from fastapi import Request
import os, asyncio
from typing import List, Optional, Dict

from langchain import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
from langchain.prompts.chat import ChatPromptTemplate
from langchain.docstore.document import Document

from configs.model_config import (
    PROMPT_TEMPLATE, SEARCH_ENGINE_TOP_K, BING_SUBSCRIPTION_KEY, BING_SEARCH_URL,
    VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD)
from dev_opsgpt.chat.utils import History, wrap_done
from dev_opsgpt.utils import BaseResponse
from .base_chat import Chat

from loguru import logger

from duckduckgo_search import DDGS


def bing_search(text, result_len=SEARCH_ENGINE_TOP_K):
    if not (BING_SEARCH_URL and BING_SUBSCRIPTION_KEY):
        return [{"snippet": "please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV",
                 "title": "env info is not found",
                 "link": "https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}]
    search = BingSearchAPIWrapper(bing_subscription_key=BING_SUBSCRIPTION_KEY,
                                  bing_search_url=BING_SEARCH_URL)
    return search.results(text, result_len)


def duckduckgo_search(
        query: str,
        result_len: int = SEARCH_ENGINE_TOP_K,
        region: Optional[str] = "wt-wt", 
        safesearch: str = "moderate", 
        time: Optional[str] = "y",
        backend: str = "api",
        ):
    with DDGS(proxies=os.environ.get("DUCKDUCKGO_PROXY")) as ddgs:
        results = ddgs.text(
            query,
            region=region,
            safesearch=safesearch,
            timelimit=time,
            backend=backend,
        )
        if results is None:
            return [{"Result": "No good DuckDuckGo Search Result was found"}]

        def to_metadata(result: Dict) -> Dict[str, str]:
            if backend == "news":
                return {
                    "date": result["date"],
                    "title": result["title"],
                    "snippet": result["body"],
                    "source": result["source"],
                    "link": result["url"],
                }
            return {
                "snippet": result["body"],
                "title": result["title"],
                "link": result["href"],
            }

        formatted_results = []
        for i, res in enumerate(results, 1):
            if res is not None:
                formatted_results.append(to_metadata(res))
            if len(formatted_results) == result_len:
                break
    return formatted_results


# def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K):
#     search = DuckDuckGoSearchAPIWrapper()
#     return search.results(text, result_len)


SEARCH_ENGINES = {"duckduckgo": duckduckgo_search,
                  "bing": bing_search,
                  }


def search_result2docs(search_results):
    docs = []
    for result in search_results:
        doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "",
                       metadata={"source": result["link"] if "link" in result.keys() else "",
                                 "filename": result["title"] if "title" in result.keys() else ""})
        docs.append(doc)
    return docs


def lookup_search_engine(
        query: str,
        search_engine_name: str,
        top_k: int = SEARCH_ENGINE_TOP_K,
):
    results = SEARCH_ENGINES[search_engine_name](query, result_len=top_k)
    docs = search_result2docs(results)
    return docs



class SearchChat(Chat):

    def __init__(
            self,
            engine_name: str = "",
            top_k: int = VECTOR_SEARCH_TOP_K,
            stream: bool = False,
            ) -> None:
        super().__init__(engine_name, top_k, stream)

    def check_service_status(self) -> BaseResponse:
        if self.engine_name not in SEARCH_ENGINES.keys():
            return BaseResponse(code=404, msg=f"未支持搜索引擎 {self.engine_name}")
        return BaseResponse(code=200, msg=f"支持搜索引擎 {self.engine_name}")
    
    def _process(self, query: str, history: List[History], model):
        '''process'''
        docs = lookup_search_engine(query, self.engine_name, self.top_k)
        context = "\n".join([doc.page_content for doc in docs])

        source_documents = [
            f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n"""
            for inum, doc in enumerate(docs)
        ]
        
        chat_prompt = ChatPromptTemplate.from_messages(
            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)]
        )
        chain = LLMChain(prompt=chat_prompt, llm=model)
        result = {"answer": "", "docs": source_documents}
        return chain, context, result

    def create_task(self, query: str, history: List[History], model):
        '''构建 llm 生成任务'''
        chain, context, result = self._process(query, history, model)
        content = chain({"context": context, "question": query})
        return result, content

    def create_atask(self, query, history, model, callback: AsyncIteratorCallbackHandler):
        chain, context, result = self._process(query, history, model)
        task = asyncio.create_task(wrap_done(
            chain.acall({"context": context, "question": query}), callback.done
        ))
        return task, result