## Get Data Sink

`DataSink DataSinks.Get(DataSinkGetParamsparameters, CancellationTokencancellationToken = default)`

**get** `/api/v1/data-sinks/{data_sink_id}`

Get a data sink by ID.

### Parameters

- `DataSinkGetParams parameters`

  - `required string dataSinkID`

### Returns

- `class DataSink:`

  Schema for a data sink.

  - `required string ID`

    Unique identifier

  - `required Component Component`

    Component that implements the data sink

    - `IReadOnlyDictionary<string, JsonElement>`

    - `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<string, JsonElement>? 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<string, JsonElement> 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<string, JsonElement>? 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

### Example

```csharp
DataSinkGetParams parameters = new()
{
    DataSinkID = "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
};

var dataSink = await client.DataSinks.Get(parameters);

Console.WriteLine(dataSink);
```

#### Response

```json
{
  "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"
}
```