:param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
class langchain.vectorstores.FAISS(embedding_function: typing.Callable, index: typing.Any, docstore: langchain.docstore.base.Docstore, index_to_docstore_id: typing.Dict[int, str], relevance_score_fn: typing.Optional[typing.Callable[[float], float]] = <function _default_relevance_score_fn>, normalize_L2: bool = False)[source]#
Wrapper around FAISS vector database.
To use, you should have the faiss python package installed.
Example
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
text_embeddings – Iterable pairs of string and embedding to
add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
Returns
List of ids from adding the texts into the vectorstore.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
Returns
List of ids from adding the texts into the vectorstore.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html