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API Reference

Data Sinks

List Data Sinks
GET/api/v1/data-sinks
Create Data Sink
POST/api/v1/data-sinks
Get Data Sink
GET/api/v1/data-sinks/{data_sink_id}
Update Data Sink
PUT/api/v1/data-sinks/{data_sink_id}
Delete Data Sink
DELETE/api/v1/data-sinks/{data_sink_id}
ModelsExpand Collapse
DataSink = object { id, component, name, 4 more }

Schema for a data sink.

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

Accepts one of the following:
UnionMember0 = 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 "l2" or "ip" or "cosine" or 3 more

The distance method to use.

Accepts one of the following:
"l2"
"ip"
"cosine"
"l1"
"hamming"
"jaccard"
ef_construction: optional number

The number of edges to use during the construction phase.

minimum1

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 "vector" or "half_vec" or "bit" or "sparse_vec"

The type of vector to use.

Accepts one of the following:
"vector"
"half_vec"
"bit"
"sparse_vec"
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

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

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: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more
Accepts one of the following:
"PINECONE"
"POSTGRES"
"QDRANT"
"AZUREAI_SEARCH"
"MONGODB_ATLAS"
"MILVUS"
"ASTRA_DB"
created_at: optional string

Creation datetime

formatdate-time
updated_at: optional string

Update datetime

formatdate-time