"""Customer-service intent recognition contract."""
from __future__ import annotations

import json
import logging
import re
from dataclasses import dataclass
from enum import StrEnum
from inspect import isawaitable


logger = logging.getLogger("rag.intent")


class Intent(StrEnum):
    GUIDE_PURCHASE = "guide_purchase"
    WANT_ADVISOR = "want_advisor"
    NL2SQL_REQUEST = "nl2sql_request"
    KNOWLEDGE_QA = "knowledge_qa"
    COMPANY_INFO = "company_info"
    CHITCHAT = "chitchat"
    OFF_TOPIC = "off_topic"
    NO_MATCH = "no_match"


INTENT_VALUES = frozenset(item.value for item in Intent)

# 按长度降序匹配，避免短标签误命中长标签的一部分
_INTENT_PATTERN = re.compile(
    "|".join(re.escape(value) for value in sorted(INTENT_VALUES, key=len, reverse=True))
)
_JSON_PATTERN = re.compile(r"\{.*\}", re.S)

# 改写句允许的最大长度：相对原句的倍数与绝对下限取大者，防止模型把历史大段塞进改写句
_REWRITE_MAX_RATIO = 4
_REWRITE_MIN_LIMIT = 200

INTENT_SYSTEM_PROMPT = (
    "你是华夏科技（一家基金代销金融机构）智能客服的意图分类器。"
    "你会看到最近几轮对话（可能为空）和用户的【当前输入】。\n"
    "任务：\n"
    "1. 只对【当前输入】判定意图；【最近对话】仅用于理解当前输入中省略的主语或指代"
    "（如“它”“那个”“呢”“还有吗”）。\n"
    "2. 若当前输入依赖上文才能理解，请把它改写为一句不依赖上文的完整问题，"
    "只能补全上文已明确出现的对象，不得添加新信息、不得改变原意；"
    "若当前输入本身已完整，query 原样返回。\n"
    "3. 当前输入若明显切换到新话题，以当前输入为准，不要延续上一轮的意图。\n"
    "4. 致谢、告别、寒暄即使出现在知识问答之后也归为 chitchat。\n"
    "意图选项：\n"
    "- guide_purchase: 用户询问如何购买基金、开户、注册等引导类问题\n"
    "- want_advisor: 用户希望获得个性化基金推荐或投资顾问服务\n"
    "- knowledge_qa: 用户询问基金相关的知识性问题，如净值、费率、风险、申赎规则等\n"
    "- company_info: 用户询问华夏科技公司本身的信息，如公司全称、成立时间、牌照、总部地址、"
    "客服电话、服务时间、官网、投诉渠道等\n"
    "- nl2sql_request: 用户要求查询具体数据或账户信息\n"
    "- chitchat: 普通寒暄，如问候、致谢、告别、询问你是谁/你能做什么、在吗等一两句话的闲聊\n"
    "- off_topic: 用户要求你实质性地处理与金融、基金、公司业务无关的事情，"
    "如写代码、讲笑话、写作文、问天气、聊政治、情感咨询、做数学题等\n"
    "- no_match: 无法归入以上任何一类\n"
    "注意：寒暄性质的一两句话归为 chitchat；一旦用户提出金融之外的实质性请求，归为 off_topic。\n"
    "只输出一行 JSON，格式：{\"intent\": \"<小写英文标签>\", \"query\": \"<完整问题>\"}，"
    "不要输出解释、Markdown 或其他内容。"
)


@dataclass(frozen=True)
class IntentResult:
    intent: Intent
    # 结合历史补全后的独立问题；无需补全或解析失败时等于原句
    query: str
    used_history: bool = False


async def _config_value(config_getter, key: str, default):
    value = config_getter(key, str(default))
    if isawaitable(value):
        value = await value
    return type(default)(value)


def parse_intent(raw: str | None) -> Intent:
    """从模型原始输出中提取第一个合法标签，提取不到则返回 NO_MATCH。"""
    if not raw:
        return Intent.NO_MATCH
    match = _INTENT_PATTERN.search(raw.lower())
    return Intent(match.group(0)) if match else Intent.NO_MATCH


def parse_intent_result(raw: str | None, *, fallback_query: str, used_history: bool = False) -> IntentResult:
    """优先按 JSON 解析意图与改写句；JSON 不可用时退回正则抽标签、原句作为 query。"""
    match = _JSON_PATTERN.search(raw) if raw else None
    data = None
    if match:
        try:
            data = json.loads(match.group(0))
        except ValueError:
            data = None
    if not isinstance(data, dict):
        return IntentResult(parse_intent(raw), fallback_query, used_history)
    intent = parse_intent(str(data.get("intent") or ""))
    rewritten = str(data.get("query") or "").strip()
    limit = max(len(fallback_query) * _REWRITE_MAX_RATIO, _REWRITE_MIN_LIMIT)
    query = rewritten if rewritten and len(rewritten) <= limit else fallback_query
    return IntentResult(intent, query, used_history)


def render_history(history, *, max_turns: int, max_chars: int) -> str:
    """把最近 max_turns 轮 user/assistant 消息折叠成一段文本，每条截断到 max_chars。"""
    if max_turns <= 0 or not history:
        return ""
    recent = [
        message for message in history
        if isinstance(message, dict)
        and message.get("role") in ("user", "assistant")
        and message.get("content")
    ][-max_turns * 2:]
    lines = []
    for message in recent:
        role = "用户" if message["role"] == "user" else "客服"
        content = str(message["content"]).replace("\n", " ").strip()
        if len(content) > max_chars:
            content = content[:max_chars] + "…"
        lines.append(f"{role}：{content}")
    return "\n".join(lines)


def build_intent_messages(query: str, rendered_history: str) -> list[dict]:
    if rendered_history:
        user_content = f"【最近对话】\n{rendered_history}\n\n【当前输入】\n{query}"
    else:
        user_content = f"【当前输入】\n{query}"
    return [
        {"role": "system", "content": INTENT_SYSTEM_PROMPT},
        {"role": "user", "content": user_content},
    ]


async def intent_recognize(
    query: str,
    *,
    llm_client,
    history: list[dict] | None = None,
    config_getter=None,
) -> IntentResult:
    """结合最近几轮对话识别当前输入的意图，并给出补全指代后的独立问题。

    history 为空或 agent.customer.intent.history_turns 配置为 0 时退化为仅看当前句。
    """
    if not query or not query.strip():
        return IntentResult(Intent.NO_MATCH, query or "")
    max_turns, max_chars = 3, 200
    if config_getter is not None:
        max_turns = await _config_value(config_getter, "agent.customer.intent.history_turns", max_turns)
        max_chars = await _config_value(config_getter, "agent.customer.intent.history_max_chars", max_chars)
    rendered = render_history(history, max_turns=max_turns, max_chars=max_chars)
    messages = build_intent_messages(query, rendered)
    try:
        raw = await llm_client.chat(messages)
    except Exception:
        logger.exception("intent recognition failed")
        return IntentResult(Intent.NO_MATCH, query, bool(rendered))
    return parse_intent_result(raw, fallback_query=query, used_history=bool(rendered))