qa_prompt: Optional[BasePromptTemplate] = None,
        chain_type: str = "stuff",
        **kwargs: Any,
    ) -> BaseConversationalRetrievalChain:
        """Load chain from LLM."""
        doc_chain = load_qa_chain(
            llm,
            chain_type=chain_type,
            prompt=qa_prompt,
        )
        condense_question_chain = LLMChain(llm=llm, prompt=condense_question_prompt)
        return cls(
            retriever=retriever,
            combine_docs_chain=doc_chain,
            question_generator=condense_question_chain,
            **kwargs,
        )
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain, BaseModel):
    """Chain for chatting with a vector database."""
    vectorstore: VectorStore = Field(alias="vectorstore")
    top_k_docs_for_context: int = 4
    search_kwargs: dict = Field(default_factory=dict)
    @property
    def _chain_type(self) -> str:
        return "chat-vector-db"
    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        warnings.warn(
            "`ChatVectorDBChain` is deprecated - "
            "please use `from langchain.chains import ConversationalRetrievalChain`"
        )
        return values
    def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
        vectordbkwargs = inputs.get("vectordbkwargs", {})
        full_kwargs = {**self.search_kwargs, **vectordbkwargs}
        return self.vectorstore.similarity_search(
            question, k=self.top_k_docs_for_context, **full_kwargs
        )
[docs]    @classmethod