import logging

import httpx

from app.config import LOW_SCORE, llm_config

logger = logging.getLogger("learn_rag")

SYSTEM_PROMPT = """你是「知迹」的知识库问答助手。只能根据给出的知识切片回答，不要补充切片之外的信息，也不要编造。
如果切片不足以回答，就说明知识库里没有这项信息。
用自然的中文直接回答问题，不要使用 Markdown，不要写标题。
引用事实时在相关句末标注编号，格式必须是〔来源1〕这种全角括号。编号只能使用给定切片的编号。"""


def build_context(chunks: list[dict]) -> str:
    blocks = []
    for chunk in chunks:
        blocks.append(
            f"〔来源{chunk['n']}〕《{chunk['title']}》相关度 {chunk['score']:.0%}\n{chunk['text']}"
        )
    return "\n\n".join(blocks)


def extractive_answer(chunks: list[dict], low_confidence: bool) -> str:
    if not chunks:
        return "知识库里还没有可以检索的内容。"
    lead = ""
    if low_confidence:
        lead = "最接近的切片和问题只是部分相关，下面按原文摘出，请以右侧切片为准。\n\n"
    lines = [lead + chunks[0]["text"].strip() + f"〔来源{chunks[0]['n']}〕"]
    for chunk in chunks[1:2]:
        if chunks[0]["score"] - chunk["score"] <= 0.08:
            lines.append("\n" + chunk["text"].strip() + f"〔来源{chunk['n']}〕")
    return "\n".join(lines).strip()


def generate(question: str, chunks: list[dict]) -> tuple[str, str, str | None, bool]:
    low_confidence = not chunks or chunks[0]["score"] < LOW_SCORE
    if not chunks:
        return "知识库里还没有可以检索的内容。", "extractive", None, True

    settings = llm_config()
    if settings is None:
        notice = "还没有配置大模型，回答直接摘自相关度最高的切片。在项目目录的 .env 中填入 OPENAI_API_KEY 后重启，即可生成连贯回答。"
        return extractive_answer(chunks, low_confidence), "extractive", notice, low_confidence

    caution = ""
    if low_confidence:
        caution = "注意：最高相关度偏低。如果切片不能直接支持答案，请明确说知识库里没有这项信息。\n\n"
    user_prompt = f"{caution}知识切片如下：\n\n{build_context(chunks)}\n\n问题：{question}"
    try:
        answer = call_llm(settings, user_prompt)
    except Exception:
        logger.exception("大模型调用失败")
        notice = "大模型暂时不可用，已改为直接摘录检索到的切片。"
        return extractive_answer(chunks, low_confidence), "extractive", notice, low_confidence
    return answer, "llm", None, low_confidence


def call_llm(settings: dict, user_prompt: str) -> str:
    response = httpx.post(
        f"{settings['base']}/chat/completions",
        headers={"Authorization": f"Bearer {settings['key']}"},
        json={
            "model": settings["model"],
            "temperature": 0.2,
            "max_tokens": 800,
            "messages": [
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_prompt},
            ],
        },
        timeout=httpx.Timeout(60.0, connect=10.0),
    )
    response.raise_for_status()
    message = response.json()["choices"][0]["message"]
    content = message.get("content")
    if isinstance(content, list):
        content = "".join(
            part.get("text", "") if isinstance(part, dict) else str(part) for part in content
        )
    text = (content or "").strip()
    if not text:
        raise ValueError("模型返回了空回答")
    return text