ChatGPTRetrievalPluginClient

class llama_index.vector_stores.ChatGPTRetrievalPluginClient(endpoint_url: str, bearer_token: Optional[str] = None, retries: Optional[Retry] = None, batch_size: int = 100, **kwargs: Any)

Bases: VectorStore

ChatGPT Retrieval Plugin Client.

In this client, we make use of the endpoints defined by ChatGPT.

Parameters

• endpoint_url (str) – URL of the ChatGPT Retrieval Plugin.

• bearer_token (Optional[str]) – Bearer token for the ChatGPT Retrieval Plugin.

• retries (Optional[Retry]) – Retry object for the ChatGPT Retrieval Plugin.

• batch_size (int) – Batch size for the ChatGPT Retrieval Plugin.

Attributes Summary

client	Get client.
is_embedding_query
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes to index.
delete(ref_doc_id, **delete_kwargs)	Delete nodes using with ref_doc_id.
query(query, **kwargs)	Get nodes for response.

Attributes Documentation

client

Get client.

is_embedding_query: bool = False

stores_text: bool = True

Methods Documentation

add(nodes: List[BaseNode], **add_kwargs: Any) → List[str]

Add nodes to index.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Get nodes for response.