## Get Data Sink `$ llamacloud-prod data-sinks get` **get** `/api/v1/data-sinks/{data_sink_id}` Get a data sink by ID. ### Parameters - `--data-sink-id: string` ### Returns - `data_sink: 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 ### Example ```cli llamacloud-prod data-sinks get \ --api-key 'My API Key' \ --data-sink-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e ``` #### Response ```json { "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" } ```