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.

llama_index.indices.vector_store.base.GPTVectorStoreIndex

alias of VectorStoreIndex

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

Vector Store Index.

Parameters

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

  • store_nodes_override (bool) – set to True to always store Node objects in index store and document store even if vector store keeps text. Defaults to False

delete_nodes(doc_ids: List[str], delete_from_docstore: bool = False, **delete_kwargs: Any) → None

Delete a list of nodes from the index.

Parameters

doc_ids (List[str]) – A list of doc_ids from the nodes to delete

delete_ref_doc(ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any) → None

Delete a document and it’s nodes by using ref_doc_id.

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.

property ref_doc_info: Dict[str, RefDocInfo]

Retrieve a dict mapping of ingested documents and their nodes+metadata.

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.

refresh_ref_docs(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 and it’s corresponding nodes.

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

Parameters

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

  • insert_kwargs (Dict) – kwargs to pass to insert

  • delete_kwargs (Dict) – kwargs to pass to delete

update_ref_doc(document: Document, **update_kwargs: Any) → None

Update a document and it’s corresponding nodes.

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

Parameters

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

  • insert_kwargs (Dict) – kwargs to pass to insert

  • delete_kwargs (Dict) – kwargs to pass to delete