Vectara

Delete by vector ID or other criteria. Args: ids: List of ids to delete.

Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain

Run more texts through the embeddings and add to the vectorstore.

Run a Vectara query

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

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

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python

    from langchain_community.vectorstores import Vectara
    vectara = Vectara.from_texts(
        texts,
        vectara_customer_id=customer_id,
        vectara_corpus_id=corpus_id,
        vectara_api_key=api_key,
    )

Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python

    from langchain_community.vectorstores import Vectara
    vectara = Vectara.from_files(
        files_list,
        vectara_customer_id=customer_id,
        vectara_corpus_id=corpus_id,
        vectara_api_key=api_key,
    )

Return a Vectara RAG runnable.

Return a Vectara RAG runnable for chat.

return a retriever object.