API Reference

Data Sinks

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Create Data Sink

data_sinks.create(DataSinkCreateParams**kwargs) -> DataSink

POST/api/v1/data-sinks

Create a new data sink.

ParametersExpand Collapse

component: Component

Component that implements the data sink

One of the following:

Dict[str, object]

class 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

api_key: str

The API key for authenticating with Pinecone

formatpassword

index_name: str

class_name: Optional[str]

insert_kwargs: Optional[Dict[str, object]]

namespace: Optional[str]

supports_nested_metadata_filters: Optional[Literal[true]]

class CloudPostgresVectorStore: …

database: str

embed_dim: int

host: str

password: str

port: int

schema_name: str

table_name: str

user: str

class_name: Optional[str]

hnsw_settings: Optional[PgVectorHnswSettings]

HNSW settings for PGVector.

distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]

The distance method to use.

One of the following:

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction: Optional[int]

The number of edges to use during the construction phase.

minimum1

ef_search: Optional[int]

The number of edges to use during the search phase.

minimum1

m: Optional[int]

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

minimum1

vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]

The type of vector to use.

One of the following:

"vector"

"half_vec"

"bit"

"sparse_vec"

hybrid_search: Optional[bool]

perform_setup: Optional[bool]

supports_nested_metadata_filters: Optional[bool]

class 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

api_key: str

collection_name: str

url: str

class_name: Optional[str]

client_kwargs: Optional[Dict[str, object]]

max_retries: Optional[int]

supports_nested_metadata_filters: Optional[Literal[true]]

class CloudAzureAISearchVectorStore: …

Cloud Azure AI Search Vector Store.

search_service_api_key: str

search_service_endpoint: str

class_name: Optional[str]

client_id: Optional[str]

client_secret: Optional[str]

embedding_dimension: Optional[int]

filterable_metadata_field_keys: Optional[Dict[str, object]]

index_name: Optional[str]

search_service_api_version: Optional[str]

supports_nested_metadata_filters: Optional[Literal[true]]

tenant_id: Optional[str]

class 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

collection_name: str

db_name: str

mongodb_uri: str

class_name: Optional[str]

embedding_dimension: Optional[int]

fulltext_index_name: Optional[str]

supports_nested_metadata_filters: Optional[bool]

vector_index_name: Optional[str]

class CloudMilvusVectorStore: …

Cloud Milvus Vector Store.

uri: str

token: Optional[str]

class_name: Optional[str]

collection_name: Optional[str]

embedding_dimension: Optional[int]

supports_nested_metadata_filters: Optional[bool]

class CloudAstraDBVectorStore: …

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: str

The Astra DB Application Token to use

formatpassword

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

class_name: Optional[str]

keyspace: Optional[str]

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

supports_nested_metadata_filters: Optional[Literal[true]]

name: str

The name of the data sink.

sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]

One of the following:

"PINECONE"

"POSTGRES"

"QDRANT"

"AZUREAI_SEARCH"

"MONGODB_ATLAS"

"MILVUS"

"ASTRA_DB"

organization_id: Optional[str]

project_id: Optional[str]

ReturnsExpand Collapse

class DataSink: …

Schema for a data sink.

id: str

Unique identifier

formatuuid

component: Component

Component that implements the data sink

One of the following:

Dict[str, object]

class 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

api_key: str

The API key for authenticating with Pinecone

formatpassword

index_name: str

class_name: Optional[str]

insert_kwargs: Optional[Dict[str, object]]

namespace: Optional[str]

supports_nested_metadata_filters: Optional[Literal[true]]

class CloudPostgresVectorStore: …

database: str

embed_dim: int

host: str

password: str

port: int

schema_name: str

table_name: str

user: str

class_name: Optional[str]

hnsw_settings: Optional[PgVectorHnswSettings]

HNSW settings for PGVector.

distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]

The distance method to use.

One of the following:

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction: Optional[int]

The number of edges to use during the construction phase.

minimum1

ef_search: Optional[int]

The number of edges to use during the search phase.

minimum1

m: Optional[int]

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

minimum1

vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]

The type of vector to use.

One of the following:

"vector"

"half_vec"

"bit"

"sparse_vec"

hybrid_search: Optional[bool]

perform_setup: Optional[bool]

supports_nested_metadata_filters: Optional[bool]

class 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

api_key: str

collection_name: str

url: str

class_name: Optional[str]

client_kwargs: Optional[Dict[str, object]]

max_retries: Optional[int]

supports_nested_metadata_filters: Optional[Literal[true]]

class CloudAzureAISearchVectorStore: …

Cloud Azure AI Search Vector Store.

search_service_api_key: str

search_service_endpoint: str

class_name: Optional[str]

client_id: Optional[str]

client_secret: Optional[str]

embedding_dimension: Optional[int]

filterable_metadata_field_keys: Optional[Dict[str, object]]

index_name: Optional[str]

search_service_api_version: Optional[str]

supports_nested_metadata_filters: Optional[Literal[true]]

tenant_id: Optional[str]

class 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

collection_name: str

db_name: str

mongodb_uri: str

class_name: Optional[str]

embedding_dimension: Optional[int]

fulltext_index_name: Optional[str]

supports_nested_metadata_filters: Optional[bool]

vector_index_name: Optional[str]

class CloudMilvusVectorStore: …

Cloud Milvus Vector Store.

uri: str

token: Optional[str]

class_name: Optional[str]

collection_name: Optional[str]

embedding_dimension: Optional[int]

supports_nested_metadata_filters: Optional[bool]

class CloudAstraDBVectorStore: …

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: str

The Astra DB Application Token to use

formatpassword

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

class_name: Optional[str]

keyspace: Optional[str]

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

supports_nested_metadata_filters: Optional[Literal[true]]

name: str

The name of the data sink.

project_id: str

sink_type: Literal["PINECONE", "POSTGRES", "QDRANT", 4 more]

One of the following:

"PINECONE"

"POSTGRES"

"QDRANT"

"AZUREAI_SEARCH"

"MONGODB_ATLAS"

"MILVUS"

"ASTRA_DB"

created_at: Optional[datetime]

Creation datetime

formatdate-time

updated_at: Optional[datetime]

Update datetime

formatdate-time

Create Data Sink

Python

HTTP

TypeScript

Python

import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
data_sink = client.data_sinks.create(
    component={
        "foo": "bar"
    },
    name="name",
    sink_type="PINECONE",
)
print(data_sink.id)

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"
}