from fastapi import Body
from fastapi.responses import StreamingResponse
from configs.model_config import (llm_model_dict, LLM_MODEL, PROMPT_TEMPLATE,
                                  VECTOR_SEARCH_TOP_K)
from server.chat.utils import wrap_done
from server.utils import BaseResponse
import os
from server.knowledge_base.utils import get_kb_path
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from typing import AsyncIterable
import asyncio
from langchain.prompts import PromptTemplate
from server.knowledge_base.utils import lookup_vs
import json


def knowledge_base_chat(query: str = Body(..., description="用户输入", example="你好"),
                        knowledge_base_name: str = Body(..., description="知识库名称", example="samples"),
                        top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
                        ):
    kb_path = get_kb_path(knowledge_base_name)
    if not os.path.exists(kb_path):
        return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")

    async def knowledge_base_chat_iterator(query: str,
                                           knowledge_base_name: str,
                                           top_k: int,
                                           ) -> AsyncIterable[str]:
        callback = AsyncIteratorCallbackHandler()
        model = ChatOpenAI(
            streaming=True,
            verbose=True,
            callbacks=[callback],
            openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
            openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
            model_name=LLM_MODEL
        )
        docs = lookup_vs(query, knowledge_base_name, top_k)
        context = "\n".join([doc.page_content for doc in docs])
        prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=["context", "question"])

        chain = LLMChain(prompt=prompt, llm=model)

        # Begin a task that runs in the background.
        task = asyncio.create_task(wrap_done(
            chain.acall({"context": context, "question": query}),
            callback.done),
        )

        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)
        ]

        async for token in callback.aiter():
            # Use server-sent-events to stream the response
            yield json.dumps({"answer": token,
                   "docs": source_documents})
        await task

    return StreamingResponse(knowledge_base_chat_iterator(query, knowledge_base_name, top_k),
                             media_type="text/event-stream")