import asyncio
import json
from typing import AsyncIterable, List, Optional
from urllib.parse import urlencode

from fastapi import Body, Request
from fastapi.concurrency import run_in_threadpool
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate
from sse_starlette.sse import EventSourceResponse

from configs import (TEMPERATURE,
                     USE_RERANKER,
                     RERANKER_MODEL,
                     RERANKER_MAX_LENGTH,
                     MODEL_PATH,
                     MAX_TOKENS,
                     MAX_CUT_TOKENS, LLM_MODELS)
from server.chat.utils import History
from server.knowledge_base.kb_service.base import KBServiceFactory
from server.reranker.reranker import LangchainReranker
from server.utils import BaseResponse, get_prompt_template
from server.utils import embedding_device
from server.utils import wrap_done, get_ChatOpenAI
from collections import defaultdict
from server.custom.custom_fun import chapter_overview_summary

async def task(param):
    contextk = param["contextk"]
    i = param["i"]
    model_name = param["model_name"]
    temperature = param["temperature"]
    max_tokens = param["max_tokens"]
    chat_prompt = param["chat_prompt"]
    print(f"i:{i}，len_context:{len(contextk)}\n")
    callback_temp = AsyncIteratorCallbackHandler()
    model_temp = get_ChatOpenAI(
        model_name=model_name,
        temperature=temperature,
        max_tokens=max_tokens,
        callbacks=[callback_temp],
    )
    chain_temp = LLMChain(prompt=chat_prompt, llm=model_temp)
    task_temp = wrap_done(chain_temp.acall({"context": contextk,
                                            "question": "对该部分内容进行总结"}),
                          callback_temp.done)

    await task_temp
    async for token in callback_temp.aiter():
        yield token


# 使用多线程执行任务
async def run_tasks_concurrently(params):
    result = []
    async for data in asyncio.as_completed([task(param) async for param in params]):
        result.append(''.join([token async for token in data]))
    return result


async def chapter_overview(query: str = Body("为我总结这些内容", description="用户输入", examples=["你好"]),
                           knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
                           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(
                               "Chapter Overview",
                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
                           ),
                           source_name_list: List[str] = Body([], description="资源列表"),
                           request: Request = None,
                           ):
   
    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}")
    async def chapter_overview_iterator(
            model_name: str = model_name,
    ) -> AsyncIterable[str]:
        nonlocal max_tokens
        if isinstance(max_tokens, int) and max_tokens <= 0:
            max_tokens = None

        docs = await run_in_threadpool(kb.get_doc_by_sources_name,source_name_list=source_name_list)
        
        chapter_summaries, global_summary = await chapter_overview_summary(docs, model_name, temperature, max_tokens)
        
        if stream:
            for h1, summaries in chapter_summaries.items():
                for summary in summaries:
                    yield json.dumps({"chapter_title": h1, "summary": summary}, ensure_ascii=False)
            yield json.dumps({"global_summary": global_summary}, ensure_ascii=False)
            # yield json.dumps({"docs": source_documents}, ensure_ascii=False)
        else:
            result = {
                "chapter_summaries": chapter_summaries,
                "global_summary": global_summary,
            }
            yield json.dumps(result, ensure_ascii=False)

    return EventSourceResponse(chapter_overview_iterator(model_name=model_name))