## List Data Sinks `DataSinkListPaginatedPageResponse DataSinks.ListPaginated(DataSinkListPaginatedParams?parameters, CancellationTokencancellationToken = default)` **get** `/api/v1/beta/data-sinks` List the data sinks in a project, newest first. ### Parameters - `DataSinkListPaginatedParams parameters` - `Boolean includeTotal` 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. - `string? organizationID` - `Long pageSize` Number of items per page - `string? pageToken` Cursor from the previous page's `next_page_token`. - `string? projectID` ### Returns - `class DataSinkListPaginatedPageResponse:` Paginated list of data sinks. - `required IReadOnlyList Items` The list of items. - `required string ID` Unique identifier - `required Component Component` Component that implements the data sink - `IReadOnlyDictionary` - `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 - `required string ApiKey` The API key for authenticating with Pinecone - `required string IndexName` - `string ClassName` - `IReadOnlyDictionary? InsertKwargs` - `string? Namespace` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `class CloudPostgresVectorStore:` - `required string Database` - `required Long EmbedDim` - `required string Host` - `required string Password` - `required Long Port` - `required string SchemaName` - `required string TableName` - `required string User` - `string ClassName` - `PgVectorHnswSettings? HnswSettings` HNSW settings for PGVector. - `DistanceMethod DistanceMethod` The distance method to use. - `"cosine"Cosine` - `"hamming"Hamming` - `"ip"IP` - `"jaccard"Jaccard` - `"l1"L1` - `"l2"L2` - `Long EfConstruction` The number of edges to use during the construction phase. - `Long EfSearch` The number of edges to use during the search phase. - `Long M` The number of bi-directional links created for each new element. - `VectorType VectorType` The type of vector to use. - `"bit"Bit` - `"half_vec"HalfVec` - `"sparse_vec"SparseVec` - `"vector"Vector` - `Boolean? HybridSearch` - `Boolean PerformSetup` - `Boolean SupportsNestedMetadataFilters` - `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 - `required string ApiKey` - `required string CollectionName` - `required string Url` - `string ClassName` - `IReadOnlyDictionary ClientKwargs` - `Long MaxRetries` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `class CloudAzureAISearchVectorStore:` Cloud Azure AI Search Vector Store. - `required string SearchServiceApiKey` - `required string SearchServiceEndpoint` - `string ClassName` - `string? ClientID` - `string? ClientSecret` - `Long? EmbeddingDimension` - `IReadOnlyDictionary? FilterableMetadataFieldKeys` - `string? IndexName` - `string? SearchServiceApiVersion` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `string? TenantID` - `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 - `required string CollectionName` - `required string DBName` - `required string MongoDBUri` - `string ClassName` - `Long? EmbeddingDimension` - `string? FulltextIndexName` - `Boolean SupportsNestedMetadataFilters` - `string? VectorIndexName` - `class CloudMilvusVectorStore:` Cloud Milvus Vector Store. - `required string Uri` - `string? Token` - `string ClassName` - `string? CollectionName` - `Long? EmbeddingDimension` - `Boolean SupportsNestedMetadataFilters` - `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' - `required string Token` The Astra DB Application Token to use - `required string ApiEndpoint` The Astra DB JSON API endpoint for your database - `required string CollectionName` Collection name to use. If not existing, it will be created - `required Long EmbeddingDimension` Length of the embedding vectors in use - `string ClassName` - `string? Keyspace` The keyspace to use. If not provided, 'default_keyspace' - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `required string Name` The name of the data sink. - `required string ProjectID` - `required SinkType SinkType` - `"ASTRA_DB"AstraDB` - `"AZUREAI_SEARCH"AzureaiSearch` - `"MILVUS"Milvus` - `"MONGODB_ATLAS"MongoDBAtlas` - `"PINECONE"Pinecone` - `"POSTGRES"Postgres` - `"QDRANT"Qdrant` - `DateTimeOffset? CreatedAt` Creation datetime - `DateTimeOffset? UpdatedAt` Update datetime - `string? NextPageToken` A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. - `Long? TotalSize` 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. ### Example ```csharp DataSinkListPaginatedParams parameters = new(); var page = await client.DataSinks.ListPaginated(parameters); await foreach (var item in page.Paginate()) { Console.WriteLine(item); } ``` #### Response ```json { "items": [ { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "component": { "foo": "bar" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "sink_type": "ASTRA_DB", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" } ], "next_page_token": "next_page_token", "total_size": 0 } ```