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