## Update Data Sink `client.dataSinks.update(stringdataSinkID, DataSinkUpdateParamsbody, RequestOptionsoptions?): DataSink` **put** `/api/v1/data-sinks/{data_sink_id}` Update a data sink by ID. ### Parameters - `dataSinkID: string` - `body: DataSinkUpdateParams` - `sink_type: "PINECONE" | "POSTGRES" | "QDRANT" | 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `component?: Record | CloudPineconeVectorStore | CloudPostgresVectorStore | 5 more | null` Component that implements the data sink - `Record` - `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 - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name?: string` - `insert_kwargs?: Record | null` - `namespace?: string | null` - `supports_nested_metadata_filters?: true` - `true` - `CloudPostgresVectorStore` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name?: string` - `hnsw_settings?: PgVectorHnswSettings | null` HNSW settings for PGVector. - `distance_method?: "l2" | "ip" | "cosine" | 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction?: number` The number of edges to use during the construction phase. - `ef_search?: number` The number of edges to use during the search phase. - `m?: number` The number of bi-directional links created for each new element. - `vector_type?: "vector" | "half_vec" | "bit" | "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search?: boolean | null` - `perform_setup?: boolean` - `supports_nested_metadata_filters?: boolean` - `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 - `api_key: string` - `collection_name: string` - `url: string` - `class_name?: string` - `client_kwargs?: Record` - `max_retries?: number` - `supports_nested_metadata_filters?: true` - `true` - `CloudAzureAISearchVectorStore` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name?: string` - `client_id?: string | null` - `client_secret?: string | null` - `embedding_dimension?: number | null` - `filterable_metadata_field_keys?: Record | null` - `index_name?: string | null` - `search_service_api_version?: string | null` - `supports_nested_metadata_filters?: true` - `true` - `tenant_id?: string | null` - `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 - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name?: string` - `embedding_dimension?: number | null` - `fulltext_index_name?: string | null` - `supports_nested_metadata_filters?: boolean` - `vector_index_name?: string | null` - `CloudMilvusVectorStore` Cloud Milvus Vector Store. - `uri: string` - `token?: string | null` - `class_name?: string` - `collection_name?: string | null` - `embedding_dimension?: number | null` - `supports_nested_metadata_filters?: boolean` - `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' - `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?: string` - `keyspace?: string | null` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters?: true` - `true` - `name?: string | null` The name of the data sink. ### Returns - `DataSink` Schema for a data sink. - `id: string` Unique identifier - `component: Record | CloudPineconeVectorStore | CloudPostgresVectorStore | 5 more` Component that implements the data sink - `Record` - `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 - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name?: string` - `insert_kwargs?: Record | null` - `namespace?: string | null` - `supports_nested_metadata_filters?: true` - `true` - `CloudPostgresVectorStore` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name?: string` - `hnsw_settings?: PgVectorHnswSettings | null` HNSW settings for PGVector. - `distance_method?: "l2" | "ip" | "cosine" | 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction?: number` The number of edges to use during the construction phase. - `ef_search?: number` The number of edges to use during the search phase. - `m?: number` The number of bi-directional links created for each new element. - `vector_type?: "vector" | "half_vec" | "bit" | "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search?: boolean | null` - `perform_setup?: boolean` - `supports_nested_metadata_filters?: boolean` - `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 - `api_key: string` - `collection_name: string` - `url: string` - `class_name?: string` - `client_kwargs?: Record` - `max_retries?: number` - `supports_nested_metadata_filters?: true` - `true` - `CloudAzureAISearchVectorStore` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name?: string` - `client_id?: string | null` - `client_secret?: string | null` - `embedding_dimension?: number | null` - `filterable_metadata_field_keys?: Record | null` - `index_name?: string | null` - `search_service_api_version?: string | null` - `supports_nested_metadata_filters?: true` - `true` - `tenant_id?: string | null` - `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 - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name?: string` - `embedding_dimension?: number | null` - `fulltext_index_name?: string | null` - `supports_nested_metadata_filters?: boolean` - `vector_index_name?: string | null` - `CloudMilvusVectorStore` Cloud Milvus Vector Store. - `uri: string` - `token?: string | null` - `class_name?: string` - `collection_name?: string | null` - `embedding_dimension?: number | null` - `supports_nested_metadata_filters?: boolean` - `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' - `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?: string` - `keyspace?: string | null` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters?: true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" | "POSTGRES" | "QDRANT" | 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at?: string | null` Creation datetime - `updated_at?: string | null` Update datetime ### Example ```typescript import LlamaCloud from '@llamaindex/llama-cloud'; const client = new LlamaCloud({ apiKey: process.env['LLAMA_CLOUD_API_KEY'], // This is the default and can be omitted }); const dataSink = await client.dataSinks.update('182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e', { sink_type: 'PINECONE', }); console.log(dataSink.id); ``` #### 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" } ```