dev/run-llama/apis/llama-platform.md) · [All operations](https://skmtc.dev/run-llama/apis/llama-platform/llms.txt) · [OpenAPI document](https://skmtc.dev/run-llama/apis/llama-platform/revisions/809d302cdb1e?raw)