# -*- coding: utf-8 -*-
"""构建文档对话检索查询文本和关键词。"""

from __future__ import annotations

import re
from typing import Any, Dict, List, Optional, Sequence

from core.document_chat.retrieval.config import DEFAULT_ACTION_TERMS, DEFAULT_DOMAIN_TERMS


def build_query(
    state: Dict[str, Any],
    domain_terms: Optional[Sequence[str]] = None,
    action_terms: Optional[Sequence[str]] = None,
) -> str:
    """构建精炼检索 query，避免冗余的项目摘要。"""
    selected_section = state.get("selected_section") or {}
    intent_result = state.get("intent_result") or {}
    keywords = build_query_keywords(state, domain_terms=domain_terms, action_terms=action_terms)

    parts = [
        state.get("user_message") or "",
        intent_result.get("normalized_instruction") or "",
        f"{selected_section.get('index', '')} {selected_section.get('title', '')}".strip(),
        " ".join(keywords[:8]),
    ]
    return dedupe_join(parts, max_chars=120)


def build_query_keywords(
    state: Dict[str, Any],
    query: Optional[str] = None,
    domain_terms: Optional[Sequence[str]] = None,
    action_terms: Optional[Sequence[str]] = None,
) -> List[str]:
    """从多来源提取检索关键词。"""
    selected_section = state.get("selected_section") or {}
    intent_result = state.get("intent_result") or {}
    history = state.get("conversation_history") or []

    sources = [
        state.get("user_message") or "",
        intent_result.get("normalized_instruction") or "",
        f"{selected_section.get('index', '')} {selected_section.get('title', '')}",
        str(selected_section.get("content") or "")[:500],
        query or "",
    ]
    if history:
        for turn in history[-6:]:
            if not isinstance(turn, dict):
                continue
            role = str(turn.get("role") or turn.get("sender") or "").lower()
            if role in ("assistant", "ai", "bot", "model"):
                continue
            content = str(turn.get("content") or turn.get("message") or "")
            if content:
                sources.append(content)

    keywords: List[str] = []
    seen = set()
    for text in sources:
        for keyword in extract_retrieval_keywords(
            str(text or ""),
            domain_terms=domain_terms,
            action_terms=action_terms,
        ):
            normalized = keyword.strip()
            if not normalized or normalized in seen:
                continue
            seen.add(normalized)
            keywords.append(normalized)
            if len(keywords) >= 20:
                return keywords
    return keywords


def dedupe_join(parts: List[str], max_chars: int) -> str:
    """去重后拼接文本片段，限制总长度。"""
    values = []
    seen = set()
    for part in parts:
        value = re.sub(r"\s+", " ", str(part or "")).strip()
        if not value or value in seen:
            continue
        seen.add(value)
        values.append(value)
    return " ".join(values)[:max_chars]


def extract_retrieval_keywords(
    text: str,
    domain_terms: Optional[Sequence[str]] = None,
    action_terms: Optional[Sequence[str]] = None,
) -> List[str]:
    """从文本中提取检索关键词。"""
    if not text:
        return []

    keywords: List[str] = []
    for match in re.findall(r"[A-Za-z]{1,8}\s*\d{2,8}(?:[-—]\d{2,4})?", text):
        keywords.append(re.sub(r"\s+", "", match).upper())
    for match in re.findall(r"《([^》]{2,40})》", text):
        keywords.append(match.strip())

    domain_terms = tuple(domain_terms or DEFAULT_DOMAIN_TERMS)
    action_terms = tuple(action_terms or DEFAULT_ACTION_TERMS)
    for term in domain_terms:
        if term in text:
            keywords.append(term)

    action_pattern = "|".join(re.escape(term) for term in action_terms if term)
    if action_pattern:
        for match in re.findall(rf"[一-鿿A-Za-z0-9.-]{{0,12}}(?:{action_pattern})", text):
            if 2 <= len(match) <= 20:
                keywords.append(match)

    normalized = re.sub(r"[\s,，。；;:：、/\\|()\[\]{}<>《》\"'""？?]+", " ", text)
    for token in normalized.split():
        token = token.strip()
        if len(token) < 2 or len(token) > 12:
            continue
        if any(term in token for term in domain_terms):
            keywords.append(token)

    seen = set()
    unique = []
    for keyword in keywords:
        keyword = keyword.strip()
        if keyword and keyword not in seen:
            seen.add(keyword)
            unique.append(keyword)
    return unique