from fastapi import Body, Request
from langchain.chains.question_answering import load_qa_chain
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import PromptTemplate
from sse_starlette.sse import EventSourceResponse
from fastapi.concurrency import run_in_threadpool
from configs import (LLM_MODELS,
                     VECTOR_SEARCH_TOP_K,
                     SCORE_THRESHOLD,
                     TEMPERATURE,
                     USE_RERANKER,
                     RERANKER_MODEL,
                     RERANKER_MAX_LENGTH,
                     MODEL_PATH,
                     MAX_TOKENS,
                     MAX_CUT_TOKENS, HISTORY_LEN)
from server.utils import wrap_done, get_ChatOpenAI
from server.utils import BaseResponse, get_prompt_template, get_format_template
from server.utils import get_strategy_prompt_template
from langchain.chains import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from typing import AsyncIterable, List, Optional
import asyncio
from langchain.prompts.chat import ChatPromptTemplate
from server.chat.utils import History
from server.knowledge_base.kb_service.base import KBServiceFactory
import json
from urllib.parse import urlencode
from server.knowledge_base.kb_doc_api import search_docs
from server.reranker.reranker import LangchainReranker
from server.utils import embedding_device
from server.chat.policy_fun import add_summary_retrieved_results
from server.chat.policy_fun_iast import get_llm_model_response
import json
from configs.basic_config import *

async def knowledge_base_chat_old(query: str = Body(..., description="用户输入", examples=["你好"]),
                                  fileName: List = Body([], description="文件名称", examples=[["123.txt"]]),
                                  knowledge_base_name: str = Body(..., description="知识库名称",
                                                                  examples=["t_policy_total_bge_v1"]),
                                  top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
                                  score_threshold: float = Body(
                                      SCORE_THRESHOLD,
                                      description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
                                      ge=0,
                                      le=2
                                  ),
                                  history: List[History] = Body(
                                      [],
                                      description="历史对话",
                                      examples=[[
                                          {"role": "user",
                                           "content": "我们来玩成语接龙，我先来，生龙活虎"},
                                          {"role": "assistant",
                                           "content": "虎头虎脑"}]]
                                  ),
                                  stream: bool = Body(False, description="流式输出"),
                                  model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
                                  temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
                                  max_tokens: Optional[int] = Body(
                                      MAX_TOKENS,
                                      description="限制LLM生成Token数量，默认None代表模型最大值"
                                  ),
                                  prompt_name: str = Body(
                                      "default",
                                      description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
                                  ),
                                  request: Request = None,
                                  use_summary=False,
                                  chunk_size: int = 20000,
                                  min_chunk_size: int = 2000,
                                  summary_model_name=LLM_MODELS[0],
                                  query_rewrite_model_name=LLM_MODELS[0]
                                  ):
    kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
    if kb is None:
        return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
    logger.info(f'当前知识库：{knowledge_base_name}')
    history = [History.from_data(h) for h in history]
    async def knowledge_base_chat_old_iterator(
            query: str,
            top_k: int,
            history: Optional[List[History]],
            model_name: str = model_name,
            prompt_name: str = prompt_name,
    ) -> AsyncIterable[str]:
        nonlocal max_tokens
        callback = AsyncIteratorCallbackHandler()
        memory = None
        if isinstance(max_tokens, int) and max_tokens <= 0:
            max_tokens = None

        model = get_ChatOpenAI(
            model_name=model_name,
            temperature=temperature,
            max_tokens=max_tokens,
            callbacks=[callback],
        )
        docs = await run_in_threadpool(search_docs,
                                       fileName=fileName,
                                       query=query,
                                       knowledge_base_name=knowledge_base_name,
                                       top_k=top_k,
                                       score_threshold=score_threshold)
        context = "\n".join([doc.page_content for doc in docs])
        context = "\n".join([doc.page_content for doc in docs])
        prompt_template = get_prompt_template("knowledge_base_chat", "Question Assistant")
        input_msg = History(role="system", content=prompt_template).to_msg_template(False)
        chat_prompt = ChatPromptTemplate.from_messages([input_msg])
        chain = LLMChain(prompt=chat_prompt, llm=model, verbose=True)

        task = asyncio.create_task(wrap_done(
            chain.acall({"context": context,
                         "question": query,
                        }),
            callback.done),
        )

        source_documents = []

        if stream:
            answer = ""
            async for token in callback.aiter():
                answer += token
                # Use server-sent-events to stream the response
                yield json.dumps({"answer": token}, ensure_ascii=False)
            logger.info(f"推荐问题：\n{answer}")
        else:
            answer = ""
            async for token in callback.aiter():
                answer += token
            yield json.dumps({"answer": answer})
            logger.info(f"推荐问题：\n{answer}")
        await task
        yield json.dumps({"docs": source_documents}, ensure_ascii=False)

    return EventSourceResponse(knowledge_base_chat_old_iterator(query, top_k, history, model_name, prompt_name))