AIRegulation-DocAnalysis/backend/app/application/perception/services.py

"""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.base_event_store import BaseEventStore
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: BaseEventStore,
        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_title": chunk.doc_title,
                            "score": round(float(chunk.score), 4),
                            "snippet": (chunk.text 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_title}]\n{(c.text 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": "{}"}