# Data Sinks ## List Data Sinks `data_sinks.list(DataSinkListParams**kwargs) -> DataSinkListResponse` **get** `/api/v1/data-sinks` List data sinks for a given project. ### Parameters - `organization_id: Optional[str]` - `project_id: Optional[str]` ### Returns - `List[DataSink]` - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime ### Example ```python import os from llama_cloud import LlamaCloud client = LlamaCloud( api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted ) data_sinks = client.data_sinks.list() print(data_sinks) ``` #### 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" } ] ``` ## Create Data Sink `data_sinks.create(DataSinkCreateParams**kwargs) -> DataSink` **post** `/api/v1/data-sinks` Create a new data sink. ### Parameters - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `organization_id: Optional[str]` - `project_id: Optional[str]` ### Returns - `class DataSink: …` Schema for a data sink. - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime ### Example ```python import os from llama_cloud import LlamaCloud client = LlamaCloud( api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted ) data_sink = client.data_sinks.create( component={ "foo": "bar" }, name="name", sink_type="PINECONE", ) print(data_sink.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" } ``` ## Get Data Sink `data_sinks.get(strdata_sink_id) -> DataSink` **get** `/api/v1/data-sinks/{data_sink_id}` Get a data sink by ID. ### Parameters - `data_sink_id: str` ### Returns - `class DataSink: …` Schema for a data sink. - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime ### Example ```python import os from llama_cloud import LlamaCloud client = LlamaCloud( api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted ) data_sink = client.data_sinks.get( "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", ) print(data_sink.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" } ``` ## Update Data Sink `data_sinks.update(strdata_sink_id, DataSinkUpdateParams**kwargs) -> DataSink` **put** `/api/v1/data-sinks/{data_sink_id}` Update a data sink by ID. ### Parameters - `data_sink_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `component: Optional[Component]` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: Optional[str]` The name of the data sink. ### Returns - `class DataSink: …` Schema for a data sink. - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime ### Example ```python import os from llama_cloud import LlamaCloud client = LlamaCloud( api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted ) data_sink = client.data_sinks.update( data_sink_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", sink_type="PINECONE", ) print(data_sink.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" } ``` ## Delete Data Sink `data_sinks.delete(strdata_sink_id)` **delete** `/api/v1/data-sinks/{data_sink_id}` Delete a data sink by ID. ### Parameters - `data_sink_id: str` ### Example ```python import os from llama_cloud import LlamaCloud client = LlamaCloud( api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted ) client.data_sinks.delete( "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", ) ``` ## Domain Types ### Data Sink - `class DataSink: …` Schema for a data sink. - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime ### Data Sink List Response - `List[DataSink]` - `id: str` Unique identifier - `component: Component` Component that implements the data sink - `Dict[str, object]` - `class 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: str` The API key for authenticating with Pinecone - `index_name: str` - `class_name: Optional[str]` - `insert_kwargs: Optional[Dict[str, object]]` - `namespace: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudPostgresVectorStore: …` - `database: str` - `embed_dim: int` - `host: str` - `password: str` - `port: int` - `schema_name: str` - `table_name: str` - `user: str` - `class_name: Optional[str]` - `hnsw_settings: Optional[PgVectorHnswSettings]` HNSW settings for PGVector. - `distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: Optional[int]` The number of edges to use during the construction phase. - `ef_search: Optional[int]` The number of edges to use during the search phase. - `m: Optional[int]` The number of bi-directional links created for each new element. - `vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: Optional[bool]` - `perform_setup: Optional[bool]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: str` - `collection_name: str` - `url: str` - `class_name: Optional[str]` - `client_kwargs: Optional[Dict[str, object]]` - `max_retries: Optional[int]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `class CloudAzureAISearchVectorStore: …` Cloud Azure AI Search Vector Store. - `search_service_api_key: str` - `search_service_endpoint: str` - `class_name: Optional[str]` - `client_id: Optional[str]` - `client_secret: Optional[str]` - `embedding_dimension: Optional[int]` - `filterable_metadata_field_keys: Optional[Dict[str, object]]` - `index_name: Optional[str]` - `search_service_api_version: Optional[str]` - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `tenant_id: Optional[str]` - `class 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: str` - `db_name: str` - `mongodb_uri: str` - `class_name: Optional[str]` - `embedding_dimension: Optional[int]` - `fulltext_index_name: Optional[str]` - `supports_nested_metadata_filters: Optional[bool]` - `vector_index_name: Optional[str]` - `class CloudMilvusVectorStore: …` Cloud Milvus Vector Store. - `uri: str` - `token: Optional[str]` - `class_name: Optional[str]` - `collection_name: Optional[str]` - `embedding_dimension: Optional[int]` - `supports_nested_metadata_filters: Optional[bool]` - `class 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: 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 - `class_name: Optional[str]` - `keyspace: Optional[str]` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: Optional[Literal[true]]` - `true` - `name: str` The name of the data sink. - `project_id: str` - `sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: Optional[datetime]` Creation datetime - `updated_at: Optional[datetime]` Update datetime