feat(perception): backend - mock event store, perception service, /perception API routes

backend/app/application/perception/__init__.py

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+"""Perception application package."""

backend/app/application/perception/services.py

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+"""Perception application service — event listing and streaming impact analysis."""
+
+from __future__ import annotations
+
+import json
+from typing import Generator
+
+from app.application.knowledge.services import KnowledgeRetrievalService
+from app.infrastructure.perception.mock_event_store import MockEventStore
+from app.services.llm.llm_factory import get_llm_client
+from app.config.settings import settings
+
+
+_ANALYSIS_SYSTEM_PROMPT = (
+    "你是汽车行业法规合规专家，专注于中国国家标准（GB）、国际法规（UN-ECE、ISO）"
+    "及欧盟法规（EUR-Lex）的解读与合规建议。回答需专业、简洁、结构清晰。"
+)
+
+
+class PerceptionService:
+    """Orchestrate regulatory event queries and streaming impact analysis."""
+
+    def __init__(
+        self,
+        event_store: MockEventStore,
+        retrieval_service: KnowledgeRetrievalService,
+    ) -> None:
+        self._store = event_store
+        self._retrieval = retrieval_service
+
+    # ------------------------------------------------------------------
+    # Queries
+    # ------------------------------------------------------------------
+
+    def list_events(
+        self,
+        *,
+        source: str | None = None,
+        impact_level: str | None = None,
+        limit: int = 50,
+    ) -> list[dict]:
+        return self._store.filter(source=source, impact_level=impact_level, limit=limit)
+
+    def get_event(self, event_id: str) -> dict | None:
+        return self._store.get(event_id)
+
+    def get_stats(self) -> dict:
+        return self._store.stats()
+
+    # ------------------------------------------------------------------
+    # Streaming analysis
+    # ------------------------------------------------------------------
+
+    def analyze_event(self, event_id: str) -> Generator[dict, None, None]:
+        """Yield SSE-ready dicts: sources → content chunks → done."""
+        event = self._store.get(event_id)
+        if not event:
+            yield {"event": "error", "data": f"事件 {event_id} 不存在"}
+            return
+
+        # --- 1. RAG retrieval: find related library documents ---
+        query = event["title"] + " " + " ".join(event["tags"])
+        chunks: list = []
+        affected_docs: list[dict] = []
+        try:
+            chunks = self._retrieval.retrieve(query=query, top_k=5)
+            seen: set[str] = set()
+            for chunk in chunks:
+                if chunk.doc_id not in seen:
+                    seen.add(chunk.doc_id)
+                    affected_docs.append(
+                        {
+                            "doc_id": chunk.doc_id,
+                            "doc_name": chunk.doc_name,
+                            "score": round(float(chunk.score), 4),
+                            "snippet": (chunk.content or "")[:180],
+                            "clause": getattr(chunk, "section_title", "") or "",
+                        }
+                    )
+        except Exception:  # noqa: BLE001
+            pass
+
+        yield {"event": "sources", "data": json.dumps(affected_docs, ensure_ascii=False)}
+
+        # --- 2. Build context from retrieved chunks ---
+        context_parts = [
+            f"[文档{i}: {c.doc_name}]\n{(c.content or '')[:400]}"
+            for i, c in enumerate(chunks[:5], 1)
+        ]
+        context = "\n\n".join(context_parts) if context_parts else "（知识库中暂无相关文档）"
+
+        # --- 3. Build prompt ---
+        effective = event.get("effective_at") or "待定"
+        user_content = f"""请对以下法规动态进行专业影响分析。
+
+【法规动态】
+标准编号：{event['standard_code']}
+标题：{event['title']}
+来源：{event['source_label']}
+摘要：{event['summary']}
+生效日期：{effective}
+分类：{event['category']}
+关键词：{', '.join(event['tags'])}
+
+【知识库关联文档】
+{context}
+
+请用 Markdown 格式，从以下四个维度进行分析：
+
+## 核心变化
+列出本次法规更新最关键的 3-5 项变化（用 - 列表）
+
+## 业务影响
+分析对现有产品、认证流程、技术文档的具体影响
+
+## 整改建议
+给出优先级排序的行动清单（标注 🔴高 🟡中 🟢低 优先级）
+
+## 时间节点
+关键合规时间表与里程碑提醒"""
+
+        messages = [
+            {"role": "system", "content": _ANALYSIS_SYSTEM_PROMPT},
+            {"role": "user", "content": user_content},
+        ]
+
+        # --- 4. Stream LLM response ---
+        try:
+            client = get_llm_client(
+                provider=settings.llm_provider,
+                model=settings.llm_model,
+            )
+            if hasattr(client, "stream_chat"):
+                for chunk in client.stream_chat(messages):
+                    yield {"event": "content", "data": chunk}
+            else:
+                response = client.chat(messages)
+                yield {"event": "content", "data": response.content or ""}
+        except Exception as exc:  # noqa: BLE001
+            yield {"event": "error", "data": str(exc)}
+            return
+
+        yield {"event": "done", "data": "{}"}