Return VectorStore initialized from documents and embeddings.
async classmethod afrom_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents asynchronously.
async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents asynchronously.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = await FAISS.afrom_texts(texts, embeddings)
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance asynchronously.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch before filtering (if needed) to
pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding