API Reference

Data Sinks

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List Data Sinks

GET/api/v1/beta/data-sinks

List the data sinks in a project, newest first.

Query ParametersExpand Collapse

include_total: optional boolean

Return total_size, a count of every row matching the filter. It is a second query on every page, so it is off unless asked for.

organization_id: optional string

page_size: optional number

Number of items per page

maximum100

minimum1

page_token: optional string

Cursor from the previous page’s next_page_token.

project_id: optional string

Cookie ParametersExpand Collapse

session: optional string

ReturnsExpand Collapse

items: array of DataSink { id, component, name, 4 more }

The list of items.

id: string

Unique identifier

formatuuid

component: map[unknown] or CloudPineconeVectorStore { api_key, index_name, class_name, 3 more } or CloudPostgresVectorStore { database, embed_dim, host, 10 more } or 5 more

Component that implements the data sink

One of the following:

map[unknown]

CloudPineconeVectorStore object { api_key, index_name, class_name, 3 more }

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

api_key: string

The API key for authenticating with Pinecone

formatpassword

index_name: string

class_name: optional string

insert_kwargs: optional map[unknown]

namespace: optional string

supports_nested_metadata_filters: optional true

CloudPostgresVectorStore object { database, embed_dim, host, 10 more }

database: string

embed_dim: number

host: string

password: string

port: number

schema_name: string

table_name: string

user: string

class_name: optional string

hnsw_settings: optional PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more }

HNSW settings for PGVector.

distance_method: optional "cosine" or "hamming" or "ip" or 3 more

The distance method to use.

One of the following:

"cosine"

"hamming"

"ip"

"jaccard"

"l1"

"l2"

ef_construction: optional number

The number of edges to use during the construction phase.

minimum1

ef_search: optional number

The number of edges to use during the search phase.

minimum1

m: optional number

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

minimum1

vector_type: optional "bit" or "half_vec" or "sparse_vec" or "vector"

The type of vector to use.

One of the following:

"bit"

"half_vec"

"sparse_vec"

"vector"

hybrid_search: optional boolean

perform_setup: optional boolean

supports_nested_metadata_filters: optional boolean

CloudQdrantVectorStore object { api_key, collection_name, url, 4 more }

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

api_key: string

collection_name: string

url: string

class_name: optional string

client_kwargs: optional map[unknown]

max_retries: optional number

supports_nested_metadata_filters: optional true

CloudAzureAISearchVectorStore object { search_service_api_key, search_service_endpoint, class_name, 8 more }

Cloud Azure AI Search Vector Store.

search_service_api_key: string

search_service_endpoint: string

class_name: optional string

client_id: optional string

client_secret: optional string

embedding_dimension: optional number

filterable_metadata_field_keys: optional map[unknown]

index_name: optional string

search_service_api_version: optional string

supports_nested_metadata_filters: optional true

tenant_id: optional string

CloudMongoDBAtlasVectorSearch object { collection_name, db_name, mongodb_uri, 5 more }

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

collection_name: string

db_name: string

mongodb_uri: string

class_name: optional string

embedding_dimension: optional number

fulltext_index_name: optional string

supports_nested_metadata_filters: optional boolean

vector_index_name: optional string

CloudMilvusVectorStore object { uri, token, class_name, 3 more }

Cloud Milvus Vector Store.

uri: string

token: optional string

class_name: optional string

collection_name: optional string

embedding_dimension: optional number

supports_nested_metadata_filters: optional boolean

CloudAstraDBVectorStore object { token, api_endpoint, collection_name, 4 more }

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

api_endpoint: string

The Astra DB JSON API endpoint for your database

collection_name: string

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

embedding_dimension: number

Length of the embedding vectors in use

class_name: optional string

keyspace: optional string

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

supports_nested_metadata_filters: optional true

name: string

The name of the data sink.

project_id: string

sink_type: "ASTRA_DB" or "AZUREAI_SEARCH" or "MILVUS" or 4 more

One of the following:

"ASTRA_DB"

"AZUREAI_SEARCH"

"MILVUS"

"MONGODB_ATLAS"

"PINECONE"

"POSTGRES"

"QDRANT"

created_at: optional string

Creation datetime

formatdate-time

updated_at: optional string

Update datetime

formatdate-time

next_page_token: optional string

A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

total_size: optional number

The total number of items available. This is only populated when specifically requested. The value may be an estimate and can be used for display purposes only.

List Data Sinks

HTTP

HTTP

TypeScript

Python

Go

Java

C#

CLI Tool

curl https://api.cloud.llamaindex.ai/api/v1/beta/data-sinks \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"

200 example

{
  "items": [
    {
      "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"
    }
  ],
  "next_page_token": "next_page_token",
  "total_size": 0
}

Returns Examples

200 example

{
  "items": [
    {
      "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"
    }
  ],
  "next_page_token": "next_page_token",
  "total_size": 0
}