Run similarity search between a query vector and the indexed vectors.
similarity_search_limit_score(query[, k, ...])
[Deprecated]  Returns the most similar indexed documents to the query text within the score_threshold range.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with vector distance.
write_schema(path)
Write the schema to a yaml file.
__init__(redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, str], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, key_prefix: Optional[str] = None, **kwargs: Any)[source]¶
Initialize Redis vector store with necessary components.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]