API Reference

Data Sinks

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Update Data Sink

$ llamacloud-prod data-sinks update

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

Update a data sink by ID.

ParametersExpand Collapse

--data-sink-id: string

--sink-type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more

--component: optional 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

--name: optional string

The name of the data sink.

ReturnsExpand Collapse

data_sink: object { id, component, name, 4 more }

Schema for a data sink.

id: string

Unique identifier

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

union_member_0: map[unknown]

cloud_pinecone_vector_store: 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

index_name: string

class_name: optional string

insert_kwargs: optional map[unknown]

namespace: optional string

supports_nested_metadata_filters: optional true

true

cloud_postgres_vector_store: 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 object { distance_method, ef_construction, ef_search, 2 more }

HNSW settings for PGVector.

distance_method: optional "l2" or "ip" or "cosine" or 3 more

The distance method to use.

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction: optional number

The number of edges to use during the construction phase.

ef_search: optional number

The number of edges to use during the search phase.

m: optional number

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

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

The type of vector to use.

"vector"

"half_vec"

"bit"

"sparse_vec"

hybrid_search: optional boolean

perform_setup: optional boolean

supports_nested_metadata_filters: optional boolean

cloud_qdrant_vector_store: 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

true

cloud_azure_ai_search_vector_store: 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

true

tenant_id: optional string

cloud_mongodb_atlas_vector_search: 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

cloud_milvus_vector_store: 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

cloud_astra_db_vector_store: 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

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

true

name: string

The name of the data sink.

project_id: string

sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more

"PINECONE"

"POSTGRES"

"QDRANT"

"AZUREAI_SEARCH"

"MONGODB_ATLAS"

"MILVUS"

"ASTRA_DB"

created_at: optional string

Creation datetime

updated_at: optional string

Update datetime

Update Data Sink

CLI Tool

HTTP

TypeScript

Python

Java

Go

CLI Tool

llamacloud-prod data-sinks update \
  --api-key 'My API Key' \
  --data-sink-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \
  --sink-type PINECONE

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"
}