from __future__ import annotations

import re
from typing import Any, Dict, List


TECH_OVERLAP_KEYWORDS = [
    "报错", "日志", "配置", "接口", "插件", "部署", "docker", "python", "openclaw", "机器人", "qdrant", "ollama",
]
ANSWER_KEYWORDS = [
    "先", "然后", "重启", "配置", "日志", "接口", "看一下", "试试", "排查",
    "报错", "原因", "因为", "改成", "装", "部署", "重现", "检查", "确认",
]


def build_conversation_hints(
    recent_messages: List[Dict],
    current_sender: str,
    current_content: str,
    quote_context: Dict[str, Any],
    bot_name: str,
) -> Dict[str, Any]:
    previous_messages = list(recent_messages[:-1]) if recent_messages else []
    recent_window = previous_messages[-4:]
    solver_count = 0
    solver_senders = set()
    current_tokens = extract_overlap_tokens(current_content)
    for item in recent_window:
        sender = str(item.get("sender", "") or "")
        if not sender or sender == current_sender:
            continue
        content = str(item.get("content") or item.get("message") or "").strip().lower()
        if looks_like_answer(content) and has_topic_overlap(current_tokens, content):
            solver_count += 1
            solver_senders.add(sender)

    previous_same_sender_directed = False
    same_sender_recent_count = 0
    bot_name_lower = str(bot_name or "").lower()
    for item in reversed(previous_messages[-6:]):
        sender = str(item.get("sender", "") or "")
        if sender != current_sender:
            continue
        same_sender_recent_count += 1
        content = str(item.get("content") or item.get("message") or "").strip().lower()
        if bool(item.get("is_at")) or (bot_name_lower and bot_name_lower in content):
            previous_same_sender_directed = True
            break

    quote_targets_bot = False
    quote_sender_name = str(quote_context.get("quote_sender_name", "") or "").strip().lower()
    if quote_sender_name and bot_name_lower and bot_name_lower in quote_sender_name:
        quote_targets_bot = True

    return {
        "has_recent_human_solver": solver_count >= 2 and len(solver_senders) >= 1,
        "solver_count": solver_count,
        "previous_same_sender_directed": previous_same_sender_directed,
        "same_sender_recent_count": same_sender_recent_count,
        "quote_targets_bot": quote_targets_bot,
    }


def looks_like_answer(content: str) -> bool:
    if not content:
        return False
    if len(content) >= 18:
        return True
    return any(keyword in content for keyword in ANSWER_KEYWORDS)


def extract_overlap_tokens(content: str) -> set[str]:
    text = str(content or "").lower()
    tokens = set(re.findall(r"[a-z0-9_\\-]{3,}", text))
    for keyword in TECH_OVERLAP_KEYWORDS:
        if keyword in text:
            tokens.add(keyword)
    return tokens


def has_topic_overlap(current_tokens: set[str], previous_content: str) -> bool:
    if not current_tokens:
        return False
    previous_tokens = extract_overlap_tokens(previous_content)
    return bool(current_tokens & previous_tokens)