import json
from datetime import datetime
from pathlib import Path
from typing import List, Dict

from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpointEmbeddings
from langgraph.runtime import Runtime
from sentry_sdk.integrations import aiohttp

from app.chat_qa.context import QAContext
from app.chat_qa.qa_state import QAState
from app.client.embedding_client_manager import embedding_client
from app.llm import llm
from app.repository.milvus.message_qa_repository import QARepository
from app.core.log import logger

"""
知识库入库
"""
async def finalize(state: QAState, runtime: Runtime[QAContext]):

    final_qas: List[Dict] = state.get("final_qas", [])
    chat_history: str = state["chat_history"]

    logger.info(f"原对胡数量：{chat_history}最终生成的QA数据：{len(final_qas)}")

    if len(final_qas)==0:
        logger.info(f"生成QA失败，原对胡数量：{chat_history}最终生成的QA数据：{len(final_qas)}")
        return

    output_dir = Path("E:/qa_output")
    output_dir.mkdir(parents=True, exist_ok=True)
    output_file = output_dir / "qa_result.jsonl"  # 改用 .jsonl 后缀

    try:
        with open(output_file, 'a', encoding='utf-8') as f:
            if isinstance(final_qas, list):
                for qa in final_qas:
                    f.write(json.dumps(qa, ensure_ascii=False) + '\n')
            else:
                f.write(json.dumps(final_qas, ensure_ascii=False) + '\n')

        logger.info(f"成功追加 {len(final_qas) if isinstance(final_qas, list) else 1} 条QA数据到文件: {output_file}")

    except Exception as e:
        logger.error(f"写入QA文件失败: {e}")

    # question_list: List[str] = [qa["question"] for qa in final_qas]
    #
    # embedding_batch_size = 10
    # embedding_ed_all: list[list[float]] = []
    # for i in range(0, len(question_list), embedding_batch_size):
    #     embedding_part_texts = question_list[i:i + embedding_batch_size]
    #     embedding_eds = await embedding_client.aembed_documents(embedding_part_texts)
    #     embedding_ed_all.extend(embedding_eds)
    #
    # # 构建对象列表
    # qa_list = []
    # for i, qa in enumerate(final_qas):
    #     qa_list.append({
    #         "question": qa["question"],
    #         "answer": qa.get("answer", ""),
    #         "question_dense_vector": embedding_ed_all[i] if i < len(embedding_ed_all) else [],
    #         "parent_id": qa.get("parent_id", 0)
    #     })
    # await qa_milvus_repository.save_qa_to_milvus(qa_list)