Macbook
4b86b2a660
- server-dev start/stop/deploy 및 Gitea push 자동 배포 - local-dev 로컬 개발 환경 Co-authored-by: Cursor <cursoragent@cursor.com>
325 lines
12 KiB
Python
325 lines
12 KiB
Python
"""
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Local + remote tool execution for the agent loop.
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"""
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import json
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import re
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from agent.chatbot_tool_client import ChatbotToolClient
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RAG_SEARCH_TOOL = {
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"type": "function",
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"function": {
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"name": "rag_search",
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"description": (
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"한국도로공사 FAQ/상담 지식베이스(Qdrant)에서 질문과 유사한 Q&A를 검색합니다. "
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"통행료 안내, Hi-pass, 환불 절차, 민원, 일반 상담 FAQ 등 정적 지식 질문에 사용하세요."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "검색할 질문 문장"},
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},
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"required": ["query"],
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},
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},
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}
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ASK_USER_TOOL = {
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"type": "function",
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"function": {
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"name": "ask_user",
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"description": (
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"조회에 필요한 정보(차량번호, IC명, 휴게소명 등)가 부족할 때 사용자에게 "
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"추가 질문을 합니다. 최종 답변 대신 clarification이 필요할 때만 사용하세요."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"question": {"type": "string", "description": "사용자에게 되물을 질문"},
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"intentType": {
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"type": "string",
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"description": "되묻는 대상 intent (예: FARE_SEARCH, FARE_UNPAID). 첫 턴 clarify 시 필수.",
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},
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},
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"required": ["question"],
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},
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},
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}
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class ToolExecutor:
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"""Executes rag_search locally and domain tools via chatbotApi."""
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_KEYWORD_RETRY_LIMIT = 3
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_KEYWORD_RETRY_FIELDS = ("q", "a", "question", "answer", "category", "source")
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_KEYWORD_RETRY_MAX_SCAN = 10000
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_GENERIC_KEYWORD_QUERIES = {
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"요금",
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"통행료",
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"할인",
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"감면",
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"환불",
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"신청",
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"방법",
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"절차",
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"문의",
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"안내",
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"고속도로",
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"휴게소",
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"하이패스",
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}
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def __init__(
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self,
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search_handler,
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config,
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chatbot_tool_client: Optional[ChatbotToolClient] = None,
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):
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self.search_handler = search_handler
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self.config = config
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self.chatbot_tool_client = chatbot_tool_client or ChatbotToolClient()
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self.last_domain_result: Optional[Dict[str, Any]] = None
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self.last_rag_result: Optional[Dict[str, Any]] = None
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# 한 턴에 여러 domain tool이 성공한 경우(복합 질의) 모두 누적
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self.domain_results: List[Dict[str, Any]] = []
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self.tool_trace: List[Dict[str, Any]] = []
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def reset_state(self) -> None:
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"""Clear per-turn domain result and tool trace."""
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self.last_domain_result = None
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self.last_rag_result = None
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self.domain_results = []
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self.tool_trace = []
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def load_remote_tools(self) -> List[Dict[str, Any]]:
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try:
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return self.chatbot_tool_client.list_tool_definitions()
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except Exception as exc:
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print(f"[ToolExecutor] remote tool definitions unavailable: {exc}")
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return []
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def all_tools(self) -> List[Dict[str, Any]]:
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return [RAG_SEARCH_TOOL, ASK_USER_TOOL] + self.load_remote_tools()
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def execute(
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self,
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tool_name: str,
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arguments: Dict[str, Any],
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*,
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bot_id: Optional[str] = None,
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user_input: Optional[str] = None,
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) -> str:
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started = datetime.now(timezone.utc).isoformat()
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try:
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if tool_name == "rag_search":
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result = self._execute_rag_search(arguments)
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elif tool_name == "ask_user":
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result = {"status": True, "clarificationQuestion": arguments.get("question")}
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else:
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payload = self.chatbot_tool_client.execute_tool(
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tool_name,
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arguments,
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bot_id=bot_id,
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user_input=user_input,
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)
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if isinstance(payload, dict):
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if payload.get("needsClarification") or payload.get("status") is True:
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self.last_domain_result = payload
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if payload.get("status") is True and payload.get("intentType"):
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self.domain_results.append(payload)
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result = payload
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success = not (
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isinstance(result, dict)
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and result.get("status") is False
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and not result.get("needsClarification")
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)
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self.tool_trace.append(
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{
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"tool": tool_name,
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"arguments": arguments,
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"startedAt": started,
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"status": success,
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}
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)
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return json.dumps(result, ensure_ascii=False)
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except Exception as exc:
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self.tool_trace.append(
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{
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"tool": tool_name,
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"arguments": arguments,
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"startedAt": started,
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"status": False,
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"error": str(exc),
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}
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)
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return json.dumps({"status": False, "error": str(exc)}, ensure_ascii=False)
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def _execute_rag_search(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
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query = (arguments or {}).get("query") or ""
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ts = datetime.now().strftime("%H:%M:%S")
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query_vec = self.search_handler.embed_query(query, ts)
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if query_vec is None:
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return {"status": False, "error": "embedding_failed", "references": []}
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search_results = self.search_handler.search(query_vec, self.config.threshold, ts)
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# no-match 시 완화된 threshold로 재검색 (Legacy /ask recall parity)
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if not search_results:
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retry_threshold = getattr(self.config, "threshold_rewrite", None)
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if retry_threshold is not None and retry_threshold < self.config.threshold:
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print(f"[ToolExecutor] rag_search no-match → threshold {retry_threshold} 재검색")
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search_results = self.search_handler.search(query_vec, retry_threshold, ts)
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# search 결과는 {"meta": {...}, "score": ...} 형태 → legacy /ask와 동일하게 meta 언랩
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candidates = [r["meta"] for r in search_results if isinstance(r, dict) and r.get("meta")]
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top_results, scores, _, _ = self.search_handler.rerank(query, candidates, ts)
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references = self._build_references(top_results, scores)
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search_info = getattr(self.search_handler, "last_search_info", {}) or {}
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search_mode = search_info.get("searchMode") or "vector"
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keyword_retry_used = False
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keyword_retry_accepted = False
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# 최종 guidance 직전 보조 검색: 벡터/완화 재검색이 모두 실패한 경우에만,
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# 문자열 포함 결과 최대 3건을 reranker로 검증해 충분히 맞을 때만 근거로 채택한다.
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if not references:
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keyword_retry_used = self._keyword_retry_allowed(query)
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if keyword_retry_used:
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keyword_references = self._keyword_retry_with_rerank(query, ts)
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if keyword_references:
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references = keyword_references
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search_mode = "keyword_retry"
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keyword_retry_accepted = True
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result = {
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"status": True,
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"query": query,
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"references": references,
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"referenceCount": len(references),
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"searchMode": search_mode,
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"candidateCount": search_info.get("candidateCount"),
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"keywordRetryUsed": keyword_retry_used,
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"keywordRetryAccepted": keyword_retry_accepted,
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}
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self.last_rag_result = result
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return result
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def _build_references(
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self, top_results: List[Dict[str, Any]], scores: List[Any], limit: int = 5
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) -> List[Dict[str, Any]]:
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references = []
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for item, score in zip((top_results or [])[:limit], (scores or [])[:limit]):
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references.append(
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{
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"question": item.get("q") or item.get("question"),
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"answer": item.get("a") or item.get("answer"),
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"score": score,
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"category": item.get("category"),
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"url": item.get("url"),
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"searchSource": item.get("_search_source"),
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"denseScore": item.get("_dense_score"),
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"sparseScore": item.get("_sparse_score"),
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}
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)
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return references
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def _keyword_retry_allowed(self, query: str) -> bool:
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text = (query or "").strip()
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if not text:
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return False
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normalized = re.sub(r"\s+", "", text).lower()
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if len(normalized) < 3:
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return False
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if not re.search(r"[0-9a-zA-Z가-힣]", normalized):
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return False
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tokens = re.findall(r"[0-9a-zA-Z가-힣]+", text.lower())
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if not tokens:
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return False
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compact_tokens = [re.sub(r"\s+", "", token) for token in tokens if token.strip()]
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if len(compact_tokens) == 1 and compact_tokens[0] in self._GENERIC_KEYWORD_QUERIES:
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return False
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compact_query = "".join(compact_tokens)
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if compact_query in self._GENERIC_KEYWORD_QUERIES:
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return False
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return True
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def _keyword_retry_with_rerank(self, query: str, ts: str) -> List[Dict[str, Any]]:
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vector_store = getattr(self.search_handler, "vector_store", None)
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keyword_search = getattr(vector_store, "keyword_search", None)
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if not callable(keyword_search):
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return []
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try:
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keyword_items, total, exhausted = keyword_search(
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query,
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fields=self._KEYWORD_RETRY_FIELDS,
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limit=self._KEYWORD_RETRY_LIMIT,
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max_scan=self._KEYWORD_RETRY_MAX_SCAN,
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count_total=False,
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)
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except TypeError:
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# 이전 시그니처/테스트 더블 호환: count_total을 받지 못하면 기본 호출로 재시도
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keyword_items, total, exhausted = keyword_search(
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query,
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fields=self._KEYWORD_RETRY_FIELDS,
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limit=self._KEYWORD_RETRY_LIMIT,
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)
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except Exception as exc:
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print(f"[ToolExecutor] keyword retry 실패: {exc}")
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return []
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if not isinstance(keyword_items, list) or not keyword_items:
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print(f"[ToolExecutor] keyword retry no-match: query={query}")
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return []
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candidates = [
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item.get("meta")
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for item in keyword_items[: self._KEYWORD_RETRY_LIMIT]
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if isinstance(item, dict) and isinstance(item.get("meta"), dict)
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]
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if not candidates:
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return []
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print(
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f"[ToolExecutor] keyword retry → rerank 검증: "
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f"query={query}, candidates={len(candidates)}, total_seen={total}, exhausted={exhausted}"
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)
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top_results, scores, rerank_used, _ = self.search_handler.rerank(query, candidates, ts)
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if not top_results or not scores or scores[0] is None:
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print("[ToolExecutor] keyword retry 거부: rerank 점수 없음")
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return []
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threshold = self._low_confidence_threshold()
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try:
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top_score = float(scores[0])
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except (TypeError, ValueError):
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print(f"[ToolExecutor] keyword retry 거부: 잘못된 rerank 점수={scores[0]}")
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return []
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if not rerank_used or top_score < threshold:
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print(
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f"[ToolExecutor] keyword retry 거부: score={top_score:.4f}, "
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f"threshold={threshold:.4f}, rerank_used={rerank_used}"
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)
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return []
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print(f"[ToolExecutor] keyword retry 채택: score={top_score:.4f}")
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return self._build_references(
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top_results[: self._KEYWORD_RETRY_LIMIT],
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scores[: self._KEYWORD_RETRY_LIMIT],
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limit=self._KEYWORD_RETRY_LIMIT,
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)
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def _low_confidence_threshold(self) -> float:
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value = getattr(self.config, "low_confidence_threshold", 0.65)
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try:
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return float(value)
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except (TypeError, ValueError):
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return 0.65
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