Dingo

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

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

Return Dingo 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.

Return docs selected using the maximal marginal relevance.

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

Construct Dingo wrapper from raw documents.

    This is a user friendly interface that:
        1. Embeds documents.
        2. Adds the documents to a provided Dingo index

    This is intended to be a quick way to get started.

    Example:
        .. code-block:: python

            from langchain_community.vectorstores import Dingo
            from langchain_community.embeddings import OpenAIEmbeddings
            import dingodb

sss embeddings = OpenAIEmbeddings() dingo = Dingo.from_texts( texts, embeddings, index_name="langchain-demo" )

Delete by vector IDs or filter. Args: ids: List of ids to delete.