ChatVectorDBChain Class — langchain Architecture

Architecture documentation for the ChatVectorDBChain class in base.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  bc11cd5d_a081_308b_a325_0c46b7e4deb4["ChatVectorDBChain"]
  be2fab99_c17a_7327_31b8_3526c30ba2b8["BaseConversationalRetrievalChain"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|extends| be2fab99_c17a_7327_31b8_3526c30ba2b8
  94993a4e_2523_282e_bb19_84e4ccbd2c96["base.py"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|defined in| 94993a4e_2523_282e_bb19_84e4ccbd2c96
  94f644c9_9259_2c31_1318_d68b5ff4c022["_chain_type()"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|method| 94f644c9_9259_2c31_1318_d68b5ff4c022
  fa5cb2d6_a8e6_f030_8bc0_d8c64365a74f["_raise_deprecation()"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|method| fa5cb2d6_a8e6_f030_8bc0_d8c64365a74f
  91f26d93_49d0_6b76_bf53_e69422a76a37["_get_docs()"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|method| 91f26d93_49d0_6b76_bf53_e69422a76a37
  25fe0504_8866_a226_a080_b16cffe920b7["_aget_docs()"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|method| 25fe0504_8866_a226_a080_b16cffe920b7
  a0d1fe2a_c733_6034_7b3d_9e4f623f96c1["from_llm()"]
  bc11cd5d_a081_308b_a325_0c46b7e4deb4 -->|method| a0d1fe2a_c733_6034_7b3d_9e4f623f96c1

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/conversational_retrieval/base.py lines 498–578

class ChatVectorDBChain(BaseConversationalRetrievalChain):
    """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"

    @model_validator(mode="before")
    @classmethod
    def _raise_deprecation(cls, values: dict) -> Any:
        warnings.warn(
            "`ChatVectorDBChain` is deprecated - "
            "please use `from langchain_classic.chains import "
            "ConversationalRetrievalChain`",
            stacklevel=4,
        )
        return values

    @override
    def _get_docs(
        self,
        question: str,
        inputs: dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> list[Document]:
        """Get docs."""
        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,
        )

    async def _aget_docs(
        self,
        question: str,
        inputs: dict[str, Any],
        *,
        run_manager: AsyncCallbackManagerForChainRun,
    ) -> list[Document]:
        """Get docs."""
        msg = "ChatVectorDBChain does not support async"
        raise NotImplementedError(msg)

    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        vectorstore: VectorStore,
        condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
        chain_type: str = "stuff",
        combine_docs_chain_kwargs: dict | None = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> BaseConversationalRetrievalChain:
        """Load chain from LLM."""
        combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
        doc_chain = load_qa_chain(
            llm,
            chain_type=chain_type,
            callbacks=callbacks,
            **combine_docs_chain_kwargs,
        )
        condense_question_chain = LLMChain(
            llm=llm,
            prompt=condense_question_prompt,
            callbacks=callbacks,
        )
        return cls(
            vectorstore=vectorstore,
            combine_docs_chain=doc_chain,
            question_generator=condense_question_chain,
            callbacks=callbacks,
            **kwargs,
        )