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#

# Source code for langchain.chains.qa_with_sources.vector_db

    """Question-answering with sources over a vector database."""

    import warnings
    from typing import Any, Dict, List

    from pydantic import Field, root_validator

    from langchain.chains.combine_documents.stuff import StuffDocumentsChain
    from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
    from langchain.docstore.document import Document
    from langchain.vectorstores.base import VectorStore

    class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
        """Question-answering with sources over a vector database."""

        vectorstore: VectorStore = Field(exclude=True)
        """Vector Database to connect to."""
        k: int = 4
        """Number of results to return from store"""
        reduce_k_below_max_tokens: bool = False
        """Reduce the number of results to return from store based on tokens limit"""
        max_tokens_limit: int = 3375
        """Restrict the docs to return from store based on tokens,
        enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
        search_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """Extra search args."""

        def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
            num_docs = len(docs)

            if self.reduce_k_below_max_tokens and isinstance(
                self.combine_documents_chain, StuffDocumentsChain
            ):
                tokens = [
                    self.combine_documents_chain.llm_chain.llm.get_num_tokens(
                        doc.page_content
                    )
                    for doc in docs
                ]
                token_count = sum(tokens[:num_docs])
                while token_count > self.max_tokens_limit:
                    num_docs -= 1
                    token_count -= tokens[num_docs]

            return docs[:num_docs]

        def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
            question = inputs[self.question_key]
            docs = self.vectorstore.similarity_search(
                question, k=self.k, **self.search_kwargs
            )
            return self._reduce_tokens_below_limit(docs)

        async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
            raise NotImplementedError("VectorDBQAWithSourcesChain does not support async")

        @root_validator()
        def raise_deprecation(cls, values: Dict) -> Dict:
            warnings.warn(
                "`VectorDBQAWithSourcesChain` is deprecated - "
                "please use `from langchain.chains import RetrievalQAWithSourcesChain`"
            )
            return values

        @property
        def _chain_type(self) -> str:
            return "vector_db_qa_with_sources_chain"