API Reference

Data Sinks

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Update Data Sink

DataSink dataSinks().update(DataSinkUpdateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())

PUT/api/v1/data-sinks/{data_sink_id}

Update a data sink by ID.

ParametersExpand Collapse

DataSinkUpdateParams params

Optional<String> dataSinkId

SinkType sinkType

PINECONE("PINECONE")

POSTGRES("POSTGRES")

QDRANT("QDRANT")

AZUREAI_SEARCH("AZUREAI_SEARCH")

MONGODB_ATLAS("MONGODB_ATLAS")

MILVUS("MILVUS")

ASTRA_DB("ASTRA_DB")

Optional<Component> component

Component that implements the data sink

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:

L2("l2")

IP("ip")

COSINE("cosine")

L1("l1")

HAMMING("hamming")

JACCARD("jaccard")

Optional<Long> efConstruction

The number of edges to use during the construction phase.

minimum1

Optional<Long> efSearch

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:

VECTOR("vector")

HALF_VEC("half_vec")

BIT("bit")

SPARSE_VEC("sparse_vec")

Optional<Boolean> hybridSearch

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

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

String collectionName

String dbName

String mongoDBUri

Optional<String> className

Optional<Long> embeddingDimension

Optional<String> fulltextIndexName

Optional<Boolean> supportsNestedMetadataFilters

Optional<String> vectorIndexName

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

Optional<String> name

The name of the 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:

L2("l2")

IP("ip")

COSINE("cosine")

L1("l1")

HAMMING("hamming")

JACCARD("jaccard")

Optional<Long> efConstruction

The number of edges to use during the construction phase.

minimum1

Optional<Long> efSearch

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:

VECTOR("vector")

HALF_VEC("half_vec")

BIT("bit")

SPARSE_VEC("sparse_vec")

Optional<Boolean> hybridSearch

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

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

String collectionName

String dbName

String mongoDBUri

Optional<String> className

Optional<Long> embeddingDimension

Optional<String> fulltextIndexName

Optional<Boolean> supportsNestedMetadataFilters

Optional<String> vectorIndexName

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:

PINECONE("PINECONE")

POSTGRES("POSTGRES")

QDRANT("QDRANT")

AZUREAI_SEARCH("AZUREAI_SEARCH")

MONGODB_ATLAS("MONGODB_ATLAS")

MILVUS("MILVUS")

ASTRA_DB("ASTRA_DB")

Optional<LocalDateTime> createdAt

Creation datetime

formatdate-time

Optional<LocalDateTime> updatedAt

Update datetime

formatdate-time

Update Data Sink

Java

HTTP

TypeScript

Python

Java

Go

CLI Tool

package com.llamacloud_prod.api.example;

import com.llamacloud_prod.api.client.LlamaCloudClient;
import com.llamacloud_prod.api.client.okhttp.LlamaCloudOkHttpClient;
import com.llamacloud_prod.api.models.datasinks.DataSink;
import com.llamacloud_prod.api.models.datasinks.DataSinkUpdateParams;

public final class Main {
    private Main() {}

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

        DataSinkUpdateParams params = DataSinkUpdateParams.builder()
            .dataSinkId("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
            .sinkType(DataSinkUpdateParams.SinkType.PINECONE)
            .build();
        DataSink dataSink = client.dataSinks().update(params);
    }
}

200 example

{
  "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"
}

Returns Examples

200 example

{
  "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"
}