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Data Sinks

Create Data Sink

DataSink dataSinks().create(DataSinkCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())
POST/api/v1/data-sinks

Create a new data sink.

ParametersExpand Collapse
DataSinkCreateParams params
Optional<String> organizationId
Optional<String> projectId
DataSinkCreate dataSinkCreate

Schema for creating a data sink.

ReturnsExpand Collapse
class DataSink:

Schema for a data sink.

String id

Unique identifier

formatuuid
Component component

Component that implements the data sink

One of the following:
class UnionMember0:
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

String apiKey

The API key for authenticating with Pinecone

formatpassword
String indexName
Optional<String> className
Optional<InsertKwargs> insertKwargs
Optional<String> namespace
Optional<SupportsNestedMetadataFilters> supportsNestedMetadataFilters
class CloudPostgresVectorStore:
String database
long embedDim
String host
String password
long port
String schemaName
String tableName
String user
Optional<String> className
Optional<PgVectorHnswSettings> hnswSettings

HNSW settings for PGVector.

Optional<DistanceMethod> distanceMethod

The distance method to use.

One of the following:
COSINE("cosine")
HAMMING("hamming")
IP("ip")
JACCARD("jaccard")
L1("l1")
L2("l2")
Optional<Long> efConstruction

The number of edges to use during the construction phase.

minimum1

The number of edges to use during the search phase.

minimum1
Optional<Long> m

The number of bi-directional links created for each new element.

minimum1
Optional<VectorType> vectorType

The type of vector to use.

One of the following:
BIT("bit")
HALF_VEC("half_vec")
SPARSE_VEC("sparse_vec")
VECTOR("vector")
Optional<Boolean> performSetup
Optional<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

String apiKey
String collectionName
String url
Optional<String> className
Optional<ClientKwargs> clientKwargs
Optional<Long> maxRetries
Optional<SupportsNestedMetadataFilters> supportsNestedMetadataFilters
class CloudAzureAiSearchVectorStore:

Cloud Azure AI Search Vector Store.

String searchServiceApiKey
String searchServiceEndpoint
Optional<String> className
Optional<String> clientId
Optional<String> clientSecret
Optional<Long> embeddingDimension
Optional<FilterableMetadataFieldKeys> filterableMetadataFieldKeys
Optional<String> indexName
Optional<String> searchServiceApiVersion
Optional<SupportsNestedMetadataFilters> supportsNestedMetadataFilters
Optional<String> tenantId

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

class CloudMilvusVectorStore:

Cloud Milvus Vector Store.

String uri
Optional<String> token
Optional<String> className
Optional<String> collectionName
Optional<Long> embeddingDimension
Optional<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’

String token

The Astra DB Application Token to use

formatpassword
String apiEndpoint

The Astra DB JSON API endpoint for your database

String collectionName

Collection name to use. If not existing, it will be created

long embeddingDimension

Length of the embedding vectors in use

Optional<String> className
Optional<String> keyspace

The keyspace to use. If not provided, ‘default_keyspace’

Optional<SupportsNestedMetadataFilters> supportsNestedMetadataFilters
String name

The name of the data sink.

String projectId
SinkType sinkType
One of the following:
ASTRA_DB("ASTRA_DB")
AZUREAI_SEARCH("AZUREAI_SEARCH")
MILVUS("MILVUS")
MONGODB_ATLAS("MONGODB_ATLAS")
PINECONE("PINECONE")
POSTGRES("POSTGRES")
QDRANT("QDRANT")
Optional<LocalDateTime> createdAt

Creation datetime

formatdate-time
Optional<LocalDateTime> updatedAt

Update datetime

formatdate-time

Create Data Sink

package ai.llamaindex.llamacloud.example;

import ai.llamaindex.llamacloud.client.LlamaCloudClient;
import ai.llamaindex.llamacloud.client.okhttp.LlamaCloudOkHttpClient;
import ai.llamaindex.llamacloud.core.JsonValue;
import ai.llamaindex.llamacloud.models.datasinks.DataSink;
import ai.llamaindex.llamacloud.models.datasinks.DataSinkCreateParams;
import ai.llamaindex.llamacloud.models.pipelines.DataSinkCreate;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        LlamaCloudClient client = LlamaCloudOkHttpClient.fromEnv();

        DataSinkCreate params = DataSinkCreate.builder()
            .component(DataSinkCreate.Component.UnionMember0.builder()
                .putAdditionalProperty("foo", JsonValue.from("bar"))
                .build())
            .name("name")
            .sinkType(DataSinkCreate.SinkType.ASTRA_DB)
            .build();
        DataSink dataSink = client.dataSinks().create(params);
    }
}
{
  "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"
}
Returns Examples
{
  "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"
}