API Reference

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Data Sinks

List Data Sinks

client.dataSinks.list(DataSinkListParams { organization_id, project_id } query?, RequestOptionsoptions?): DataSinkListResponse { id, component, name, 4 more }

GET/api/v1/data-sinks

Create Data Sink

client.dataSinks.create(DataSinkCreateParams { component, name, sink_type, 2 more } params, RequestOptionsoptions?): DataSink { id, component, name, 4 more }

POST/api/v1/data-sinks

Get Data Sink

client.dataSinks.get(stringdataSinkID, RequestOptionsoptions?): DataSink { id, component, name, 4 more }

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

Update Data Sink

client.dataSinks.update(stringdataSinkID, DataSinkUpdateParams { sink_type, component, name } body, RequestOptionsoptions?): DataSink { id, component, name, 4 more }

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

Delete Data Sink

client.dataSinks.delete(stringdataSinkID, RequestOptionsoptions?): void

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

ModelsExpand Collapse

DataSink { id, component, name, 4 more }

Schema for a data sink.

id: string

Unique identifier

formatuuid

component: Record<string, unknown> | CloudPineconeVectorStore { api_key, index_name, class_name, 3 more } | CloudPostgresVectorStore { database, embed_dim, host, 10 more } | 5 more

Component that implements the data sink

One of the following:

Record<string, unknown>

CloudPineconeVectorStore { 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?: string

insert_kwargs?: Record<string, unknown> | null

namespace?: string | null

supports_nested_metadata_filters?: true

CloudPostgresVectorStore { 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?: string

hnsw_settings?: PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more } | null

HNSW settings for PGVector.

distance_method?: "cosine" | "hamming" | "ip" | 3 more

The distance method to use.

One of the following:

"cosine"

"hamming"

"ip"

"jaccard"

"l1"

"l2"

ef_construction?: number

The number of edges to use during the construction phase.

minimum1

ef_search?: number

The number of edges to use during the search phase.

minimum1

m?: number

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

minimum1

vector_type?: "bit" | "half_vec" | "sparse_vec" | "vector"

The type of vector to use.

One of the following:

"bit"

"half_vec"

"sparse_vec"

"vector"

hybrid_search?: boolean | null

perform_setup?: boolean

supports_nested_metadata_filters?: boolean

CloudQdrantVectorStore { 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?: string

client_kwargs?: Record<string, unknown>

max_retries?: number

supports_nested_metadata_filters?: true

CloudAzureAISearchVectorStore { 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?: string

client_id?: string | null

client_secret?: string | null

embedding_dimension?: number | null

filterable_metadata_field_keys?: Record<string, unknown> | null

index_name?: string | null

search_service_api_version?: string | null

supports_nested_metadata_filters?: true

tenant_id?: string | null

CloudMongoDBAtlasVectorSearch { 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?: string

embedding_dimension?: number | null

fulltext_index_name?: string | null

supports_nested_metadata_filters?: boolean

vector_index_name?: string | null

CloudMilvusVectorStore { uri, token, class_name, 3 more }

Cloud Milvus Vector Store.

uri: string

token?: string | null

class_name?: string

collection_name?: string | null

embedding_dimension?: number | null

supports_nested_metadata_filters?: boolean

CloudAstraDBVectorStore { 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?: string

keyspace?: string | null

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

supports_nested_metadata_filters?: true

name: string

The name of the data sink.

project_id: string

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

One of the following:

"ASTRA_DB"

"AZUREAI_SEARCH"

"MILVUS"

"MONGODB_ATLAS"

"PINECONE"

"POSTGRES"

"QDRANT"

created_at?: string | null

Creation datetime

formatdate-time

updated_at?: string | null

Update datetime

formatdate-time

DataSinkListResponse = Array<DataSink { id, component, name, 4 more } >

id: string

Unique identifier

formatuuid

component: Record<string, unknown> | CloudPineconeVectorStore { api_key, index_name, class_name, 3 more } | CloudPostgresVectorStore { database, embed_dim, host, 10 more } | 5 more

Component that implements the data sink

One of the following:

Record<string, unknown>

CloudPineconeVectorStore { 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?: string

insert_kwargs?: Record<string, unknown> | null

namespace?: string | null

supports_nested_metadata_filters?: true

CloudPostgresVectorStore { 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?: string

hnsw_settings?: PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more } | null

HNSW settings for PGVector.

distance_method?: "cosine" | "hamming" | "ip" | 3 more

The distance method to use.

One of the following:

"cosine"

"hamming"

"ip"

"jaccard"

"l1"

"l2"

ef_construction?: number

The number of edges to use during the construction phase.

minimum1

ef_search?: number

The number of edges to use during the search phase.

minimum1

m?: number

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

minimum1

vector_type?: "bit" | "half_vec" | "sparse_vec" | "vector"

The type of vector to use.

One of the following:

"bit"

"half_vec"

"sparse_vec"

"vector"

hybrid_search?: boolean | null

perform_setup?: boolean

supports_nested_metadata_filters?: boolean

CloudQdrantVectorStore { 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?: string

client_kwargs?: Record<string, unknown>

max_retries?: number

supports_nested_metadata_filters?: true

CloudAzureAISearchVectorStore { 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?: string

client_id?: string | null

client_secret?: string | null

embedding_dimension?: number | null

filterable_metadata_field_keys?: Record<string, unknown> | null

index_name?: string | null

search_service_api_version?: string | null

supports_nested_metadata_filters?: true

tenant_id?: string | null

CloudMongoDBAtlasVectorSearch { 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?: string

embedding_dimension?: number | null

fulltext_index_name?: string | null

supports_nested_metadata_filters?: boolean

vector_index_name?: string | null

CloudMilvusVectorStore { uri, token, class_name, 3 more }

Cloud Milvus Vector Store.

uri: string

token?: string | null

class_name?: string

collection_name?: string | null

embedding_dimension?: number | null

supports_nested_metadata_filters?: boolean

CloudAstraDBVectorStore { 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?: string

keyspace?: string | null

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

supports_nested_metadata_filters?: true

name: string

The name of the data sink.

project_id: string

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

One of the following:

"ASTRA_DB"

"AZUREAI_SEARCH"

"MILVUS"

"MONGODB_ATLAS"

"PINECONE"

"POSTGRES"

"QDRANT"

created_at?: string | null

Creation datetime

formatdate-time

updated_at?: string | null

Update datetime

formatdate-time