"""
Local + remote tool execution for the agent loop.
"""

import json
import re
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional

from agent.chatbot_tool_client import ChatbotToolClient

RAG_SEARCH_TOOL = {
    "type": "function",
    "function": {
        "name": "rag_search",
        "description": (
            "한국도로공사 FAQ/상담 지식베이스(Qdrant)에서 질문과 유사한 Q&A를 검색합니다. "
            "통행료 안내, Hi-pass, 환불 절차, 민원, 일반 상담 FAQ 등 정적 지식 질문에 사용하세요."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "검색할 질문 문장"},
            },
            "required": ["query"],
        },
    },
}

ASK_USER_TOOL = {
    "type": "function",
    "function": {
        "name": "ask_user",
        "description": (
            "조회에 필요한 정보(차량번호, IC명, 휴게소명 등)가 부족할 때 사용자에게 "
            "추가 질문을 합니다. 최종 답변 대신 clarification이 필요할 때만 사용하세요."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "question": {"type": "string", "description": "사용자에게 되물을 질문"},
                "intentType": {
                    "type": "string",
                    "description": "되묻는 대상 intent (예: FARE_SEARCH, FARE_UNPAID). 첫 턴 clarify 시 필수.",
                },
            },
            "required": ["question"],
        },
    },
}


class ToolExecutor:
    """Executes rag_search locally and domain tools via chatbotApi."""

    _KEYWORD_RETRY_LIMIT = 3
    _KEYWORD_RETRY_FIELDS = ("q", "a", "question", "answer", "category", "source")
    _KEYWORD_RETRY_MAX_SCAN = 10000
    _GENERIC_KEYWORD_QUERIES = {
        "요금",
        "통행료",
        "할인",
        "감면",
        "환불",
        "신청",
        "방법",
        "절차",
        "문의",
        "안내",
        "고속도로",
        "휴게소",
        "하이패스",
    }

    def __init__(
        self,
        search_handler,
        config,
        chatbot_tool_client: Optional[ChatbotToolClient] = None,
    ):
        self.search_handler = search_handler
        self.config = config
        self.chatbot_tool_client = chatbot_tool_client or ChatbotToolClient()
        self.last_domain_result: Optional[Dict[str, Any]] = None
        self.last_rag_result: Optional[Dict[str, Any]] = None
        # 한 턴에 여러 domain tool이 성공한 경우(복합 질의) 모두 누적
        self.domain_results: List[Dict[str, Any]] = []
        self.tool_trace: List[Dict[str, Any]] = []

    def reset_state(self) -> None:
        """Clear per-turn domain result and tool trace."""
        self.last_domain_result = None
        self.last_rag_result = None
        self.domain_results = []
        self.tool_trace = []

    def load_remote_tools(self) -> List[Dict[str, Any]]:
        try:
            return self.chatbot_tool_client.list_tool_definitions()
        except Exception as exc:
            print(f"[ToolExecutor] remote tool definitions unavailable: {exc}")
            return []

    def all_tools(self) -> List[Dict[str, Any]]:
        return [RAG_SEARCH_TOOL, ASK_USER_TOOL] + self.load_remote_tools()

    def execute(
        self,
        tool_name: str,
        arguments: Dict[str, Any],
        *,
        bot_id: Optional[str] = None,
        user_input: Optional[str] = None,
    ) -> str:
        started = datetime.now(timezone.utc).isoformat()
        try:
            if tool_name == "rag_search":
                result = self._execute_rag_search(arguments)
            elif tool_name == "ask_user":
                result = {"status": True, "clarificationQuestion": arguments.get("question")}
            else:
                payload = self.chatbot_tool_client.execute_tool(
                    tool_name,
                    arguments,
                    bot_id=bot_id,
                    user_input=user_input,
                )
                if isinstance(payload, dict):
                    if payload.get("needsClarification") or payload.get("status") is True:
                        self.last_domain_result = payload
                    if payload.get("status") is True and payload.get("intentType"):
                        self.domain_results.append(payload)
                result = payload
            success = not (
                isinstance(result, dict)
                and result.get("status") is False
                and not result.get("needsClarification")
            )
            self.tool_trace.append(
                {
                    "tool": tool_name,
                    "arguments": arguments,
                    "startedAt": started,
                    "status": success,
                }
            )
            return json.dumps(result, ensure_ascii=False)
        except Exception as exc:
            self.tool_trace.append(
                {
                    "tool": tool_name,
                    "arguments": arguments,
                    "startedAt": started,
                    "status": False,
                    "error": str(exc),
                }
            )
            return json.dumps({"status": False, "error": str(exc)}, ensure_ascii=False)

    def _execute_rag_search(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
        query = (arguments or {}).get("query") or ""
        ts = datetime.now().strftime("%H:%M:%S")
        query_vec = self.search_handler.embed_query(query, ts)
        if query_vec is None:
            return {"status": False, "error": "embedding_failed", "references": []}

        search_results = self.search_handler.search(query_vec, self.config.threshold, ts)
        # no-match 시 완화된 threshold로 재검색 (Legacy /ask recall parity)
        if not search_results:
            retry_threshold = getattr(self.config, "threshold_rewrite", None)
            if retry_threshold is not None and retry_threshold < self.config.threshold:
                print(f"[ToolExecutor] rag_search no-match → threshold {retry_threshold} 재검색")
                search_results = self.search_handler.search(query_vec, retry_threshold, ts)
        # search 결과는 {"meta": {...}, "score": ...} 형태 → legacy /ask와 동일하게 meta 언랩
        candidates = [r["meta"] for r in search_results if isinstance(r, dict) and r.get("meta")]
        top_results, scores, _, _ = self.search_handler.rerank(query, candidates, ts)
        references = self._build_references(top_results, scores)
        search_info = getattr(self.search_handler, "last_search_info", {}) or {}
        search_mode = search_info.get("searchMode") or "vector"
        keyword_retry_used = False
        keyword_retry_accepted = False

        # 최종 guidance 직전 보조 검색: 벡터/완화 재검색이 모두 실패한 경우에만,
        # 문자열 포함 결과 최대 3건을 reranker로 검증해 충분히 맞을 때만 근거로 채택한다.
        if not references:
            keyword_retry_used = self._keyword_retry_allowed(query)
            if keyword_retry_used:
                keyword_references = self._keyword_retry_with_rerank(query, ts)
                if keyword_references:
                    references = keyword_references
                    search_mode = "keyword_retry"
                    keyword_retry_accepted = True

        result = {
            "status": True,
            "query": query,
            "references": references,
            "referenceCount": len(references),
            "searchMode": search_mode,
            "candidateCount": search_info.get("candidateCount"),
            "keywordRetryUsed": keyword_retry_used,
            "keywordRetryAccepted": keyword_retry_accepted,
        }
        self.last_rag_result = result
        return result

    def _build_references(
        self, top_results: List[Dict[str, Any]], scores: List[Any], limit: int = 5
    ) -> List[Dict[str, Any]]:
        references = []
        for item, score in zip((top_results or [])[:limit], (scores or [])[:limit]):
            references.append(
                {
                    "question": item.get("q") or item.get("question"),
                    "answer": item.get("a") or item.get("answer"),
                    "score": score,
                    "category": item.get("category"),
                    "url": item.get("url"),
                    "searchSource": item.get("_search_source"),
                    "denseScore": item.get("_dense_score"),
                    "sparseScore": item.get("_sparse_score"),
                }
            )
        return references

    def _keyword_retry_allowed(self, query: str) -> bool:
        text = (query or "").strip()
        if not text:
            return False

        normalized = re.sub(r"\s+", "", text).lower()
        if len(normalized) < 3:
            return False

        if not re.search(r"[0-9a-zA-Z가-힣]", normalized):
            return False

        tokens = re.findall(r"[0-9a-zA-Z가-힣]+", text.lower())
        if not tokens:
            return False

        compact_tokens = [re.sub(r"\s+", "", token) for token in tokens if token.strip()]
        if len(compact_tokens) == 1 and compact_tokens[0] in self._GENERIC_KEYWORD_QUERIES:
            return False

        compact_query = "".join(compact_tokens)
        if compact_query in self._GENERIC_KEYWORD_QUERIES:
            return False

        return True

    def _keyword_retry_with_rerank(self, query: str, ts: str) -> List[Dict[str, Any]]:
        vector_store = getattr(self.search_handler, "vector_store", None)
        keyword_search = getattr(vector_store, "keyword_search", None)
        if not callable(keyword_search):
            return []

        try:
            keyword_items, total, exhausted = keyword_search(
                query,
                fields=self._KEYWORD_RETRY_FIELDS,
                limit=self._KEYWORD_RETRY_LIMIT,
                max_scan=self._KEYWORD_RETRY_MAX_SCAN,
                count_total=False,
            )
        except TypeError:
            # 이전 시그니처/테스트 더블 호환: count_total을 받지 못하면 기본 호출로 재시도
            keyword_items, total, exhausted = keyword_search(
                query,
                fields=self._KEYWORD_RETRY_FIELDS,
                limit=self._KEYWORD_RETRY_LIMIT,
            )
        except Exception as exc:
            print(f"[ToolExecutor] keyword retry 실패: {exc}")
            return []

        if not isinstance(keyword_items, list) or not keyword_items:
            print(f"[ToolExecutor] keyword retry no-match: query={query}")
            return []

        candidates = [
            item.get("meta")
            for item in keyword_items[: self._KEYWORD_RETRY_LIMIT]
            if isinstance(item, dict) and isinstance(item.get("meta"), dict)
        ]
        if not candidates:
            return []

        print(
            f"[ToolExecutor] keyword retry → rerank 검증: "
            f"query={query}, candidates={len(candidates)}, total_seen={total}, exhausted={exhausted}"
        )
        top_results, scores, rerank_used, _ = self.search_handler.rerank(query, candidates, ts)
        if not top_results or not scores or scores[0] is None:
            print("[ToolExecutor] keyword retry 거부: rerank 점수 없음")
            return []

        threshold = self._low_confidence_threshold()
        try:
            top_score = float(scores[0])
        except (TypeError, ValueError):
            print(f"[ToolExecutor] keyword retry 거부: 잘못된 rerank 점수={scores[0]}")
            return []

        if not rerank_used or top_score < threshold:
            print(
                f"[ToolExecutor] keyword retry 거부: score={top_score:.4f}, "
                f"threshold={threshold:.4f}, rerank_used={rerank_used}"
            )
            return []

        print(f"[ToolExecutor] keyword retry 채택: score={top_score:.4f}")
        return self._build_references(
            top_results[: self._KEYWORD_RETRY_LIMIT],
            scores[: self._KEYWORD_RETRY_LIMIT],
            limit=self._KEYWORD_RETRY_LIMIT,
        )

    def _low_confidence_threshold(self) -> float:
        value = getattr(self.config, "low_confidence_threshold", 0.65)
        try:
            return float(value)
        except (TypeError, ValueError):
            return 0.65