"""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, Union

from pydantic import Extra, Field, root_validator

from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
    Callbacks,
)
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
from langchain.schema import BasePromptTemplate, BaseRetriever, Document
from langchain.schema.messages import BaseMessage
from langchain.vectorstores.base import VectorStore

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

    combine_docs_chain: BaseCombineDocumentsChain
    question_generator: LLMChain
    output_key: str = "answer"
    return_source_documents: bool = False
    return_generated_question: bool = False
    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
    """Return the source documents."""

    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"]

    @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]
        new_inputs = inputs.copy()
        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: Dict[str, Any] = {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]

        new_inputs = inputs.copy()
        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: Dict[str, Any] = {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 savable when `get_chat_history` is not None.")
        super().save(file_path)


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

    retriever: BaseRetriever
    """Index to connect to."""
    max_tokens_limit: Optional[int] = None
    """If set, restricts the docs to return from store based on tokens, enforced only
    for StuffDocumentChain"""

    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.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,
        question: str,
        inputs: Dict[str, Any],
        *,
        run_manager: CallbackManagerForChainRun,
    ) -> List[Document]:
        """Get docs."""
        docs = self.retriever.get_relevant_documents(
            question, 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.aget_relevant_documents(
            question, 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:
        """Load chain from LLM."""
        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,
        )
