Update project and configurations

2026-06-11 16:28:00 +08:00
parent 12d3922091
commit a29a91867d
237 changed files with 164880 additions and 90 deletions
--- a/intelligent_cabin/app/services/knowledge_llm.py
+++ b/intelligent_cabin/app/services/knowledge_llm.py
@@ -0,0 +1,238 @@
+"""
+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,
+        )