239 lines
9.6 KiB
Python
239 lines
9.6 KiB
Python
"""
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app/services/knowledge_llm.py
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当 BERT NLU 未命中时，使用 LLM + knowledge_search function call 查询本地知识库。
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流程：
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1. 构建 tools=[knowledge_search] 发给 LLM
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2. 若 LLM 返回 tool_calls → 执行 KnowledgeStore.search() → 拼结果再发一次 LLM
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3. LLM 生成最终回复 reply_text + knowledge_doc_id（可选）
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返回 KnowledgeReplyResult，包含：
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- reply_text: 简短自然语言摘要
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- doc_id / doc_content: 命中的知识文档（供前端渲染 KnowledgeArtifact）
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from typing import Any
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from urllib import error, request
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from app.services.knowledge_store import KnowledgeDoc, KnowledgeStore
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@dataclass
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class KnowledgeReplyResult:
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reply_text: str
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backend: str
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model_name: str
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doc_id: str | None = None
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doc_content: str | None = None # 原始 MD 内容，前端渲染用
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doc_title: str | None = None
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error_message: str | None = None
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metadata: dict[str, Any] = field(default_factory=dict)
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# ── LLM 工具定义（OpenAI function calling 格式，DashScope 兼容）────────────────
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_KNOWLEDGE_SEARCH_TOOL: dict[str, Any] = {
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": (
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"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。"
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"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。"
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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": {
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"type": "string",
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"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",
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}
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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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_SYSTEM_PROMPT = """\
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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。
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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。
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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。
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如果工具返回了相关知识，请基于知识内容回答，不要编造。
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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"。
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"""
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class DashScopeKnowledgeLLM:
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"""使用 DashScope（OpenAI 兼容 API）+ function calling 的知识库问答器。"""
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def __init__(
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self,
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base_url: str,
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api_key: str,
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model_name: str,
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knowledge_store: KnowledgeStore,
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timeout_seconds: float = 12.0,
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max_tool_rounds: int = 2,
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) -> None:
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self._base_url = base_url.rstrip("/")
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self._api_key = api_key
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self._model_name = model_name
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self._store = knowledge_store
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self._timeout = timeout_seconds
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self._max_tool_rounds = max_tool_rounds
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# ── 主入口 ─────────────────────────────────────────────────────────────────
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def reply(self, user_text: str) -> KnowledgeReplyResult:
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"""完整 function-call 对话流（最多 max_tool_rounds 轮工具调用）。"""
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if not self._base_url or not self._api_key or not self._model_name:
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return self._local_fallback(user_text, "LLM not configured")
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": _SYSTEM_PROMPT},
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{"role": "user", "content": user_text},
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]
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found_doc: KnowledgeDoc | None = None
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for _round in range(self._max_tool_rounds):
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raw = self._chat(messages, tools=[_KNOWLEDGE_SEARCH_TOOL])
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if raw is None:
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return self._local_fallback(user_text, "LLM request failed")
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choice = self._first_choice(raw)
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if choice is None:
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return self._local_fallback(user_text, "empty choices")
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finish_reason = choice.get("finish_reason", "")
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message = choice.get("message", {})
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# ── 工具调用分支 ─────────────────────────────────────────────────
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if finish_reason == "tool_calls" or message.get("tool_calls"):
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tool_calls = message.get("tool_calls", [])
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messages.append({"role": "assistant", **message})
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for tc in tool_calls:
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fn_name = tc.get("function", {}).get("name", "")
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fn_args_raw = tc.get("function", {}).get("arguments", "{}")
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tc_id = tc.get("id", "call_0")
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if fn_name == "knowledge_search":
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try:
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fn_args = json.loads(fn_args_raw)
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except json.JSONDecodeError:
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fn_args = {"query": user_text}
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query = fn_args.get("query", user_text)
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tool_result, found_doc = self._run_knowledge_search(query)
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else:
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tool_result = f"Unknown tool: {fn_name}"
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messages.append({
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"role": "tool",
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"tool_call_id": tc_id,
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"content": tool_result,
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})
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# 继续下一轮 LLM 调用
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continue
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# ── 正常文本回复 ─────────────────────────────────────────────────
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content = self._extract_content(message)
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if not content:
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return self._local_fallback(user_text, "empty content")
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return KnowledgeReplyResult(
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reply_text=content,
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backend="dashscope",
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model_name=self._model_name,
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doc_id=found_doc.doc_id if found_doc else None,
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doc_content=found_doc.content if found_doc else None,
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doc_title=found_doc.title if found_doc else None,
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)
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# 超出工具调用轮数，直接本地兜底
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return self._local_fallback(user_text, "max tool rounds exceeded")
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# ── 内部工具执行 ───────────────────────────────────────────────────────────
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def _run_knowledge_search(self, query: str) -> tuple[str, KnowledgeDoc | None]:
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"""执行本地知识库搜索，返回 (tool_result_str, best_doc)。"""
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results = self._store.search(query, top_k=2)
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if not results:
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return "未找到相关知识文档。", None
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best = results[0]
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# 给 LLM 的 tool result：文档标题 + 正文（截断到 800 字节）
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excerpt = best.doc.content[:800]
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tool_text = (
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f"[知识库检索结果]\n"
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f"文档：{best.doc.title}\n"
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f"命中关键词：{', '.join(best.matched_keywords)}\n\n"
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f"{excerpt}"
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)
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return tool_text, best.doc
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# ── HTTP 调用 ──────────────────────────────────────────────────────────────
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def _chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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) -> dict[str, Any] | None:
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payload: dict[str, Any] = {
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"model": self._model_name,
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"temperature": 0.3,
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"enable_thinking": False,
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"max_tokens": 300,
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"messages": messages,
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}
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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req = request.Request(
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self._endpoint(),
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data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self._api_key}",
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},
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method="POST",
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)
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try:
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with request.urlopen(req, timeout=self._timeout) as resp:
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return json.loads(resp.read().decode("utf-8"))
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except (error.URLError, TimeoutError, ValueError, OSError):
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return None
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def _endpoint(self) -> str:
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if self._base_url.endswith("/chat/completions"):
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return self._base_url
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return f"{self._base_url}/chat/completions"
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def _first_choice(self, payload: dict[str, Any]) -> dict[str, Any] | None:
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choices = payload.get("choices")
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if not isinstance(choices, list) or not choices:
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return None
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return choices[0]
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def _extract_content(self, message: dict[str, Any]) -> str:
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content = message.get("content", "")
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if isinstance(content, list):
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return "".join(
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str(item.get("text", "")).strip()
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for item in content
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if isinstance(item, dict) and item.get("type") == "text"
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).strip()
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return str(content).strip()
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def _local_fallback(self, _user_text: str, reason: str) -> KnowledgeReplyResult:
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return KnowledgeReplyResult(
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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。",
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backend="local-fallback",
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model_name="knowledge-fallback",
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error_message=reason,
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)
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