from __future__ import annotations

import re
from typing import Any

from app.services.user_agent_knowledge_constants import (
    KNOWLEDGE_ARTICLE_PATTERN,
    KNOWLEDGE_LIST_ITEM_PATTERN,
    KNOWLEDGE_NUMBERED_ITEM_PATTERN,
    KNOWLEDGE_QUERY_STOPWORDS,
    KNOWLEDGE_SECTION_HEADING_PATTERN,
    MAX_KNOWLEDGE_MODEL_HITS,
    MAX_KNOWLEDGE_QUERY_TERMS,
)


class UserAgentKnowledgeHelpersMixin:

    @staticmethod
    def _select_knowledge_model_hits(
        tool_payload: dict[str, Any],
        *,
        question: str | None = None,
    ) -> list[dict[str, Any]]:
        raw_hits = [
            item
            for item in list(tool_payload.get("hits") or [])
            if isinstance(item, dict)
        ][: max(MAX_KNOWLEDGE_MODEL_HITS + 1, 6)]
        if not raw_hits:
            return []

        query_terms = UserAgentKnowledgeHelpersMixin._extract_knowledge_query_terms(question or "")
        if not query_terms:
            return raw_hits[:MAX_KNOWLEDGE_MODEL_HITS]

        ranked_hits = sorted(
            enumerate(raw_hits),
            key=lambda value: (
                UserAgentKnowledgeHelpersMixin._score_knowledge_model_hit(
                    value[1],
                    query_terms=query_terms,
                    rank_index=value[0],
                ),
                -value[0],
            ),
            reverse=True,
        )
        return [item for _, item in ranked_hits[:MAX_KNOWLEDGE_MODEL_HITS]]


    @staticmethod
    def _score_knowledge_model_hit(
        item: dict[str, Any],
        *,
        query_terms: list[str],
        rank_index: int,
    ) -> int:
        title = str(item.get("title") or item.get("document_name") or "").lower()
        excerpt = str(item.get("excerpt") or "").lower()
        content = str(item.get("content") or "").lower()
        haystack = "\n".join([title, excerpt, content[:1400]])

        matched_terms = [term for term in query_terms if term in haystack]
        score = max(1, 48 - rank_index * 4)
        score += len(matched_terms) * 10
        score += sum(1 for term in matched_terms if term in title) * 8

        leading_marker = UserAgentKnowledgeHelpersMixin._leading_knowledge_appendix_marker(content)
        if leading_marker == "# 章节导航":
            score -= 22
        elif leading_marker == "# 问答线索补充":
            score += 6 if matched_terms else -8
        elif leading_marker == "# 重点章节摘录":
            score += 4 if matched_terms else -4
        elif leading_marker == "# 结构化表格补充":
            score += 8 if matched_terms else -3

        if matched_terms and "|" in content:
            score += 8
        if matched_terms and any(marker in content for marker in ("：", ":")):
            score += 10
        if matched_terms and "\n" in content:
            score += 4
        if matched_terms and any(marker in content for marker in ("附表", "第", "条")):
            score += 4
        if matched_terms and any(marker in content for marker in ("第", "条", "：", "-", "•")):
            score += 4
        if re.search(r"没有.{0,8}(信息|规定|说明|依据)", content):
            score -= 12
        return score


    @staticmethod
    def _leading_knowledge_appendix_marker(content: str) -> str:
        normalized = str(content or "").lstrip()
        for marker in ("# 章节导航", "# 重点章节摘录", "# 问答线索补充", "# 结构化表格补充"):
            index = normalized.find(marker)
            if 0 <= index <= 220:
                return marker
        return ""


    def _prioritize_knowledge_evidence_items(
        self,
        question: str,
        evidence_items: list[dict[str, Any]],
    ) -> list[dict[str, Any]]:
        if not evidence_items or not self._question_requires_explicit_condition(question):
            return evidence_items

        for preferred_kind in ("table", "kv", "clause", "list"):
            for index, item in enumerate(evidence_items):
                if str(item.get("kind") or "") != preferred_kind:
                    continue
                return [item, *evidence_items[:index], *evidence_items[index + 1 :]]

        for index, item in enumerate(evidence_items):
            if re.search(r"\d", str(item.get("content") or "")):
                return [item, *evidence_items[:index], *evidence_items[index + 1 :]]

        return evidence_items



    @staticmethod
    def _is_knowledge_lead_in_segment(item: dict[str, str]) -> bool:
        kind = str(item.get("kind") or "").strip()
        content = str(item.get("content") or "").strip()
        return kind in {"kv", "list", "clause"} and content.endswith(("：", ":"))



    @staticmethod
    def _extract_knowledge_marker_family(content: str) -> str:
        normalized = str(content or "").strip()
        if not normalized:
            return ""
        if KNOWLEDGE_ARTICLE_PATTERN.match(normalized):
            return "article"
        if re.match(r"^\d+[.)、]\s*", normalized):
            return "arabic"
        if re.match(r"^[（(][一二三四五六七八九十百零0-9]+[)）]\s*", normalized):
            return "paren"
        if re.match(r"^[①②③④⑤⑥⑦⑧⑨⑩]\s*", normalized):
            return "circled"
        if KNOWLEDGE_LIST_ITEM_PATTERN.match(normalized):
            return "bullet"
        return ""



    @staticmethod
    def _format_knowledge_heading_label(heading: str) -> str:
        parts = [item.strip() for item in str(heading or "").split(">") if item.strip()]
        return " / ".join(parts)



    def _score_knowledge_evidence_candidate(
        self,
        item: dict[str, str],
        query_terms: list[str],
    ) -> int:
        heading = str(item.get("heading") or "").lower()
        content = str(item.get("content") or "").lower()
        kind = str(item.get("kind") or "").strip()
        haystack = "\n".join([heading, content])

        matched_terms = [term for term in query_terms if term in haystack]
        score = len(matched_terms) * 10
        score += sum(1 for term in matched_terms if term in heading) * 6

        if kind == "table":
            score += 10
        elif kind in {"kv", "clause", "list"}:
            score += 8
        elif kind == "paragraph":
            score += 4

        if "问答线索补充" in heading or "重点章节摘录" in heading:
            score += 8
        if "结构化表格补充" in heading:
            score += 10
        if "章节导航" in heading or "目录" in heading:
            score -= 16
        if re.search(r"[.。…]{6,}", content):
            score -= 12
        if any(hint in content for hint in ("应", "需", "不得", "可以", "标准", "条件", "材料", "审批", "流程", "包括")):
            score += 3

        content_length = len(content)
        if content_length > 220:
            score -= min(8, (content_length - 220) // 40)
        return score



    @staticmethod
    def _extract_knowledge_query_terms(question: str) -> list[str]:
        normalized_question = str(question or "").strip().lower()
        if not normalized_question:
            return []

        terms: list[str] = []
        seen: set[str] = set()

        def remember(term: str) -> None:
            normalized = str(term or "").strip().lower()
            if (
                not normalized
                or normalized in seen
                or normalized in KNOWLEDGE_QUERY_STOPWORDS
            ):
                return
            seen.add(normalized)
            terms.append(normalized)

        for item in re.findall(r"[a-z0-9][a-z0-9_\-]{1,}", normalized_question):
            remember(item)

        for block in re.findall(r"[\u4e00-\u9fff]{2,20}", normalized_question):
            if len(block) <= 4:
                remember(block)
                continue
            for size in (4, 3, 2):
                for start in range(0, len(block) - size + 1):
                    remember(block[start : start + size])
                    if len(terms) >= MAX_KNOWLEDGE_QUERY_TERMS:
                        return terms

        return terms[:MAX_KNOWLEDGE_QUERY_TERMS]



    @staticmethod
    def _clean_knowledge_segment_text(content: str) -> str:
        normalized = str(content or "").strip()
        normalized = re.sub(r"^[-*•]\s*", "", normalized)
        normalized = re.sub(r"^(?:\d+[.)、]|[①②③④⑤⑥⑦⑧⑨⑩])\s*", "", normalized)
        normalized = re.sub(r"^[（(][一二三四五六七八九十百零0-9]+[)）]\s*", "", normalized)
        normalized = re.sub(r"\s+", " ", normalized)
        if len(normalized) <= 180:
            return normalized
        return f"{normalized[:177].rstrip()}..."



    @staticmethod
    def _normalize_knowledge_line(content: str, *, preserve_marker: bool) -> str:
        normalized = str(content or "").strip()
        normalized = re.sub(r"^[-*•]\s*", "", normalized)
        if not preserve_marker:
            normalized = re.sub(r"^(?:\d+[.)、]|[①②③④⑤⑥⑦⑧⑨⑩])\s*", "", normalized)
            normalized = re.sub(r"^[（(][一二三四五六七八九十百零0-9]+[)）]\s*", "", normalized)
        normalized = re.sub(r"\s+", " ", normalized)
        return normalized



    def _split_clean_knowledge_lines(
        self,
        content: str,
        *,
        preserve_marker: bool,
    ) -> list[str]:
        return [
            line
            for line in (
                self._normalize_knowledge_line(item, preserve_marker=preserve_marker)
                for item in str(content or "").splitlines()
            )
            if line
        ]



    @staticmethod
    def _extract_relevant_table_preview(content: str, query_terms: list[str]) -> str:
        lines = [line.strip() for line in str(content or "").splitlines() if line.strip()]
        if len(lines) <= 3:
            return "\n".join(lines)

        header = lines[0]
        divider = lines[1] if len(lines) > 1 else ""
        body = lines[2:] if divider.count("|") >= 2 else lines[1:]

        matched_rows = [
            row
            for row in body
            if any(term in row.lower() for term in query_terms)
        ]
        selected_rows = matched_rows[:3] or body[:2]
        preview_lines = [header]
        if divider:
            preview_lines.append(divider)
        preview_lines.extend(selected_rows)
        return "\n".join(preview_lines).strip()



    @staticmethod
    def _question_requires_explicit_condition(question: str) -> bool:
        normalized = str(question or "").strip()
        return any(keyword in normalized for keyword in ("多少", "金额", "上限", "限额", "标准", "条件", "需要"))



    @staticmethod
    def _answer_evidence_has_numeric_or_condition(evidence_items: list[dict[str, Any]]) -> bool:
        for item in evidence_items:
            content = str(item.get("content") or "")
            if re.search(r"\d", content):
                return True
            if any(
                keyword in content
                for keyword in ("应", "需", "不得", "可以", "条件", "材料", "审批", "流程", "标准", "适用")
            ):
                return True
        return False