""" app/services/knowledge_llm.py 当 BERT NLU 未命中时，使用 LLM + knowledge_search function call 查询本地知识库。 流程： 1. 构建 tools=[knowledge_search] 发给 LLM 2. 若 LLM 返回 tool_calls → 执行 KnowledgeStore.search() → 拼结果再发一次 LLM 3. LLM 生成最终回复 reply_text + knowledge_doc_id（可选） 返回 KnowledgeReplyResult，包含： - reply_text: 简短自然语言摘要 - doc_id / doc_content: 命中的知识文档（供前端渲染 KnowledgeArtifact） """ from __future__ import annotations import json from dataclasses import dataclass, field from typing import Any from urllib import error, request from app.services.knowledge_store import KnowledgeDoc, KnowledgeStore @dataclass class KnowledgeReplyResult: reply_text: str backend: str model_name: str doc_id: str | None = None doc_content: str | None = None # 原始 MD 内容，前端渲染用 doc_title: str | None = None error_message: str | None = None metadata: dict[str, Any] = field(default_factory=dict) # ── LLM 工具定义（OpenAI function calling 格式，DashScope 兼容）──────────────── _KNOWLEDGE_SEARCH_TOOL: dict[str, Any] = { "type": "function", "function": { "name": "knowledge_search", "description": ( "搜索本地设备知识库，获取焊管机/弯管机产线相关的故障排查、操作规程等知识。" "当用户问到设备故障、报警处理、操作方法、工艺参数时请调用此工具。" ), "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "搜索关键词，如'虚焊报警'、'激光扫描仪操作'、'弯管模具调节'等", } }, "required": ["query"], }, }, } _SYSTEM_PROMPT = """\ 你是焊管机产线智能助手，负责回答操作工人关于设备故障、工艺调节、仪器使用的问题。 你有一个工具 knowledge_search 可以查询本地设备知识库，遇到设备类问题时请先调用它。 回答时语言简洁、口语化，先给出结论，再说步骤，总长度不超过 100 字。 如果工具返回了相关知识，请基于知识内容回答，不要编造。 如果没有找到相关知识，诚实告知"暂未找到相关资料，建议联系技术支持"。 """ class DashScopeKnowledgeLLM: """使用 DashScope（OpenAI 兼容 API）+ function calling 的知识库问答器。""" def __init__( self, base_url: str, api_key: str, model_name: str, knowledge_store: KnowledgeStore, timeout_seconds: float = 12.0, max_tool_rounds: int = 2, ) -> None: self._base_url = base_url.rstrip("/") self._api_key = api_key self._model_name = model_name self._store = knowledge_store self._timeout = timeout_seconds self._max_tool_rounds = max_tool_rounds # ── 主入口 ───────────────────────────────────────────────────────────────── def reply(self, user_text: str) -> KnowledgeReplyResult: """完整 function-call 对话流（最多 max_tool_rounds 轮工具调用）。""" if not self._base_url or not self._api_key or not self._model_name: return self._local_fallback(user_text, "LLM not configured") messages: list[dict[str, Any]] = [ {"role": "system", "content": _SYSTEM_PROMPT}, {"role": "user", "content": user_text}, ] found_doc: KnowledgeDoc | None = None for _round in range(self._max_tool_rounds): raw = self._chat(messages, tools=[_KNOWLEDGE_SEARCH_TOOL]) if raw is None: return self._local_fallback(user_text, "LLM request failed") choice = self._first_choice(raw) if choice is None: return self._local_fallback(user_text, "empty choices") finish_reason = choice.get("finish_reason", "") message = choice.get("message", {}) # ── 工具调用分支 ───────────────────────────────────────────────── if finish_reason == "tool_calls" or message.get("tool_calls"): tool_calls = message.get("tool_calls", []) messages.append({"role": "assistant", **message}) for tc in tool_calls: fn_name = tc.get("function", {}).get("name", "") fn_args_raw = tc.get("function", {}).get("arguments", "{}") tc_id = tc.get("id", "call_0") if fn_name == "knowledge_search": try: fn_args = json.loads(fn_args_raw) except json.JSONDecodeError: fn_args = {"query": user_text} query = fn_args.get("query", user_text) tool_result, found_doc = self._run_knowledge_search(query) else: tool_result = f"Unknown tool: {fn_name}" messages.append({ "role": "tool", "tool_call_id": tc_id, "content": tool_result, }) # 继续下一轮 LLM 调用 continue # ── 正常文本回复 ───────────────────────────────────────────────── content = self._extract_content(message) if not content: return self._local_fallback(user_text, "empty content") return KnowledgeReplyResult( reply_text=content, backend="dashscope", model_name=self._model_name, doc_id=found_doc.doc_id if found_doc else None, doc_content=found_doc.content if found_doc else None, doc_title=found_doc.title if found_doc else None, ) # 超出工具调用轮数，直接本地兜底 return self._local_fallback(user_text, "max tool rounds exceeded") # ── 内部工具执行 ─────────────────────────────────────────────────────────── def _run_knowledge_search(self, query: str) -> tuple[str, KnowledgeDoc | None]: """执行本地知识库搜索，返回 (tool_result_str, best_doc)。""" results = self._store.search(query, top_k=2) if not results: return "未找到相关知识文档。", None best = results[0] # 给 LLM 的 tool result：文档标题 + 正文（截断到 800 字节） excerpt = best.doc.content[:800] tool_text = ( f"[知识库检索结果]\n" f"文档：{best.doc.title}\n" f"命中关键词：{', '.join(best.matched_keywords)}\n\n" f"{excerpt}" ) return tool_text, best.doc # ── HTTP 调用 ────────────────────────────────────────────────────────────── def _chat( self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None, ) -> dict[str, Any] | None: payload: dict[str, Any] = { "model": self._model_name, "temperature": 0.3, "enable_thinking": False, "max_tokens": 300, "messages": messages, } if tools: payload["tools"] = tools payload["tool_choice"] = "auto" req = request.Request( self._endpoint(), data=json.dumps(payload, ensure_ascii=False).encode("utf-8"), headers={ "Content-Type": "application/json", "Authorization": f"Bearer {self._api_key}", }, method="POST", ) try: with request.urlopen(req, timeout=self._timeout) as resp: return json.loads(resp.read().decode("utf-8")) except (error.URLError, TimeoutError, ValueError, OSError): return None def _endpoint(self) -> str: if self._base_url.endswith("/chat/completions"): return self._base_url return f"{self._base_url}/chat/completions" def _first_choice(self, payload: dict[str, Any]) -> dict[str, Any] | None: choices = payload.get("choices") if not isinstance(choices, list) or not choices: return None return choices[0] def _extract_content(self, message: dict[str, Any]) -> str: content = message.get("content", "") if isinstance(content, list): return "".join( str(item.get("text", "")).strip() for item in content if isinstance(item, dict) and item.get("type") == "text" ).strip() return str(content).strip() def _local_fallback(self, _user_text: str, reason: str) -> KnowledgeReplyResult: return KnowledgeReplyResult( reply_text="暂未找到相关资料，建议联系技术支持或查阅设备手册。", backend="local-fallback", model_name="knowledge-fallback", error_message=reason, )