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# Source code for langchain.chains.conversational_retrieval.base

    """Chain for chatting with a vector database."""
    from __future__ import annotations

    import inspect
    import warnings
    from abc import abstractmethod
    from pathlib import Path
    from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

    from langchain_core._api import deprecated
    from langchain_core.callbacks import (
        AsyncCallbackManagerForChainRun,
        CallbackManagerForChainRun,
        Callbacks,
    )
    from langchain_core.documents import Document
    from langchain_core.language_models import BaseLanguageModel
    from langchain_core.messages import BaseMessage
    from langchain_core.prompts import BasePromptTemplate
    from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
    from langchain_core.retrievers import BaseRetriever
    from langchain_core.runnables import RunnableConfig
    from langchain_core.vectorstores import VectorStore

    from langchain.chains.base import Chain
    from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
    from langchain.chains.combine_documents.stuff import StuffDocumentsChain
    from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
    from langchain.chains.llm import LLMChain
    from langchain.chains.question_answering import load_qa_chain

    # Depending on the memory type and configuration, the chat history format may differ.
    # This needs to be consolidated.
    CHAT_TURN_TYPE = Union[Tuple[str, str], BaseMessage]

    _ROLE_MAP = {"human": "Human: ", "ai": "Assistant: "}

    def _get_chat_history(chat_history: List[CHAT_TURN_TYPE]) -> str:
        buffer = ""
        for dialogue_turn in chat_history:
            if isinstance(dialogue_turn, BaseMessage):
                role_prefix = _ROLE_MAP.get(dialogue_turn.type, f"{dialogue_turn.type}: ")
                buffer += f"\n{role_prefix}{dialogue_turn.content}"
            elif isinstance(dialogue_turn, tuple):
                human = "Human: " + dialogue_turn[0]
                ai = "Assistant: " + dialogue_turn[1]
                buffer += "\n" + "\n".join([human, ai])
            else:
                raise ValueError(
                    f"Unsupported chat history format: {type(dialogue_turn)}."
                    f" Full chat history: {chat_history} "
                )
        return buffer

    class InputType(BaseModel):
        """Input type for ConversationalRetrievalChain."""

        question: str
        """The question to answer."""
        chat_history: List[CHAT_TURN_TYPE] = Field(default_factory=list)
        """The chat history to use for retrieval."""

    class BaseConversationalRetrievalChain(Chain):
        """Chain for chatting with an index."""

        combine_docs_chain: BaseCombineDocumentsChain
        """The chain used to combine any retrieved documents."""
        question_generator: LLMChain
        """The chain used to generate a new question for the sake of retrieval.
        This chain will take in the current question (with variable `question`)
        and any chat history (with variable `chat_history`) and will produce
        a new standalone question to be used later on."""
        output_key: str = "answer"
        """The output key to return the final answer of this chain in."""
        rephrase_question: bool = True
        """Whether or not to pass the new generated question to the combine_docs_chain.
        If True, will pass the new generated question along.
        If False, will only use the new generated question for retrieval and pass the
        original question along to the combine_docs_chain."""
        return_source_documents: bool = False
        """Return the retrieved source documents as part of the final result."""
        return_generated_question: bool = False
        """Return the generated question as part of the final result."""
        get_chat_history: Optional[Callable[[List[CHAT_TURN_TYPE]], str]] = None
        """An optional function to get a string of the chat history.
        If None is provided, will use a default."""
        response_if_no_docs_found: Optional[str]
        """If specified, the chain will return a fixed response if no docs
        are found for the question. """

        class Config:
            """Configuration for this pydantic object."""

            extra = Extra.forbid
            arbitrary_types_allowed = True
            allow_population_by_field_name = True

        @property
        def input_keys(self) -> List[str]:
            """Input keys."""
            return ["question", "chat_history"]

        def get_input_schema(
            self, config: Optional[RunnableConfig] = None
        ) -> Type[BaseModel]:
            return InputType

        @property
        def output_keys(self) -> List[str]:
            """Return the output keys.

            :meta private:
            """
            _output_keys = [self.output_key]
            if self.return_source_documents:
                _output_keys = _output_keys + ["source_documents"]
            if self.return_generated_question:
                _output_keys = _output_keys + ["generated_question"]
            return _output_keys

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

        def _call(
            self,
            inputs: Dict[str, Any],
            run_manager: Optional[CallbackManagerForChainRun] = None,
        ) -> Dict[str, Any]:
            _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
            question = inputs["question"]
            get_chat_history = self.get_chat_history or _get_chat_history
            chat_history_str = get_chat_history(inputs["chat_history"])

            if chat_history_str:
                callbacks = _run_manager.get_child()
                new_question = self.question_generator.run(
                    question=question, chat_history=chat_history_str, callbacks=callbacks
                )
            else:
                new_question = question
            accepts_run_manager = (
                "run_manager" in inspect.signature(self._get_docs).parameters
            )
            if accepts_run_manager:
                docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
            else:
                docs = self._get_docs(new_question, inputs)  # type: ignore[call-arg]
            output: Dict[str, Any] = {}
            if self.response_if_no_docs_found is not None and len(docs) == 0:
                output[self.output_key] = self.response_if_no_docs_found
            else:
                new_inputs = inputs.copy()
                if self.rephrase_question:
                    new_inputs["question"] = new_question
                new_inputs["chat_history"] = chat_history_str
                answer = self.combine_docs_chain.run(
                    input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
                )
                output[self.output_key] = answer

            if self.return_source_documents:
                output["source_documents"] = docs
            if self.return_generated_question:
                output["generated_question"] = new_question
            return output

        @abstractmethod
        async def _aget_docs(
            self,
            question: str,
            inputs: Dict[str, Any],
            *,
            run_manager: AsyncCallbackManagerForChainRun,
        ) -> List[Document]:
            """Get docs."""

        async def _acall(
            self,
            inputs: Dict[str, Any],
            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
        ) -> Dict[str, Any]:
            _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
            question = inputs["question"]
            get_chat_history = self.get_chat_history or _get_chat_history
            chat_history_str = get_chat_history(inputs["chat_history"])
            if chat_history_str:
                callbacks = _run_manager.get_child()
                new_question = await self.question_generator.arun(
                    question=question, chat_history=chat_history_str, callbacks=callbacks
                )
            else:
                new_question = question
            accepts_run_manager = (
                "run_manager" in inspect.signature(self._aget_docs).parameters
            )
            if accepts_run_manager:
                docs = await self._aget_docs(new_question, inputs, run_manager=_run_manager)
            else:
                docs = await self._aget_docs(new_question, inputs)  # type: ignore[call-arg]

            output: Dict[str, Any] = {}
            if self.response_if_no_docs_found is not None and len(docs) == 0:
                output[self.output_key] = self.response_if_no_docs_found
            else:
                new_inputs = inputs.copy()
                if self.rephrase_question:
                    new_inputs["question"] = new_question
                new_inputs["chat_history"] = chat_history_str
                answer = await self.combine_docs_chain.arun(
                    input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
                )
                output[self.output_key] = answer

            if self.return_source_documents:
                output["source_documents"] = docs
            if self.return_generated_question:
                output["generated_question"] = new_question
            return output

        def save(self, file_path: Union[Path, str]) -> None:
            if self.get_chat_history:
                raise ValueError("Chain not saveable when `get_chat_history` is not None.")
            super().save(file_path)

    @deprecated(
        since="0.1.17",
        alternative=(
            "create_history_aware_retriever together with create_retrieval_chain "
            "(see example in docstring)"
        ),
        removal="0.3.0",
    )
    class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
        """Chain for having a conversation based on retrieved documents.

        This class is deprecated. See below for an example implementation using
        `create_retrieval_chain`. Additional walkthroughs can be found at
        https://python.langchain.com/docs/use_cases/question_answering/chat_history

            .. code-block:: python

                from langchain.chains import (
                    create_history_aware_retriever,
                    create_retrieval_chain,
                )
                from langchain.chains.combine_documents import create_stuff_documents_chain
                from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
                from langchain_openai import ChatOpenAI

                retriever = ...  # Your retriever

                llm = ChatOpenAI()

                # Contextualize question
                contextualize_q_system_prompt = (
                    "Given a chat history and the latest user question "
                    "which might reference context in the chat history, "
                    "formulate a standalone question which can be understood "
                    "without the chat history. Do NOT answer the question, just "
                    "reformulate it if needed and otherwise return it as is."
                )
                contextualize_q_prompt = ChatPromptTemplate.from_messages(
                    [
                        ("system", contextualize_q_system_prompt),
                        MessagesPlaceholder("chat_history"),
                        ("human", "{input}"),
                    ]
                )
                history_aware_retriever = create_history_aware_retriever(
                    llm, retriever, contextualize_q_prompt
                )

                # Answer question
                qa_system_prompt = (
                    "You are an assistant for question-answering tasks. Use "
                    "the following pieces of retrieved context to answer the "
                    "question. If you don't know the answer, just say that you "
                    "don't know. Use three sentences maximum and keep the answer "
                    "concise."
                    "\n\n"
                    "{context}"
                )
                qa_prompt = ChatPromptTemplate.from_messages(
                    [
                        ("system", qa_system_prompt),
                        MessagesPlaceholder("chat_history"),
                        ("human", "{input}"),
                    ]
                )
                # Below we use create_stuff_documents_chain to feed all retrieved context
                # into the LLM. Note that we can also use StuffDocumentsChain and other
                # instances of BaseCombineDocumentsChain.
                question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
                rag_chain = create_retrieval_chain(
                    history_aware_retriever, question_answer_chain
                )

                # Usage:
                chat_history = []  # Collect chat history here (a sequence of messages)
                rag_chain.invoke({"input": query, "chat_history": chat_history})

        This chain takes in chat history (a list of messages) and new questions,
        and then returns an answer to that question.
        The algorithm for this chain consists of three parts:

        1. Use the chat history and the new question to create a "standalone question".
        This is done so that this question can be passed into the retrieval step to fetch
        relevant documents. If only the new question was passed in, then relevant context
        may be lacking. If the whole conversation was passed into retrieval, there may
        be unnecessary information there that would distract from retrieval.

        2. This new question is passed to the retriever and relevant documents are
        returned.

        3. The retrieved documents are passed to an LLM along with either the new question
        (default behavior) or the original question and chat history to generate a final
        response.

        Example:
            .. code-block:: python

                from langchain.chains import (
                    StuffDocumentsChain, LLMChain, ConversationalRetrievalChain
                )
                from langchain_core.prompts import PromptTemplate
                from langchain_community.llms import OpenAI

                combine_docs_chain = StuffDocumentsChain(...)
                vectorstore = ...
                retriever = vectorstore.as_retriever()

                # This controls how the standalone question is generated.
                # Should take `chat_history` and `question` as input variables.
                template = (
                    "Combine the chat history and follow up question into "
                    "a standalone question. Chat History: {chat_history}"
                    "Follow up question: {question}"
                )
                prompt = PromptTemplate.from_template(template)
                llm = OpenAI()
                question_generator_chain = LLMChain(llm=llm, prompt=prompt)
                chain = ConversationalRetrievalChain(
                    combine_docs_chain=combine_docs_chain,
                    retriever=retriever,
                    question_generator=question_generator_chain,
                )
        """

        retriever: BaseRetriever
        """Retriever to use to fetch documents."""
        max_tokens_limit: Optional[int] = None
        """If set, enforces that the documents returned are less than this limit.
        This is only enforced if `combine_docs_chain` is of type StuffDocumentsChain."""

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

            if self.max_tokens_limit and isinstance(
                self.combine_docs_chain, StuffDocumentsChain
            ):
                tokens = [
                    self.combine_docs_chain.llm_chain._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,
            question: str,
            inputs: Dict[str, Any],
            *,
            run_manager: CallbackManagerForChainRun,
        ) -> List[Document]:
            """Get docs."""
            docs = self.retriever.invoke(
                question, config={"callbacks": run_manager.get_child()}
            )
            return self._reduce_tokens_below_limit(docs)

        async def _aget_docs(
            self,
            question: str,
            inputs: Dict[str, Any],
            *,
            run_manager: AsyncCallbackManagerForChainRun,
        ) -> List[Document]:
            """Get docs."""
            docs = await self.retriever.ainvoke(
                question, config={"callbacks": run_manager.get_child()}
            )
            return self._reduce_tokens_below_limit(docs)

        @classmethod
        def from_llm(
            cls,
            llm: BaseLanguageModel,
            retriever: BaseRetriever,
            condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
            chain_type: str = "stuff",
            verbose: bool = False,
            condense_question_llm: Optional[BaseLanguageModel] = None,
            combine_docs_chain_kwargs: Optional[Dict] = None,
            callbacks: Callbacks = None,
            **kwargs: Any,
        ) -> BaseConversationalRetrievalChain:
            """Convenience method to load chain from LLM and retriever.

            This provides some logic to create the `question_generator` chain
            as well as the combine_docs_chain.

            Args:
                llm: The default language model to use at every part of this chain
                    (eg in both the question generation and the answering)
                retriever: The retriever to use to fetch relevant documents from.
                condense_question_prompt: The prompt to use to condense the chat history
                    and new question into a standalone question.
                chain_type: The chain type to use to create the combine_docs_chain, will
                    be sent to `load_qa_chain`.
                verbose: Verbosity flag for logging to stdout.
                condense_question_llm: The language model to use for condensing the chat
                    history and new question into a standalone question. If none is
                    provided, will default to `llm`.
                combine_docs_chain_kwargs: Parameters to pass as kwargs to `load_qa_chain`
                    when constructing the combine_docs_chain.
                callbacks: Callbacks to pass to all subchains.
                **kwargs: Additional parameters to pass when initializing
                    ConversationalRetrievalChain
            """
            combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
            doc_chain = load_qa_chain(
                llm,
                chain_type=chain_type,
                verbose=verbose,
                callbacks=callbacks,
                **combine_docs_chain_kwargs,
            )

            _llm = condense_question_llm or llm
            condense_question_chain = LLMChain(
                llm=_llm,
                prompt=condense_question_prompt,
                verbose=verbose,
                callbacks=callbacks,
            )
            return cls(
                retriever=retriever,
                combine_docs_chain=doc_chain,
                question_generator=condense_question_chain,
                callbacks=callbacks,
                **kwargs,
            )

    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"

        @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],
            *,
            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."""
            raise NotImplementedError("ChatVectorDBChain does not support async")

        @classmethod
        def from_llm(
            cls,
            llm: BaseLanguageModel,
            vectorstore: VectorStore,
            condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
            chain_type: str = "stuff",
            combine_docs_chain_kwargs: Optional[Dict] = 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,
            )