Upsert Data Sink

PUT 
/api/v1/data-sinks

Upserts a data sink. Updates if a data sink with the same name and project_id already exists. Otherwise, creates a new data sink.

Request

Query Parameters

project_id any
organization_id any

Cookie Parameters

session any

application/json

Body

required

name Name (string)required

The name of the data sink.

sink_type ConfigurableDataSinkNames (string)required

Possible values: [PINECONE, POSTGRES, QDRANT, AZUREAI_SEARCH, MONGODB_ATLAS, MILVUS]

component

object

required

anyOf

MOD1

object

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

api_key passwordrequired

The API key for authenticating with Pinecone

index_name Index Name (string)required

namespace

object

anyOf

MOD1

string

insert_kwargs

object

anyOf

MOD1

object

class_name Class Name (string)

Default value: CloudPineconeVectorStore

CloudPostgresVectorStore

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

database Database (string)required

host Host (string)required

password passwordrequired

port Port (integer)required

user User (string)required

table_name Table Name (string)required

schema_name Schema Name (string)required

embed_dim Embed Dim (integer)required

hybrid_search

object

anyOf

MOD1

boolean

class_name Class Name (string)

Default value: CloudPostgresVectorStore

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

collection_name Collection Name (string)required

url Url (string)required

api_key passwordrequired

max_retries Max Retries (integer)

Default value: 3

client_kwargs object

class_name Class Name (string)

Default value: CloudQdrantVectorStore

CloudAzureAISearchVectorStore

Cloud Azure AI Search Vector Store.

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

search_service_api_key passwordrequired

search_service_endpoint Search Service Endpoint (string)required

search_service_api_version

object

anyOf

MOD1

string

index_name

object

anyOf

MOD1

string

filterable_metadata_field_keys

object

anyOf

MOD1

object

embedding_dimension

object

anyOf

MOD1

integer

client_id

object

anyOf

MOD1

string

client_secret

object

anyOf

MOD1

string

tenant_id

object

anyOf

MOD1

string

class_name Class Name (string)

Default value: CloudAzureAISearchVectorStore

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

mongodb_uri passwordrequired

db_name Db Name (string)required

collection_name Collection Name (string)required

vector_index_name

object

anyOf

MOD1

string

fulltext_index_name

object

anyOf

MOD1

string

class_name Class Name (string)

Default value: CloudMongoDBAtlasVectorSearch

CloudMilvusVectorStore

Cloud Milvus Vector Store.

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

uri Uri (string)required

collection_name

object

anyOf

MOD1

string

token

object

anyOf

MOD1

string

embedding_dimension

object

anyOf

MOD1

integer

class_name Class Name (string)

Default value: CloudMilvusVectorStore

Responses

200

Successful Response

application/json

Schema

Schema

id uuidrequired

Unique identifier

created_at

object

Creation datetime

anyOf

MOD1

string

updated_at

object

Update datetime

anyOf

MOD1

string

name Name (string)required

The name of the data sink.

sink_type ConfigurableDataSinkNames (string)required

Possible values: [PINECONE, POSTGRES, QDRANT, AZUREAI_SEARCH, MONGODB_ATLAS, MILVUS]

component

object

required

anyOf

MOD1

object

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

index_name Index Name (string)required

namespace

object

anyOf

MOD1

string

insert_kwargs

object

anyOf

MOD1

object

class_name Class Name (string)

Default value: CloudPineconeVectorStore

CloudPostgresVectorStore

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

database Database (string)required

host Host (string)required

port Port (integer)required

user User (string)required

table_name Table Name (string)required

schema_name Schema Name (string)required

embed_dim Embed Dim (integer)required

hybrid_search

object

anyOf

MOD1

boolean

class_name Class Name (string)

Default value: CloudPostgresVectorStore

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

collection_name Collection Name (string)required

url Url (string)required

max_retries Max Retries (integer)

Default value: 3

client_kwargs object

class_name Class Name (string)

Default value: CloudQdrantVectorStore

CloudAzureAISearchVectorStore

Cloud Azure AI Search Vector Store.

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [true]

Default value: true

search_service_endpoint Search Service Endpoint (string)required

search_service_api_version

object

anyOf

MOD1

string

index_name

object

anyOf

MOD1

string

filterable_metadata_field_keys

object

anyOf

MOD1

object

embedding_dimension

object

anyOf

MOD1

integer

client_id

object

anyOf

MOD1

string

client_secret

object

anyOf

MOD1

tenant_id

object

anyOf

MOD1

string

class_name Class Name (string)

Default value: CloudAzureAISearchVectorStore

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

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

db_name Db Name (string)required

collection_name Collection Name (string)required

vector_index_name

object

anyOf

MOD1

string

fulltext_index_name

object

anyOf

MOD1

string

class_name Class Name (string)

Default value: CloudMongoDBAtlasVectorSearch

CloudMilvusVectorStore

Cloud Milvus Vector Store.

supports_nested_metadata_filters Supports Nested Metadata Filters (boolean)

Possible values: [false]

Default value: false

uri Uri (string)required

collection_name

object

anyOf

MOD1

string

token

object

anyOf

MOD1

embedding_dimension

object

anyOf

MOD1

integer

class_name Class Name (string)

Default value: CloudMilvusVectorStore
project_id uuidrequired

Example (from schema)

{
  "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "created_at": "2024-07-29T15:51:28.071Z",
  "updated_at": "2024-07-29T15:51:28.071Z",
  "name": "string",
  "sink_type": "PINECONE",
  "component": {},
  "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
}

422

Validation Error

application/json

Schema

Schema

detail

object[]

Array [

loc

object[]

required

Array [

anyOf

MOD1

string

MOD2

integer

]

msg Message (string)required

type Error Type (string)required

]

Example (from schema)

{
  "detail": [
    {
      "loc": [
        "string",
        0
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

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