RedisVectorStore

Add text documents to the vector store.

Create a RedisVectorStore from a list of texts.

Create a RedisVectorStore from a list of Documents.

Create a RedisVectorStore from an existing Redis Search Index.

This method allows you to connect to an already existing index in Redis, which can be useful for continuing work with previously created indexes or for connecting to indexes created outside of this client.

Delete ids from the vector store.

Return docs most similar to embedding vector.

Return docs most similar to query.

Return docs most similar to embedding vector.

Return documents most similar to query string, along with scores.

    Args:
        query: Text to look up documents similar to.
        k: Number of Documents to return. Defaults to 4.
        filter: Optional filter expression to apply to the query.
        sort_by: Optional sort_by expression to apply to the query.
        **kwargs: Other keyword arguments to pass to the search function:
            - custom_query: Optional callable that can be used
                            to customize the query.
            - doc_builder: Optional callable to customize Document creation.
            - return_metadata: Whether to return metadata. Defaults to True.
            - distance_threshold: Optional distance threshold for filtering results.
            - return_all: Whether to return all data in the Hash/JSON including
            non-indexed fields. Defaults to False.

    Returns:
        List of tuples of (Document, score) most similar to the query.

    Example:
        .. code-block:: python

            from langchain_redis import RedisVectorStore
            from langchain_openai import OpenAIEmbeddings

            vector_store = RedisVectorStore(
                index_name="langchain-demo",
                embedding=OpenAIEmbeddings(),
                redis_url="redis://localhost:6379",
            )

            results = vector_store.similarity_search_with_score(
                "What is machine learning?",
                k=2,
                filter=None
            )

            for doc, score in results:
                print(f"Score: {score}")
                print(f"Content: {doc.page_content}")
                print(f"Metadata: {doc.metadata}

")

    Note:
        - The method returns scores along with documents. Lower scores indicate
        higher similarity.
        - The actual search is performed using the vector representation of the
          query, which is why an embedding function must be provided during
          initialization.
        - The `filter` parameter allows for additional filtering of results
          based on metadata.
        - If `return_all` is set to True, all fields stored in Redis will be
          returned, which may include non-indexed fields.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.