API Reference

Data Sinks

Copy Markdown

Open in Claude

Open in ChatGPT

Open in Cursor

---

Copy Markdown

View as Markdown

Update Data Sink

client.DataSinks.Update(ctx, dataSinkID, body) (*DataSink, error)

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

Update a data sink by ID.

ParametersExpand Collapse

dataSinkID string

body DataSinkUpdateParams

SinkType param.Field[DataSinkUpdateParamsSinkType]

const DataSinkUpdateParamsSinkTypePinecone DataSinkUpdateParamsSinkType = "PINECONE"

const DataSinkUpdateParamsSinkTypePostgres DataSinkUpdateParamsSinkType = "POSTGRES"

const DataSinkUpdateParamsSinkTypeQdrant DataSinkUpdateParamsSinkType = "QDRANT"

const DataSinkUpdateParamsSinkTypeAzureaiSearch DataSinkUpdateParamsSinkType = "AZUREAI_SEARCH"

const DataSinkUpdateParamsSinkTypeMongoDBAtlas DataSinkUpdateParamsSinkType = "MONGODB_ATLAS"

const DataSinkUpdateParamsSinkTypeMilvus DataSinkUpdateParamsSinkType = "MILVUS"

const DataSinkUpdateParamsSinkTypeAstraDB DataSinkUpdateParamsSinkType = "ASTRA_DB"

Component param.Field[DataSinkUpdateParamsComponentUnion]optional

Component that implements the data sink

type DataSinkUpdateParamsComponentMap map[string, any]

type CloudPineconeVectorStore struct{…}

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

APIKey string

The API key for authenticating with Pinecone

formatpassword

IndexName string

ClassName stringoptional

InsertKwargs map[string, any]optional

Namespace stringoptional

SupportsNestedMetadataFilters booloptional

type CloudPostgresVectorStore struct{…}

Database string

EmbedDim int64

Host string

Password string

Port int64

SchemaName string

TableName string

User string

ClassName stringoptional

HnswSettings PgVectorHnswSettingsoptional

HNSW settings for PGVector.

DistanceMethod PgVectorHnswSettingsDistanceMethodoptional

The distance method to use.

One of the following:

const PgVectorHnswSettingsDistanceMethodL2 PgVectorHnswSettingsDistanceMethod = "l2"

const PgVectorHnswSettingsDistanceMethodIP PgVectorHnswSettingsDistanceMethod = "ip"

const PgVectorHnswSettingsDistanceMethodCosine PgVectorHnswSettingsDistanceMethod = "cosine"

const PgVectorHnswSettingsDistanceMethodL1 PgVectorHnswSettingsDistanceMethod = "l1"

const PgVectorHnswSettingsDistanceMethodHamming PgVectorHnswSettingsDistanceMethod = "hamming"

const PgVectorHnswSettingsDistanceMethodJaccard PgVectorHnswSettingsDistanceMethod = "jaccard"

EfConstruction int64optional

The number of edges to use during the construction phase.

minimum1

EfSearch int64optional

The number of edges to use during the search phase.

minimum1

M int64optional

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

minimum1

VectorType PgVectorHnswSettingsVectorTypeoptional

The type of vector to use.

One of the following:

const PgVectorHnswSettingsVectorTypeVector PgVectorHnswSettingsVectorType = "vector"

const PgVectorHnswSettingsVectorTypeHalfVec PgVectorHnswSettingsVectorType = "half_vec"

const PgVectorHnswSettingsVectorTypeBit PgVectorHnswSettingsVectorType = "bit"

const PgVectorHnswSettingsVectorTypeSparseVec PgVectorHnswSettingsVectorType = "sparse_vec"

HybridSearch booloptional

PerformSetup booloptional

SupportsNestedMetadataFilters booloptional

type CloudQdrantVectorStore struct{…}

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

APIKey string

CollectionName string

URL string

ClassName stringoptional

ClientKwargs map[string, any]optional

MaxRetries int64optional

SupportsNestedMetadataFilters booloptional

type CloudAzureAISearchVectorStore struct{…}

Cloud Azure AI Search Vector Store.

SearchServiceAPIKey string

SearchServiceEndpoint string

ClassName stringoptional

ClientID stringoptional

ClientSecret stringoptional

EmbeddingDimension int64optional

FilterableMetadataFieldKeys map[string, any]optional

IndexName stringoptional

SearchServiceAPIVersion stringoptional

SupportsNestedMetadataFilters booloptional

TenantID stringoptional

type CloudMongoDBAtlasVectorSearch struct{…}

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

CollectionName string

DBName string

MongoDBUri string

ClassName stringoptional

EmbeddingDimension int64optional

FulltextIndexName stringoptional

SupportsNestedMetadataFilters booloptional

VectorIndexName stringoptional

type CloudMilvusVectorStore struct{…}

Cloud Milvus Vector Store.

Uri string

Token stringoptional

ClassName stringoptional

CollectionName stringoptional

EmbeddingDimension int64optional

SupportsNestedMetadataFilters booloptional

type CloudAstraDBVectorStore struct{…}

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 string

The Astra DB Application Token to use

formatpassword

APIEndpoint string

The Astra DB JSON API endpoint for your database

CollectionName string

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

EmbeddingDimension int64

Length of the embedding vectors in use

ClassName stringoptional

Keyspace stringoptional

The keyspace to use. If not provided, 'default_keyspace'

SupportsNestedMetadataFilters booloptional

Name param.Field[string]optional

The name of the data sink.

ReturnsExpand Collapse

type DataSink struct{…}

Schema for a data sink.

ID string

Unique identifier

formatuuid

Component DataSinkComponentUnion

Component that implements the data sink

One of the following:

type DataSinkComponentMap map[string, any]

type CloudPineconeVectorStore struct{…}

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

APIKey string

The API key for authenticating with Pinecone

formatpassword

IndexName string

ClassName stringoptional

InsertKwargs map[string, any]optional

Namespace stringoptional

SupportsNestedMetadataFilters booloptional

type CloudPostgresVectorStore struct{…}

Database string

EmbedDim int64

Host string

Password string

Port int64

SchemaName string

TableName string

User string

ClassName stringoptional

HnswSettings PgVectorHnswSettingsoptional

HNSW settings for PGVector.

DistanceMethod PgVectorHnswSettingsDistanceMethodoptional

The distance method to use.

One of the following:

const PgVectorHnswSettingsDistanceMethodL2 PgVectorHnswSettingsDistanceMethod = "l2"

const PgVectorHnswSettingsDistanceMethodIP PgVectorHnswSettingsDistanceMethod = "ip"

const PgVectorHnswSettingsDistanceMethodCosine PgVectorHnswSettingsDistanceMethod = "cosine"

const PgVectorHnswSettingsDistanceMethodL1 PgVectorHnswSettingsDistanceMethod = "l1"

const PgVectorHnswSettingsDistanceMethodHamming PgVectorHnswSettingsDistanceMethod = "hamming"

const PgVectorHnswSettingsDistanceMethodJaccard PgVectorHnswSettingsDistanceMethod = "jaccard"

EfConstruction int64optional

The number of edges to use during the construction phase.

minimum1

EfSearch int64optional

The number of edges to use during the search phase.

minimum1

M int64optional

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

minimum1

VectorType PgVectorHnswSettingsVectorTypeoptional

The type of vector to use.

One of the following:

const PgVectorHnswSettingsVectorTypeVector PgVectorHnswSettingsVectorType = "vector"

const PgVectorHnswSettingsVectorTypeHalfVec PgVectorHnswSettingsVectorType = "half_vec"

const PgVectorHnswSettingsVectorTypeBit PgVectorHnswSettingsVectorType = "bit"

const PgVectorHnswSettingsVectorTypeSparseVec PgVectorHnswSettingsVectorType = "sparse_vec"

HybridSearch booloptional

PerformSetup booloptional

SupportsNestedMetadataFilters booloptional

type CloudQdrantVectorStore struct{…}

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

APIKey string

CollectionName string

URL string

ClassName stringoptional

ClientKwargs map[string, any]optional

MaxRetries int64optional

SupportsNestedMetadataFilters booloptional

type CloudAzureAISearchVectorStore struct{…}

Cloud Azure AI Search Vector Store.

SearchServiceAPIKey string

SearchServiceEndpoint string

ClassName stringoptional

ClientID stringoptional

ClientSecret stringoptional

EmbeddingDimension int64optional

FilterableMetadataFieldKeys map[string, any]optional

IndexName stringoptional

SearchServiceAPIVersion stringoptional

SupportsNestedMetadataFilters booloptional

TenantID stringoptional

type CloudMongoDBAtlasVectorSearch struct{…}

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

CollectionName string

DBName string

MongoDBUri string

ClassName stringoptional

EmbeddingDimension int64optional

FulltextIndexName stringoptional

SupportsNestedMetadataFilters booloptional

VectorIndexName stringoptional

type CloudMilvusVectorStore struct{…}

Cloud Milvus Vector Store.

Uri string

Token stringoptional

ClassName stringoptional

CollectionName stringoptional

EmbeddingDimension int64optional

SupportsNestedMetadataFilters booloptional

type CloudAstraDBVectorStore struct{…}

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 string

The Astra DB Application Token to use

formatpassword

APIEndpoint string

The Astra DB JSON API endpoint for your database

CollectionName string

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

EmbeddingDimension int64

Length of the embedding vectors in use

ClassName stringoptional

Keyspace stringoptional

The keyspace to use. If not provided, 'default_keyspace'

SupportsNestedMetadataFilters booloptional

Name string

The name of the data sink.

ProjectID string

SinkType DataSinkSinkType

One of the following:

const DataSinkSinkTypePinecone DataSinkSinkType = "PINECONE"

const DataSinkSinkTypePostgres DataSinkSinkType = "POSTGRES"

const DataSinkSinkTypeQdrant DataSinkSinkType = "QDRANT"

const DataSinkSinkTypeAzureaiSearch DataSinkSinkType = "AZUREAI_SEARCH"

const DataSinkSinkTypeMongoDBAtlas DataSinkSinkType = "MONGODB_ATLAS"

const DataSinkSinkTypeMilvus DataSinkSinkType = "MILVUS"

const DataSinkSinkTypeAstraDB DataSinkSinkType = "ASTRA_DB"

CreatedAt Timeoptional

Creation datetime

formatdate-time

UpdatedAt Timeoptional

Update datetime

formatdate-time

Update Data Sink

Go

HTTP

TypeScript

Python

Go

CLI Tool

package main

import (
  "context"
  "fmt"

  "github.com/stainless-sdks/llamacloud-prod-go"
  "github.com/stainless-sdks/llamacloud-prod-go/option"
)

func main() {
  client := llamacloudprod.NewClient(
    option.WithAPIKey("My API Key"),
  )
  dataSink, err := client.DataSinks.Update(
    context.TODO(),
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    llamacloudprod.DataSinkUpdateParams{
      SinkType: llamacloudprod.DataSinkUpdateParamsSinkTypePinecone,
    },
  )
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", dataSink.ID)
}

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"
}