Vector Store Index

Below we show the vector store index classes.

Each vector store index class is a combination of a base vector store index class and a vector store, shown below.

Base vector store index.

An index that that is built on top of an existing vector store.

class llama_index.indices.vector_store.base.GPTVectorStoreIndex(nodes: Optional[Sequence[Node]] = None, index_struct: Optional[IndexDict] = None, service_context: Optional[ServiceContext] = None, storage_context: Optional[StorageContext] = None, use_async: bool = False, **kwargs: Any)

Base GPT Vector Store Index.

Parameters

use_async (bool) – Whether to use asynchronous calls. Defaults to False.

classmethod from_documents(documents: Sequence[Document], storage_context: Optional[StorageContext] = None, service_context: Optional[ServiceContext] = None, **kwargs: Any) → IndexType

Create index from documents.

Parameters

documents (Optional[Sequence[BaseDocument]]) – List of documents to build the index from.

property index_id: str

Get the index struct.

insert(document: Document, **insert_kwargs: Any) → None

Insert a document.

refresh(documents: Sequence[Document], **update_kwargs: Any) → List[bool]

Refresh an index with documents that have changed.

This allows users to save LLM and Embedding model calls, while only updating documents that have any changes in text or extra_info. It will also insert any documents that previously were not stored.

set_index_id(index_id: str) → None

Set the index id.

NOTE: if you decide to set the index_id on the index_struct manually, you will need to explicitly call add_index_struct on the index_store to update the index store.

Parameters

index_id (str) – Index id to set.

update(document: Document, **update_kwargs: Any) → None

Update a document.

This is equivalent to deleting the document and then inserting it again.

Parameters

• document (Union[BaseDocument, BaseGPTIndex]) – document to update

• insert_kwargs (Dict) – kwargs to pass to insert

• delete_kwargs (Dict) – kwargs to pass to delete