FaissVectorStore

pydantic model llama_index.vector_stores.FaissVectorStore

Faiss Vector Store.

Embeddings are stored within a Faiss index.

During query time, the index uses Faiss to query for the top k embeddings, and returns the corresponding indices.

Parameters

faiss_index (faiss.Index) – Faiss index instance

Show JSON schema

{
   "title": "FaissVectorStore",
   "description": "Faiss Vector Store.\n\nEmbeddings are stored within a Faiss index.\n\nDuring query time, the index uses Faiss to query for the top\nk embeddings, and returns the corresponding indices.\n\nArgs:\n    faiss_index (faiss.Index): Faiss index instance",
   "type": "object",
   "properties": {
      "stores_text": {
         "title": "Stores Text",
         "default": false,
         "type": "boolean"
      },
      "is_embedding_query": {
         "title": "Is Embedding Query",
         "default": true,
         "type": "boolean"
      },
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "base_component"
      }
   }
}

Config

• schema_extra: function = <function BaseComponent.Config.schema_extra at 0x7ff1e41e53a0>

Fields

• stores_text (bool)

field stores_text: bool = False

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

Add nodes to index.

NOTE: in the Faiss vector store, we do not store text in Faiss.

Parameters

nodes – List[BaseNode]: list of nodes with embeddings

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.

classmethod from_persist_dir(persist_dir: str = './storage', fs: Optional[AbstractFileSystem] = None) → FaissVectorStore

classmethod from_persist_path(persist_path: str, fs: Optional[AbstractFileSystem] = None) → FaissVectorStore

persist(persist_path: str = './storage/default__vector_store.json', fs: Optional[AbstractFileSystem] = None) → None

Save to file.

This method saves the vector store to disk.

Parameters

persist_path (str) – The save_path of the file.

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

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

property client: Any

Return the faiss index.