Add LLM token

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

.env

@@ -48,8 +48,16 @@ CHUNK_OVERLAP=50
 MAX_FILE_SIZE_MB=100
 PARSER_BACKEND=aliyun
 CHUNK_BACKEND=aliyun
-# 文档元数据存储后端：json（默认）或 postgres
+# 文档元数据存储后端：启用 postgres 以激活合规分析历史记录（Direction B）及 Finding Chat 持久化（Direction C）
-DOCUMENT_REPOSITORY_BACKEND=json
+DOCUMENT_REPOSITORY_BACKEND=postgres
+# Set to true only when a Celery worker is actually running (./dev.sh start worker).
+# Default false: processing runs in FastAPI's threadpool — no external worker needed.
+USE_CELERY_WORKER=false
+
+# ===== 法规感知爬取配置 =====
+PERCEPTION_CRAWL_TIMEOUT_SECONDS=120
+PERCEPTION_MAX_EVENTS_PER_SOURCE=100
+PERCEPTION_DIFF_SIMILARITY_THRESHOLD=0.85
 
 # ===== API配置 =====
 API_HOST=0.0.0.0
@@ -92,3 +100,30 @@ ALIYUN_LLM_ENHANCEMENT=true
 ALIYUN_ENHANCEMENT_MODE=VLM
 DOCUMENT_PARSE_ARTIFACT_PREFIX=artifacts
 PARSER_FAILURE_MODE=fail
+
+# ===== Reranker 配置 =====
+RERANKER_ENABLED=false
+RERANKER_BASE_URL=http://6.86.80.4:30080/v1
+RERANKER_MODEL=BAAI/bge-reranker-v2-m3
+RERANKER_API_KEY=sk-fVr9KmDZNC4pGDBQj0EUWz9bDmFzNxjYC9EzZpe2bVDsxtz8
+RERANKER_TOP_K=5
+
+# ===== 会话持久化 =====
+SESSION_BACKEND=redis
+
+# ===== 认证配置 =====
+# 生产环境请修改为强随机密钥: python -c "import secrets; print(secrets.token_hex(32))"
+AUTH_SECRET_KEY=ai-compliance-hub-jwt-secret-2026-tsystems
+AUTH_ALGORITHM=HS256
+AUTH_TOKEN_EXPIRE_MINUTES=480
+AUTH_ENABLED=true
+
+# ===== CORS =====
+CORS_ALLOW_ORIGINS=http://localhost:5173
+
+# ===== HyDE ???? =====
+HYDE_ENABLED=true
+HYDE_MAX_TOKENS=200
+HYDE_LLM_PROVIDER=qwen
+HYDE_LLM_MODEL=qwen3.5-flash
+

.env.development

@@ -31,5 +31,5 @@ POSTGRES_PASSWORD=postgresql123456
 POSTGRES_DB=compliance_db
 
 # ===== 文档元数据后端 =====
-# 改为 postgres 以启用 PG 持久化（structure_nodes + semantic_blocks 入库）
+# 改为 postgres 以启用合规分析历史记录（Direction B）和 Finding Chat（Direction C）
 DOCUMENT_REPOSITORY_BACKEND=json

.env.example

@@ -50,7 +50,19 @@ DOCUMENT_METADATA_PATH=backend/data/documents.json
 PARSER_BACKEND=aliyun
 CHUNK_BACKEND=aliyun
 # 文档元数据存储后端：json（默认，无需数据库）或 postgres（启用 PG 持久化）
+# ⚠  以下功能需要 postgres（设为 json 时功能静默降级或报 500）：
+#   - Direction B: 合规分析历史记录 (/compliance/history/*)
+#   - Direction B: DOCX 报告下载
+#   - Direction C: Finding Chat 消息持久化
 DOCUMENT_REPOSITORY_BACKEND=json
+# Set to true only when a Celery worker is running (./dev.sh start worker).
+# Default false: document processing runs in FastAPI's threadpool (no external worker needed).
+USE_CELERY_WORKER=false
+
+# ===== 法规感知爬取配置 =====
+PERCEPTION_CRAWL_TIMEOUT_SECONDS=120
+PERCEPTION_MAX_EVENTS_PER_SOURCE=100
+PERCEPTION_DIFF_SIMILARITY_THRESHOLD=0.85
 
 # ===== 阿里云文档解析 =====
 ALIBABA_ACCESS_KEY_ID=your_aliyun_access_key_id
@@ -96,11 +108,15 @@ RAG_TOP_K=10
 RAG_RETRIEVAL_TOP_K=20
 RAG_MAX_CONTEXT_TOKENS=4000
 RAG_SUMMARY_MAX_TOKENS=1024
+RAG_SKILLS_MAX_TOKENS=2048
 
-# ===== Reranker配置（Cross-Encoder精排，默认关闭）=====
+# ── Reranker (Cross-Encoder) ──────────────────────────────────────────────────
-# 设置 RERANKER_ENABLED=true 并配置 RERANKER_BASE_URL 以启用精排
+# Set RERANKER_ENABLED=true and point to a TEI or Cohere-compatible rerank API.
-RERANKER_ENABLED=false
+# Recommended model: BAAI/bge-reranker-v2.5-gemma2-lightweight (lighter) or
-RERANKER_BASE_URL=
+#                    BAAI/bge-reranker-v2-m3 (heavier, higher quality).
+# The endpoint must expose POST /rerank (TEI style) or POST /v1/rerank (Cohere style).
+RERANKER_ENABLED=true
+RERANKER_BASE_URL=http://6.86.80.4:30080/v1
 RERANKER_MODEL=BAAI/bge-reranker-v2-m3
 RERANKER_API_KEY=
 RERANKER_TOP_K=5
@@ -108,3 +124,45 @@ RERANKER_TOP_K=5
 # ===== 会话配置 =====
 SESSION_MAX_SESSIONS=100
 SESSION_TIMEOUT_MINUTES=30
+# SESSION_BACKEND=redis 启用 Redis 持久化会话（需要 Redis 可用，推荐生产环境）
+# SESSION_BACKEND=memory 使用内存会话（重启丢失，适合本地开发）
+SESSION_BACKEND=memory
+
+# ===== 认证配置 (Auth) =====
+# 生产环境必须替换为强随机密钥:
+#   python -c "import secrets; print(secrets.token_hex(32))"
+AUTH_SECRET_KEY=change-me-in-production-must-be-32-or-more-characters-long
+AUTH_ALGORITHM=HS256
+# Token 有效期（分钟），默认 8 小时
+AUTH_TOKEN_EXPIRE_MINUTES=480
+# 设为 false 可跳过认证（仅限本地开发调试，生产必须 true）
+AUTH_ENABLED=true
+
+
+# ===== HyDE 查询增强 =====
+# HyDE (Hypothetical Document Embeddings): 在检索前让 LLM 生成一段"假设性回答"，
+# 用该段落的 embedding 代替原始查询 embedding 进行向量检索。
+# 无需新模型，复用现有 LLM 和 Embedding 服务。降低此功能可减少每次查询的 LLM 调用次数。
+HYDE_ENABLED=true
+HYDE_MAX_TOKENS=200
+# ?????? LLM;???????????????
+HYDE_LLM_PROVIDER=qwen
+HYDE_LLM_MODEL=qwen3.5-flash
+
+# ===== Agentic RAG 配置 (P0-1) =====
+# 以下参数控制 /api/v1/agent/agentic/stream 多步推理管线
+# 意图分类: simple_qa / compare / multi_hop / ambiguous
+# compare 和 multi_hop 触发查询分解，最多 AGENTIC_MAX_SUB_QUERIES 个子查询
+AGENTIC_MAX_SUB_QUERIES=4
+# 引文锚定 fast-path 阈值: avg_score > 此值 且 chunks >= 3 时跳过 LLM grounding check
+# 降低此值可让更多查询触发 LLM 二次验证（更准确，但延迟+成本增加）
+AGENTIC_GROUNDING_THRESHOLD=0.65
+# 各步骤 LLM 最大 token 数（越小越快，越大越准）
+AGENTIC_INTENT_MAX_TOKENS=200
+AGENTIC_PLAN_MAX_TOKENS=400
+AGENTIC_GROUNDING_MAX_TOKENS=250
+
+# ===== CORS =====
+# 逗号分隔的允许跨域来源列表，生产环境绝不能使用 *
+CORS_ALLOW_ORIGINS=http://localhost:5173
+

.gitignore

@@ -62,3 +62,6 @@ logs/
 
 # codex
 .agents
+
+# personal local records (never commit)
+local/

.superpowers/brainstorm/1055-1780892298/content/directions.html

@@ -0,0 +1,56 @@
+<h2>Compliance Analysis — 哪个方向最值得优化？</h2>
+<p class="subtitle">基于代码深度分析，发现了 4 个有价值的改进方向。选择你最希望深入的那个。</p>
+
+<div class="options">
+
+  <div class="option" data-choice="A" onclick="toggleSelect(this)">
+    <div class="letter">A</div>
+    <div class="content">
+      <h3>⚡ 分析质量提升</h3>
+      <p>并行子句处理（速度 3–5×）、跨编码器重排序、置信度过滤、修复 highlight_terms 失效 Bug、减少 LLM 静默失败。</p>
+      <div class="pros-cons" style="margin-top:10px">
+        <div class="pros"><h4>收益</h4><ul><li>更快、更准确的分析</li><li>消除当前 Bug</li></ul></div>
+        <div class="cons"><h4>难度</h4><ul><li>需要改造 pipeline.py</li></ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="B" onclick="toggleSelect(this)">
+    <div class="letter">B</div>
+    <div class="content">
+      <h3>📋 分析历史 &amp; 专业报告</h3>
+      <p>持久化分析记录（PostgreSQL）、历史对比、PDF/DOCX 专业报告导出、分析版本追踪。</p>
+      <div class="pros-cons" style="margin-top:10px">
+        <div class="pros"><h4>收益</h4><ul><li>结果不再丢失</li><li>可交付给客户的报告</li></ul></div>
+        <div class="cons"><h4>难度</h4><ul><li>需要新增数据库表</li></ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="C" onclick="toggleSelect(this)">
+    <div class="letter">C</div>
+    <div class="content">
+      <h3>💬 深度 Chat 增强</h3>
+      <p>每个 Finding 独立对话线程（持久化）、Chat 上下文绑定真实检索到的法规原文、多轮追问记忆、快捷建议问句生成。</p>
+      <div class="pros-cons" style="margin-top:10px">
+        <div class="pros"><h4>收益</h4><ul><li>Finding 解读深度大幅提升</li><li>用户粘性强</li></ul></div>
+        <div class="cons"><h4>难度</h4><ul><li>需重构 chat 端点</li></ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="D" onclick="toggleSelect(this)">
+    <div class="letter">D</div>
+    <div class="content">
+      <h3>📑 自定义规则 &amp; 模板</h3>
+      <p>用户自定义合规规则库、按行业预设模板（汽车/金融/医疗）、Prompt 版本管理、A/B 测试不同提示策略。</p>
+      <div class="pros-cons" style="margin-top:10px">
+        <div class="pros"><h4>收益</h4><ul><li>适应不同行业场景</li><li>可配置，无需改代码</li></ul></div>
+        <div class="cons"><h4>难度</h4><ul><li>需要规则管理 UI</li></ul></div>
+      </div>
+    </div>
+  </div>
+
+</div>
+
+<p class="subtitle" style="margin-top:20px">💡 也可以多选，或者在终端告诉我你有其他想法。</p>

.superpowers/brainstorm/1055-1780892298/state/events

@@ -0,0 +1,3 @@
+{"type":"click","text":"C\n    \n      💬 深度 Chat 增强\n      每个 Finding 独立对话线程（持久化）、Chat 上下文绑定真实检索到的法规原文、多轮追问记忆、快捷建议问句生成。\n      \n        收益Finding 解读深度大幅提升用户粘性强\n        难度需重构 chat 端点","choice":"C","id":null,"timestamp":1780897984866}
+{"type":"click","text":"B\n    \n      📋 分析历史 & 专业报告\n      持久化分析记录（PostgreSQL）、历史对比、PDF/DOCX 专业报告导出、分析版本追踪。\n      \n        收益结果不再丢失可交付给客户的报告\n        难度需要新增数据库表","choice":"B","id":null,"timestamp":1780897985879}
+{"type":"click","text":"A\n    \n      ⚡ 分析质量提升\n      并行子句处理（速度 3–5×）、跨编码器重排序、置信度过滤、修复 highlight_terms 失效 Bug、减少 LLM 静默失败。\n      \n        收益更快、更准确的分析消除当前 Bug\n        难度需要改造 pipeline.py","choice":"A","id":null,"timestamp":1780897986554}

.superpowers/brainstorm/1055-1780892298/state/server-stopped

@@ -0,0 +1 @@
+{"reason":"idle timeout","timestamp":1780894411095}

.superpowers/brainstorm/1055-1780892298/state/server.pid

@@ -0,0 +1 @@
+1055

QUICK_DEPLOY.md

@@ -390,12 +390,38 @@ Demo-glm/
 | 下载文档 | `/api/v1/documents/download/{doc_id}` | GET | 下载原文PDF/DOCX |
 | 文档列表 | `/api/v1/documents/list` | GET | 列出已上传文档 |
 | 检索知识 | `/api/v1/knowledge/search` | POST | 向量检索 |
-| 单次问答 | `/api/v1/agent/ask` | POST | 智能问答 |
+| 单次问答 | `/api/v1/agent/ask` | POST | 标准单轮问答 |
-| 多轮对话 | `/api/v1/agent/chat` | POST | 会话对话 |
+| 多轮对话 | `/api/v1/agent/chat` | POST | 标准会话对话 |
+| 流式对话 | `/api/v1/agent/chat/stream` | POST | 标准流式问答 (SSE) |
+| **Agentic 流式对话** | **`/api/v1/agent/agentic/stream`** | **POST** | **P0-1 多步推理 (SSE)：意图分析→查询分解→迭代检索→引文锚定→生成** |
 | 会话信息 | `/api/v1/agent/session/{id}` | GET | 获取会话 |
 | 删除会话 | `/api/v1/agent/session/{id}` | DELETE | 删除会话 |
-| Prompt模板 | `/api/v1/agent/templates` | GET | 模板列表 |
+| 会话历史 | `/api/v1/agent/session/{id}/history` | GET | 获取历史记录 |
-| 可用模型 | `/api/v1/agent/models` | GET | LLM模型列表 |
+| 会话列表 | `/api/v1/agent/sessions` | GET | 列出所有会话 |
+
+### Agentic 流式接口说明 (`/api/v1/agent/agentic/stream`)
+
+**请求体** (同 `/agent/chat/stream`)：
+```json
+{ "query": "GB 18384 与 ECE R100 在电池安全上有哪些差异？", "session_id": null, "top_k": 5 }
+```
+
+**额外 SSE 事件** (`thinking`)：
+```
+event: thinking
+data: {"step": "intent_analysis", "status": "done", "intent_type": "compare", "requires_decomposition": true}
+
+event: thinking
+data: {"step": "query_planning", "status": "done", "sub_queries": ["GB 18384 电池安全要求", "ECE R100 电池安全要求"]}
+
+event: thinking
+data: {"step": "retrieving", "status": "done", "query": "GB 18384 电池安全要求", "index": 1, "total": 2, "found": 8}
+
+event: thinking
+data: {"step": "grounding_check", "status": "done", "sufficient": true, "confidence": 0.82, "reason": "检索置信度充足"}
+```
+
+**意图类型**：`simple_qa`（单跳）/ `compare`（对比）/ `multi_hop`（多跳）/ `ambiguous`（模糊）
 
 ---
 

backend/app/api/dependencies/__init__.py

@@ -0,0 +1,5 @@
+"""FastAPI dependency functions for authentication and authorisation.
+
+Import `get_current_user` or `require_role` into route modules to protect
+endpoints. Both use the shared JWTHandler wired through bootstrap.
+"""

backend/app/api/dependencies/auth.py

@@ -0,0 +1,72 @@
+"""FastAPI dependencies for JWT authentication.
+
+Usage in a route:
+    from app.api.dependencies.auth import get_current_user, require_role
+    from app.domain.auth.models import UserRole
+
+    @router.get("/protected")
+    async def protected(user: UserClaims = Depends(get_current_user)):
+        return {"user": user.username}
+
+    @router.delete("/admin-only")
+    async def admin_only(user: UserClaims = Depends(require_role(UserRole.ADMIN))):
+        ...
+"""
+
+from __future__ import annotations
+
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+
+from app.config.settings import settings
+from app.domain.auth.models import UserClaims, UserRole
+from app.shared.bootstrap import get_jwt_handler
+
+# Use Bearer token scheme — client sends `Authorization: Bearer <token>`.
+_bearer = HTTPBearer(auto_error=False)
+
+
+async def get_current_user(
+    credentials: HTTPAuthorizationCredentials | None = Depends(_bearer),
+) -> UserClaims:
+    """Extract and validate the JWT from the Authorization header.
+
+    Returns the decoded UserClaims on success.
+    Raises HTTP 401 when the token is missing, expired, or invalid.
+    When auth_enabled=False (development), returns a synthetic admin user.
+    """
+    if not settings.auth_enabled:
+        # Development bypass — never enable this in production.
+        return UserClaims(user_id="dev", username="dev-admin", role=UserRole.ADMIN)
+
+    if credentials is None:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing authentication token",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    try:
+        return get_jwt_handler().decode_token(credentials.credentials)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail=str(exc),
+            headers={"WWW-Authenticate": "Bearer"},
+        ) from exc
+
+
+def require_role(*roles: UserRole):
+    """Return a dependency that enforces one of the given roles.
+
+    Example:
+        Depends(require_role(UserRole.ADMIN, UserRole.LEGAL))
+    """
+    async def _check(user: UserClaims = Depends(get_current_user)) -> UserClaims:
+        """Verify the user holds one of the required roles."""
+        if user.role not in roles:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN,
+                detail=f"Role '{user.role}' is not permitted. Required: {[r.value for r in roles]}",
+            )
+        return user
+    return _check

backend/app/api/main.py

@@ -8,6 +8,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 from loguru import logger
 
+from app.api.middleware.audit import AuditMiddleware
 from app.api.models import ErrorResponse
 from app.api.routes import api_router
 from app.config.logging import setup_logging
@@ -46,14 +47,23 @@ app = FastAPI(
     redoc_url="/redoc",
 )
 
+# Tighten CORS — only allow configured origins.
+# Set CORS_ALLOW_ORIGINS in .env to the real frontend URL in production.
+_ORIGINS = [o.strip() for o in settings.cors_allow_origins.split(",") if o.strip()]
+if not _ORIGINS:
+    _ORIGINS = ["http://localhost:5173"]
+
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
+    allow_origins=_ORIGINS,
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
+# Audit middleware logs every authenticated API call for compliance traceability.
+app.add_middleware(AuditMiddleware)
+
 app.include_router(api_router, prefix="/api/v1")
 
 

backend/app/api/middleware/__init__.py

@@ -0,0 +1 @@
+"""HTTP middleware for cross-cutting concerns: audit logging."""

backend/app/api/middleware/audit.py

@@ -0,0 +1,56 @@
+"""Audit logging middleware.
+
+Logs every API request with method, path, status code, response time,
+and the authenticated user identity (extracted from the JWT when present).
+Log lines are structured so they can be ingested by ELK / Loki.
+"""
+
+from __future__ import annotations
+
+import time
+
+from fastapi import Request, Response
+from loguru import logger
+from starlette.middleware.base import BaseHTTPMiddleware
+
+
+class AuditMiddleware(BaseHTTPMiddleware):
+    """Log all API calls. Skips health/docs paths to reduce noise."""
+
+    # Paths that produce no audit log entry.
+    _SKIP_PREFIXES = ("/health", "/docs", "/redoc", "/openapi.json")
+
+    async def dispatch(self, request: Request, call_next) -> Response:
+        """Intercept the request, call the handler, and log the outcome."""
+        path = request.url.path
+        if path == "/" or any(path == p or path.startswith(p + "/") for p in self._SKIP_PREFIXES):
+            return await call_next(request)
+
+        start = time.perf_counter()
+        response = await call_next(request)
+        elapsed_ms = int((time.perf_counter() - start) * 1000)
+
+        # Extract user identity from JWT header for structured audit records.
+        # The token is not re-validated here — auth dependencies do that upstream.
+        user_id = "anonymous"
+        username = "anonymous"
+        auth_header = request.headers.get("authorization", "")
+        if auth_header.startswith("Bearer "):
+            try:
+                from app.shared.bootstrap import get_jwt_handler
+                claims = get_jwt_handler().decode_token(auth_header[7:])
+                user_id = claims.user_id
+                username = claims.username
+            except Exception:
+                pass
+
+        logger.info(
+            "AUDIT method={} path={} status={} elapsed_ms={} user_id={} username={}",
+            request.method,
+            path,
+            response.status_code,
+            elapsed_ms,
+            user_id,
+            username,
+        )
+        return response

backend/app/api/models/agent.py

@@ -42,6 +42,11 @@ class ChatRequest(BaseModel):
     provider: Optional[str] = None
     model: Optional[str] = None
     top_k: Optional[int] = Field(default=None, ge=1, le=20)
+    # Optional document text uploaded by the user as conversation context.
+    # The text is injected directly into the LLM prompt so the model can
+    # answer questions about it without vector-store indexing.
+    context_text: Optional[str] = Field(default=None, max_length=12000)
+    context_filename: Optional[str] = Field(default=None, max_length=256)
 
 
 class ChatResponse(BaseModel):

backend/app/api/routes/__init__.py

@@ -1,6 +1,7 @@
 """Initialize the app.api.routes package."""
 
 from fastapi import APIRouter
+from .auth import router as auth_router
 from .compliance import router as compliance_router
 from .documents import router as documents_router
 from .knowledge import router as knowledge_router
@@ -14,7 +15,8 @@ from .rag import router as rag_router
 # Keep package boundaries explicit so backend imports stay predictable.
 api_router = APIRouter()
 
-# Keep package boundaries explicit so backend imports stay predictable.
+# Auth routes first so /auth/token is easy to discover.
+api_router.include_router(auth_router)
 api_router.include_router(documents_router)
 api_router.include_router(knowledge_router)
 api_router.include_router(agent_router)
@@ -25,6 +27,7 @@ api_router.include_router(rag_router)
 
 __all__ = [
     "api_router",
+    "auth_router",
     "documents_router",
     "knowledge_router",
     "agent_router",

backend/app/api/routes/agent.py

@@ -20,7 +20,11 @@ from app.api.models import (
 )
 from app.config.settings import settings
 from app.shared.async_utils import iter_in_thread
-from app.shared.bootstrap import get_agent_conversation_service, get_agent_session_service
+from app.shared.bootstrap import (
+    get_agent_conversation_service,
+    get_agent_session_service,
+    get_agentic_conversation_service,
+)
 # Keep route handlers close to their transport-layer wiring for easier auditing.
 
 
@@ -182,3 +186,58 @@ async def submit_feedback(request: FeedbackRequest):
         return {"message": "反馈已提交", "session_id": result.session_id, "message_index": result.message_index}
     except ValueError as exc:
         raise HTTPException(status_code=404, detail=str(exc))
+
+
+# ── P0-1: Agentic RAG endpoint ────────────────────────────────────────────────
+
+@router.post("/agentic/stream")
+async def agentic_stream(request: ChatRequest):
+    """Stream an Agentic RAG response with live multi-step reasoning trace.
+
+    Unlike the standard ``/chat/stream`` endpoint this route runs a full pipeline:
+    intent analysis → query planning → iterative retrieval → grounding check →
+    answer generation.
+
+    Extra SSE event types beyond the standard ones:
+
+    * ``thinking`` — reasoning sub-step progress; data is a JSON object with
+      ``step`` (intent_analysis / query_planning / retrieving / grounding_check),
+      ``status`` (running / done), and step-specific fields.
+
+    The ``sources``, ``content``, and ``done`` events are identical to the standard
+    chat-stream contract so the existing frontend parser can handle them without
+    changes.
+    """
+    async def generate_sse() -> AsyncGenerator[str, None]:
+        """Handle SSE generation for the agentic chat endpoint."""
+        try:
+            session_id_, event_stream = get_agentic_conversation_service().stream_agentic_chat(
+                query=request.query,
+                session_id=request.session_id,
+                filters=request.filters,
+                provider=request.provider or settings.llm_provider,
+                model=request.model or settings.llm_model,
+                top_k=request.top_k or settings.rag_top_k,
+                context_text=request.context_text,
+                context_filename=request.context_filename,
+            )
+            yield f"event: session\ndata: {json.dumps({'session_id': session_id_})}\n\n"
+            async for event_data in iter_in_thread(event_stream):
+                event_type = event_data.get("event", "content")
+                data = event_data.get("data", "")
+                if isinstance(data, (dict, list)):
+                    yield f"event: {event_type}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+                else:
+                    yield f"event: {event_type}\ndata: {data}\n\n"
+        except Exception as exc:
+            yield f"event: error\ndata: {str(exc)}\n\n"
+
+    return StreamingResponse(
+        generate_sse(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )

backend/app/api/routes/auth.py

@@ -0,0 +1,63 @@
+"""Authentication routes — token issuance only.
+
+POST /auth/token  — exchange username + password for a JWT.
+GET  /auth/me     — return the current user identity (requires token).
+"""
+
+from __future__ import annotations
+
+from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordRequestForm
+from pydantic import BaseModel
+
+from app.api.dependencies.auth import get_current_user
+from app.config.settings import settings
+from app.domain.auth.models import UserClaims
+from app.shared.bootstrap import get_jwt_handler, get_user_store
+
+
+router = APIRouter(prefix="/auth", tags=["认证"])
+
+
+class TokenResponse(BaseModel):
+    """JWT token response body."""
+
+    access_token: str
+    token_type: str = "bearer"
+    expires_in: int
+
+
+@router.post("/token", response_model=TokenResponse)
+async def login(form: OAuth2PasswordRequestForm = Depends()):
+    """Issue a JWT for valid username + password credentials.
+
+    Uses standard OAuth2 password grant form fields — compatible with
+    Swagger UI Authorize button.
+    """
+    user = get_user_store().authenticate(form.username, form.password)
+    if user is None:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect username or password",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    token = get_jwt_handler().create_access_token(
+        user_id=user.id,
+        username=user.username,
+        role=user.role,
+    )
+    return TokenResponse(
+        access_token=token,
+        token_type="bearer",
+        expires_in=settings.auth_token_expire_minutes * 60,
+    )
+
+
+@router.get("/me")
+async def get_me(current_user: UserClaims = Depends(get_current_user)):
+    """Return the identity of the currently authenticated user."""
+    return {
+        "user_id": current_user.user_id,
+        "username": current_user.username,
+        "role": current_user.role.value,
+    }

backend/app/api/routes/compliance.py

@@ -7,10 +7,12 @@ import json
 from pathlib import Path
 from typing import AsyncGenerator, Optional
 
-from fastapi import APIRouter, File, Form, UploadFile
+from fastapi import APIRouter, Depends, File, Form, UploadFile
 from fastapi.responses import StreamingResponse
 from loguru import logger
 
+from app.api.dependencies.auth import get_current_user
+from app.domain.auth.models import UserClaims
 from app.schemas.compliance import (
     AnalyzeResponse,
     ComplianceChatRequest,
@@ -75,6 +77,7 @@ async def analyze_stream(
     file: Optional[UploadFile] = File(None),
     domains: Optional[str] = Form(None),
     title: Optional[str] = Form(None),
+    current_user: UserClaims = Depends(get_current_user),
 ):
     """Stream compliance analysis as SSE events.
 
@@ -82,10 +85,10 @@ async def analyze_stream(
     Events: stage | source | finding | done | error
     """
     from app.application.compliance.pipeline import (
-        check_clause_compliance,
+        detect_cross_clause_conflicts,
         extract_text_from_doc_id,
         extract_text_from_file,
-        retrieve_for_clause,
+        run_clauses_streaming,
         split_into_clauses,
         synthesize_conclusion,
     )
@@ -133,22 +136,32 @@ async def analyze_stream(
             await asyncio.sleep(0)
             clauses: list[str] = await asyncio.to_thread(split_into_clauses, para_text, client)
 
-            # ── Stage 3: retrieve + gap check per clause ──────────────────
+            # ── Stage 3: progressive per-clause retrieve + gap check ──────
             findings: list[dict] = []
+            total_clauses = len(clauses)
 
-            for i, clause in enumerate(clauses):
             yield _sse({
                 "type": "stage",
                 "stage": "analyzing",
-                    "label": f"Analyzing clause {i + 1}/{len(clauses)}…",
+                "label": f"Analyzing {total_clauses} clauses…",
             })
+            # Emit initial progress so the frontend can show the total count
+            yield _sse({"type": "progress", "done": 0, "total": total_clauses})
             await asyncio.sleep(0)
 
-                chunks = await asyncio.to_thread(
+            done_count = 0
-                    retrieve_for_clause, clause, retrieval_service, 5, domains or None
+            # Stream results as each clause completes (not after all finish)
-                )
+            async for res in run_clauses_streaming(
+                clauses, retrieval_service, client,
+                top_k=5,
+                domains=domains or None,
+            ):
+                done_count += 1
+                i = res["index"]
+                chunks = res["chunks"]
+                finding = res["finding"]
 
-                # Emit source events
+                # Emit source events for this clause
                 for chunk in chunks[:3]:
                     yield _sse({
                         "type": "source",
@@ -157,15 +170,25 @@ async def analyze_stream(
                         "score": round(float(getattr(chunk, "score", 0)), 3),
                         "status": "retrieved",
                         "full_content": (getattr(chunk, "text", "") or "")[:300],
+                        "clause_index": i,
                     })
-                await asyncio.sleep(0)
 
-                finding = await asyncio.to_thread(check_clause_compliance, clause, chunks, client)
                 if finding:
                     findings.append(finding)
                     yield _sse({"type": "finding", **finding})
+
+                # Real progress update after each clause completes
+                yield _sse({"type": "progress", "done": done_count, "total": total_clauses})
                 await asyncio.sleep(0)
 
+            # ── Stage 3b: cross-clause conflict detection ─────────────────
+            if findings:
+                conflicts = await asyncio.to_thread(
+                    detect_cross_clause_conflicts, findings, client
+                )
+                if conflicts:
+                    yield _sse({"type": "conflicts", "items": conflicts})
+
             # ── Stage 4: synthesize conclusion ────────────────────────────
             yield _sse({"type": "stage", "stage": "concluding", "label": "Generating conclusion…"})
             await asyncio.sleep(0)
@@ -175,6 +198,45 @@ async def analyze_stream(
             )
             yield _sse({"type": "done", **conclusion_data})
 
+            # Auto-save analysis to database
+            try:
+                from app.shared.bootstrap import get_compliance_repository
+                from app.domain.compliance.ports import AnalysisRecord, FindingRecord
+                from datetime import datetime
+
+                repo = get_compliance_repository()
+                finding_records = [
+                    FindingRecord(
+                        id="",
+                        analysis_id="",
+                        seq=i,
+                        title=f.get("title", ""),
+                        description=f.get("desc", ""),
+                        status=f.get("status", "ok"),
+                        clause_ref=f.get("clause_ref"),
+                    )
+                    for i, f in enumerate(findings)
+                ]
+                record = AnalysisRecord(
+                    id="",
+                    created_at=datetime.utcnow(),
+                    created_by=current_user.username if hasattr(current_user, "username") else None,
+                    doc_name=file_name or (title or "Pasted text"),
+                    standard_name=title or "",
+                    risk_score=conclusion_data.get("risk_score", 0),
+                    conclusion=conclusion_data.get("conclusion", ""),
+                    actions=conclusion_data.get("actions", []),
+                    para_text=conclusion_data.get("para_text", ""),
+                    highlight_terms=conclusion_data.get("highlight_terms", []),
+                    findings=finding_records,
+                )
+                analysis_id = await asyncio.to_thread(repo.save_analysis, record)
+                yield _sse({"type": "saved", "analysis_id": analysis_id})
+            except NotImplementedError:
+                pass  # No postgres backend configured — skip saving
+            except Exception as exc:
+                logger.warning("Failed to auto-save compliance analysis: {}", exc)
+
         except Exception as exc:
             logger.exception("analyze-stream pipeline error")
             yield _sse({"type": "error", "text": str(exc)})
@@ -222,3 +284,226 @@ async def compliance_chat(segment_id: int, request: ComplianceChatRequest):
         media_type="text/event-stream",
         headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
     )
+
+
+@router.get("/history")
+async def list_history(
+    limit: int = 20,
+    offset: int = 0,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return paginated list of saved compliance analyses (newest first)."""
+    from app.shared.bootstrap import get_compliance_repository
+    try:
+        repo = get_compliance_repository()
+        records = await asyncio.to_thread(repo.list_analyses, limit, offset)
+        return [
+            {
+                "id": r.id,
+                "created_at": r.created_at.isoformat(),
+                "created_by": r.created_by,
+                "doc_name": r.doc_name,
+                "standard_name": r.standard_name,
+                "risk_score": r.risk_score,
+                "finding_count": len(r.findings),
+            }
+            for r in records
+        ]
+    except NotImplementedError:
+        return []
+
+
+@router.get("/history/{analysis_id}")
+async def get_history_item(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return full analysis record including findings."""
+    from app.shared.bootstrap import get_compliance_repository
+    from fastapi import HTTPException
+    repo = get_compliance_repository()
+    record = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not record:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+    return {
+        "id": record.id,
+        "created_at": record.created_at.isoformat(),
+        "created_by": record.created_by,
+        "doc_name": record.doc_name,
+        "standard_name": record.standard_name,
+        "risk_score": record.risk_score,
+        "conclusion": record.conclusion,
+        "actions": record.actions,
+        "para_text": record.para_text,
+        "highlight_terms": record.highlight_terms,
+        "findings": [
+            {
+                "id": f.id,
+                "seq": f.seq,
+                "title": f.title,
+                "description": f.description,
+                "status": f.status,
+                "clause_ref": f.clause_ref,
+            }
+            for f in record.findings
+        ],
+    }
+
+
+@router.delete("/history/{analysis_id}", status_code=204)
+async def delete_history_item(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Delete a saved analysis (cascade removes findings and chat messages)."""
+    from app.shared.bootstrap import get_compliance_repository
+    repo = get_compliance_repository()
+    await asyncio.to_thread(repo.delete_analysis, analysis_id)
+
+
+@router.get("/history/{analysis_id}/download")
+async def download_history_docx(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return a DOCX compliance report for the given analysis."""
+    from app.shared.bootstrap import get_compliance_repository
+    from app.infrastructure.compliance.docx_export import generate_docx
+    from fastapi import HTTPException
+    from fastapi.responses import Response
+
+    repo = get_compliance_repository()
+    record = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not record:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+
+    docx_bytes = await asyncio.to_thread(generate_docx, record)
+    safe_name = (record.doc_name or "report").replace(" ", "_")[:50]
+    filename = f"compliance_{safe_name}_{record.created_at.strftime('%Y%m%d')}.docx"
+    return Response(
+        content=docx_bytes,
+        media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+@router.get("/analyses/{analysis_id}/findings/{finding_id}/chat")
+async def get_finding_chat_history(
+    analysis_id: str,
+    finding_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return persisted chat messages for a finding thread, oldest first."""
+    from app.shared.bootstrap import get_compliance_repository
+    try:
+        repo = get_compliance_repository()
+        messages = await asyncio.to_thread(repo.get_messages, finding_id)
+        return messages
+    except NotImplementedError:
+        return []
+
+
+@router.post("/analyses/{analysis_id}/findings/{finding_id}/suggestions")
+async def get_finding_suggestions(
+    analysis_id: str,
+    finding_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Generate 3 LLM-powered follow-up question suggestions for a finding."""
+    from app.application.compliance.pipeline import generate_suggestions
+    from app.shared.bootstrap import get_compliance_repository
+    from app.services.llm.llm_factory import get_llm_client
+    from fastapi import HTTPException
+
+    repo = get_compliance_repository()
+    analysis = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not analysis:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+
+    finding = next((f for f in analysis.findings if f.id == finding_id), None)
+    if not finding:
+        raise HTTPException(status_code=404, detail="Finding not found")
+
+    client = get_llm_client(provider=settings.llm_provider, model=settings.llm_model)
+    questions = await asyncio.to_thread(generate_suggestions, finding, analysis, client)
+    return {"questions": questions}
+
+
+@router.post("/analyses/{analysis_id}/findings/{finding_id}/chat")
+async def finding_chat(
+    analysis_id: str,
+    finding_id: str,
+    request: ComplianceChatRequest,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Stream a grounded chat response for a specific finding.
+
+    Loads the finding and analysis from DB to build grounded context.
+    Persists both user message and assistant response to finding_chat_messages.
+    """
+    from app.application.compliance.pipeline import build_finding_context
+    from app.shared.bootstrap import get_compliance_repository
+    from fastapi import HTTPException
+
+    repo = get_compliance_repository()
+    analysis = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not analysis:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+    finding = next((f for f in analysis.findings if f.id == finding_id), None)
+    if not finding:
+        raise HTTPException(status_code=404, detail="Finding not found")
+
+    # Persist user message
+    await asyncio.to_thread(
+        repo.save_message, analysis_id, finding_id, "user", request.query
+    )
+
+    # Build message history (last 10 messages = 5 turns)
+    history = await asyncio.to_thread(repo.get_messages, finding_id)
+    history_messages = [
+        {"role": m["role"], "content": m["content"]}
+        for m in history[-10:]
+    ]
+
+    # Build grounded system context
+    system_context = build_finding_context(finding, analysis)
+    full_query = f"[Compliance Finding Context]\n{system_context}\n\nUser question: {request.query}"
+
+    assistant_buffer: list[str] = []
+
+    async def generate() -> AsyncGenerator[str, None]:
+        try:
+            _, event_stream = get_agent_conversation_service().stream_chat(
+                query=full_query,
+                top_k=5,
+                prompt_template="compliance_qa",
+            )
+            for event in event_stream:
+                event_type = event.get("event", "")
+                if event_type == "content":
+                    text = event.get("data", "")
+                    if text:
+                        assistant_buffer.append(text)
+                        yield _sse({"type": "chunk", "text": text})
+                elif event_type == "done":
+                    yield _sse({"type": "done"})
+                await asyncio.sleep(0)
+        except Exception as exc:
+            logger.exception("finding_chat stream error")
+            yield _sse({"type": "error", "text": str(exc)})
+        finally:
+            # Persist assistant response after stream completes
+            full_response = "".join(assistant_buffer)
+            if full_response:
+                try:
+                    await asyncio.to_thread(
+                        repo.save_message, analysis_id, finding_id, "assistant", full_response
+                    )
+                except Exception as exc:
+                    logger.warning("Failed to persist assistant message: {}", exc)
+
+    return StreamingResponse(
+        generate(),
+        media_type="text/event-stream",
+        headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
+    )

backend/app/api/routes/documents.py

@@ -5,12 +5,15 @@ from __future__ import annotations
 from io import BytesIO
 from urllib.parse import quote
 
-from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, UploadFile
 from fastapi.responses import StreamingResponse
 from loguru import logger
 
+from app.api.dependencies.auth import get_current_user
 from app.api.models import DocumentUploadResponse
 from app.application.documents import DocumentProcessResult
+from app.config.settings import settings
+from app.domain.auth.models import UserClaims
 from app.shared.bootstrap import get_document_command_service, get_document_query_service
 # Keep route handlers close to their transport-layer wiring for easier auditing.
 
@@ -31,16 +34,60 @@ def _document_response(result: DocumentProcessResult) -> DocumentUploadResponse:
     )
 
 
+def _run_process_in_background(
+    *,
+    doc_id: str,
+    file_name: str,
+    final_doc_name: str,
+    content: bytes,
+    regulation_type: str,
+    version: str,
+    generate_summary: bool,
+    run_id: str | None,
+) -> None:
+    """Run document processing synchronously inside a FastAPI BackgroundTask thread.
+
+    FastAPI executes BackgroundTasks in a threadpool executor, so blocking I/O
+    (parser API calls, embedding, Milvus upsert) is safe here.
+    """
+    try:
+        svc = get_document_command_service()
+        svc._process_document(
+            doc_id=doc_id,
+            file_name=file_name,
+            final_doc_name=final_doc_name,
+            content=content,
+            regulation_type=regulation_type,
+            version=version,
+            generate_summary=generate_summary,
+            run_id=run_id,
+        )
+    except Exception:
+        logger.exception("BackgroundTask document processing failed: doc_id={}", doc_id)
+
+
 @router.post("/upload", response_model=DocumentUploadResponse)
 async def upload_document(
+    background_tasks: BackgroundTasks,
     file: UploadFile = File(..., description="上传的文档文件"),
     doc_id: str | None = Form(None, description="客户端预分配的文档ID，不传则自动生成"),
     doc_name: str | None = Form(None, description="文档名称"),
     regulation_type: str | None = Form(None, description="法规类型"),
     version: str | None = Form(None, description="文档版本"),
     generate_summary: bool = Form(False, description="是否生成摘要"),
+    sync: bool = Form(False, description="同步处理（演示/测试用，默认异步处理）"),
+    current_user: UserClaims = Depends(get_current_user),
 ):
-    """Handle upload document."""
+    """Upload a document and process it asynchronously.
+
+    Default path (sync=false):
+      1. Store binary to MinIO immediately — returns within seconds.
+      2. Schedule parse→embed→index as a FastAPI BackgroundTask (same process,
+         threadpool) OR enqueue to Celery workers when USE_CELERY_WORKER=true.
+      3. Poll GET /documents/status/{doc_id} for progress.
+
+    sync=true path: full inline processing, blocks until complete (demo / CI use).
+    """
     content = await file.read()
     if not file.filename:
         raise HTTPException(status_code=400, detail="文件名不能为空")
@@ -48,7 +95,11 @@ async def upload_document(
         raise HTTPException(status_code=400, detail="上传文件为空")
 
     try:
-        result = get_document_command_service().upload_and_process(
+        svc = get_document_command_service()
+
+        if sync:
+            # Synchronous fallback: full inline processing.
+            result = svc.upload_and_process(
                 doc_id=doc_id,
                 file_name=file.filename,
                 content=content,
@@ -58,9 +109,59 @@ async def upload_document(
                 version=version or "",
                 generate_summary=generate_summary,
             )
+        else:
+            # Step 1: store binary and create the document record (fast, sync).
+            stored_doc_id, run_id = svc.store_document(
+                doc_id=doc_id,
+                file_name=file.filename,
+                content=content,
+                content_type=file.content_type or "application/octet-stream",
+                doc_name=doc_name,
+                regulation_type=regulation_type or "",
+                version=version or "",
+                generate_summary=generate_summary,
+            )
+            final_doc_name = doc_name or file.filename
+
+            # Step 2: schedule processing via Celery worker OR FastAPI BackgroundTask.
+            if settings.use_celery_worker:
+                from app.infrastructure.tasks.document_tasks import process_document_task
+                process_document_task.delay(
+                    doc_id=stored_doc_id,
+                    file_name=file.filename,
+                    doc_name=final_doc_name,
+                    regulation_type=regulation_type or "",
+                    version=version or "",
+                    generate_summary=generate_summary,
+                    run_id=run_id,
+                )
+                processing_note = "已入 Celery 队列，由 Worker 处理。"
+            else:
+                # Default: run in FastAPI's threadpool — no external worker needed.
+                background_tasks.add_task(
+                    _run_process_in_background,
+                    doc_id=stored_doc_id,
+                    file_name=file.filename,
+                    final_doc_name=final_doc_name,
+                    content=content,
+                    regulation_type=regulation_type or "",
+                    version=version or "",
+                    generate_summary=generate_summary,
+                    run_id=run_id,
+                )
+                processing_note = "正在后台处理。"
+
+            result = DocumentProcessResult(
+                doc_id=stored_doc_id,
+                doc_name=final_doc_name,
+                status="stored",
+                message=f"文件已存储，{processing_note}请轮询 GET /documents/status/{{doc_id}} 查看进度。",
+            )
+
         if result.status == "failed":
             raise HTTPException(status_code=500, detail=result.message)
         return _document_response(result)
+
     except HTTPException:
         raise
     except Exception as exc:
@@ -106,7 +207,7 @@ async def download_document(doc_id: str):
 
 
 @router.get("/list")
-async def list_documents():
+async def list_documents(current_user: UserClaims = Depends(get_current_user)):
     """List documents."""
     documents = get_document_query_service().list_documents()
     return {
@@ -140,6 +241,9 @@ async def get_document_management_list():
                 "updated_at": item.updated_at.isoformat(),
                 "regulation_type": item.regulation_type,
                 "version": item.version,
+                # True only when the original binary file is stored in MinIO.
+                # Milvus-only synthetic docs have no binary file — download is disabled.
+                "has_file": bool(item.object_name),
             }
             for item in documents
         ],
@@ -148,7 +252,7 @@ async def get_document_management_list():
 
 
 @router.delete("/{doc_id}")
-async def delete_document(doc_id: str):
+async def delete_document(doc_id: str, current_user: UserClaims = Depends(get_current_user)):
     """Delete a document and its associated data."""
     deleted = get_document_command_service().delete(doc_id)
     if not deleted:

backend/app/api/routes/perception.py

@@ -4,10 +4,12 @@ from __future__ import annotations
 
 import json
 
-from fastapi import APIRouter, Query
+from fastapi import APIRouter, Depends, Query
 from fastapi.responses import StreamingResponse
 
-from app.shared.bootstrap import get_perception_service
+from app.shared.bootstrap import get_crawl_service, get_event_store, get_perception_service
+from app.api.dependencies.auth import get_current_user
+from app.domain.auth.models import UserClaims
 from app.shared.async_utils import iter_in_thread
 
 router = APIRouter(prefix="/perception", tags=["智能感知"])
@@ -65,3 +67,77 @@ async def analyze_event(event_id: str):
             "X-Accel-Buffering": "no",
         },
     )
+
+
+@router.post("/crawl")
+async def run_crawl(
+    body: dict = None,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Trigger manual crawl of regulatory sources. Streams SSE progress.
+
+    Body (optional): {"sources": ["CATARC", "国标委·强制性", "EUR-Lex"]}
+    Omit sources to crawl all registered sources.
+    """
+    sources: list[str] | None = (body or {}).get("sources")
+    crawl_svc = get_crawl_service()
+
+    async def crawl_stream():
+        async for item in iter_in_thread(crawl_svc.run_crawl(sources=sources)):
+            event_name = item.get("event", "message")
+            data = item.get("data", "")
+            if isinstance(data, (dict, list)):
+                data = json.dumps(data, ensure_ascii=False)
+            yield f"event: {event_name}\ndata: {data}\n\n"
+
+    return StreamingResponse(
+        crawl_stream(),
+        media_type="text/event-stream",
+        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+    )
+
+
+@router.post("/events/{event_id}/process")
+async def process_event(
+    event_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Trigger LLM pipeline (extract + assess + diff) for a single event."""
+    from datetime import UTC, datetime
+    from app.infrastructure.perception.llm_pipeline import LlmPipeline
+    from app.shared.bootstrap import get_retrieval_service
+
+    event = get_perception_service().get_event(event_id)
+    if not event:
+        from fastapi import HTTPException
+        raise HTTPException(status_code=404, detail=f"Event {event_id} not found")
+
+    store = get_event_store()
+    pipeline = LlmPipeline()
+
+    structure = pipeline.extract_structure(event)
+    event.update(structure)
+    event["affected_docs"] = pipeline.assess_impact(event, get_retrieval_service())
+    event["processed_at"] = datetime.now(UTC).isoformat()
+    store.upsert(event)
+
+    return {"status": "ok", "event_id": event_id, "processed_at": event["processed_at"]}
+
+
+@router.get("/events/{event_id}/diff")
+async def get_event_diff(event_id: str):
+    """Return semantic diff detail for an event (only available if previously crawled twice)."""
+    event = get_perception_service().get_event(event_id)
+    if not event:
+        from fastapi import HTTPException
+        raise HTTPException(status_code=404, detail=f"Event {event_id} not found")
+    if not event.get("change_summary"):
+        from fastapi import HTTPException
+        raise HTTPException(status_code=404, detail="No diff available for this event")
+    return {
+        "event_id": event_id,
+        "change_summary": event.get("change_summary"),
+        "changed_sections": event.get("changed_sections") or [],
+        "previous_hash": event.get("previous_hash"),
+        "content_hash": event.get("content_hash"),
+    }

backend/app/api/routes/rag.py

@@ -3,16 +3,24 @@
 from __future__ import annotations
 
 import json
-from typing import AsyncGenerator
+import os
+import re
+import tempfile
+from typing import AsyncGenerator, Optional
 
-from fastapi import APIRouter
+from fastapi import APIRouter, Depends, File, UploadFile
 from fastapi.responses import StreamingResponse
+from loguru import logger
 
+from app.api.dependencies.auth import get_current_user
 from app.config.settings import settings
+from app.domain.auth.models import UserClaims
 from app.schemas.rag import RagChatRequest, QuickQuestionsResponse, QuickQuestion
 from app.shared.async_utils import iter_in_thread
 from app.shared.bootstrap import get_agent_conversation_service
 
+# Maximum characters of document text injected as LLM context (≈ 6 000 tokens).
+_MAX_CONTEXT_CHARS = 8_000
 
 router = APIRouter(prefix="/rag", tags=["RAG问答"])
 
@@ -26,14 +34,90 @@ _DEFAULT_QUICK_QUESTIONS = [
 ]
 
 
+def _extract_text_from_bytes(content: bytes, filename: str) -> str:
+    """Extract plain text from an uploaded file using the document parser.
+
+    Tries the configured parser first; falls back to raw UTF-8 decode for
+    plain-text formats (.txt, .md). Returns at most _MAX_CONTEXT_CHARS characters
+    so the text fits comfortably inside the LLM context window.
+    """
+    suffix = os.path.splitext(filename or "doc.pdf")[1] or ".pdf"
+    # Fast path: plain-text files don't need a parser
+    if suffix.lower() in {".txt", ".md", ".csv"}:
+        try:
+            return content.decode("utf-8", errors="replace")[:_MAX_CONTEXT_CHARS]
+        except Exception:
+            pass
+
+    tmp_path = ""
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            tmp.write(content)
+            tmp_path = tmp.name
+        from app.shared.bootstrap import get_document_command_service
+        svc = get_document_command_service()
+        parsed = svc.parser.parse(file_path=tmp_path, doc_id="ctx_extract", doc_name=filename)
+        if parsed.raw_text:
+            return parsed.raw_text[:_MAX_CONTEXT_CHARS]
+        # Fallback: join semantic blocks
+        return "\n".join(
+            b.get("text", "") for b in parsed.semantic_blocks if b.get("text")
+        )[:_MAX_CONTEXT_CHARS]
+    except Exception as exc:
+        logger.warning("Context text extraction failed for {}: {}", filename, exc)
+        return ""
+    finally:
+        if tmp_path:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+
+@router.post("/upload-context")
+async def upload_context(
+    file: UploadFile = File(...),
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Extract text from an uploaded document and return it as conversation context.
+
+    The client stores the returned text and includes it in subsequent /rag/chat
+    requests via the context_text field — the LLM receives the document content
+    directly without requiring vector-store indexing.
+    """
+    content = await file.read()
+    filename = file.filename or "document"
+    text = await __import__("asyncio").to_thread(_extract_text_from_bytes, content, filename)
+    if not text.strip():
+        from fastapi import HTTPException
+        raise HTTPException(status_code=422, detail="Could not extract text from the uploaded file.")
+    return {
+        "filename": filename,
+        "text": text,
+        "char_count": len(text),
+        "truncated": len(text) >= _MAX_CONTEXT_CHARS,
+    }
+
+
 @router.post("/chat")
-async def rag_chat(request: RagChatRequest):
+async def rag_chat(
-    """Stream RAG Q&A using the real agent service."""
+    request: RagChatRequest,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Stream RAG Q&A using the real agent service.
+
+    When request.context_text is provided the document text is passed directly
+    to the answer generator as a dedicated document context section — RAG
+    retrieval still runs on the user's original question (not the document text)
+    so embedding quality is preserved for regulation chunk matching.
+    """
     session_id, event_stream = get_agent_conversation_service().stream_chat(
         query=request.query,
         session_id=request.session_id,
         filters=request.filters,
         top_k=request.top_k or settings.rag_top_k,
+        context_text=request.context_text,
+        context_filename=request.context_filename,
     )
 
     async def generate() -> AsyncGenerator[str, None]:

backend/app/api/routes/status.py

@@ -1,18 +1,24 @@
 """Define API routes for status."""
 
+import asyncio
 import time
 from typing import Any
 
 from fastapi import APIRouter
 
 from app.config.settings import settings
+from app.domain.retrieval import RetrievedChunk
+from app.services.llm.llm_factory import get_llm_client, get_llm_factory
 from app.shared.bootstrap import (
     get_bm25_retriever,
     get_binary_store,
     get_conversation_store,
     get_document_query_service,
+    get_embedding_provider,
+    get_reranker,
     get_vector_index,
 )
+from app.shared.model_usage_tracker import get_model_usage_tracker
 
 router = APIRouter(prefix="/status", tags=["系统状态"])
 
@@ -23,6 +29,16 @@ _stats_cache: dict[str, Any] = {}
 _stats_cache_time: float = 0.0
 _STATS_TTL_SECONDS: float = 10.0
 
+# ---------------------------------------------------------------------------
+# AI model roles surfaced on the Status page (Task: System Status AI models)
+# ---------------------------------------------------------------------------
+_MODEL_ROLES: dict[str, str] = {
+    "main_llm": "主问答 LLM",
+    "hyde_llm": "HyDE 查询增强",
+    "embedding": "Embedding",
+    "reranker": "Reranker",
+}
+
 
 @router.get("/stats")
 async def get_stats():
@@ -111,3 +127,156 @@ async def get_health():
             "max": settings.session_max_sessions,
         },
     }
+
+
+def _normalize_llm_provider(raw_provider: str) -> str:
+    """Normalize a raw LLM_PROVIDER/HYDE_LLM_PROVIDER settings string to the
+    canonical LLMProvider enum value, the SAME way LLMFactory.create() does.
+
+    TrackedLLMClient.chat() (tracked_client.py) always records usage under
+    `self._inner.config.provider.value` — the NORMALIZED enum value produced by
+    LLMFactory._parse_provider() — never the raw string a caller passed to
+    get_llm_client(). Reusing that same normalization here (instead of
+    duplicating the alias table) guarantees the tracker key this route reads
+    always agrees with the key TrackedLLMClient wrote, even when the raw
+    settings value is a non-canonical alias (e.g. "deepseek-v3") or different
+    casing. Falls back to the raw string, unchanged, if it does not match any
+    known provider/alias, so this passive status endpoint still renders
+    (as "never_called") instead of raising on a misconfigured provider string.
+    """
+    try:
+        return get_llm_factory()._parse_provider(raw_provider).value
+    except ValueError:
+        return raw_provider
+
+
+def _resolve_role_provider_model(role: str) -> tuple[str, str]:
+    """Return the (provider, model) pair currently configured for one AI model role.
+
+    For "hyde_llm" this mirrors the exact fallback logic already used in
+    hyde_expander.py (settings.hyde_llm_provider or settings.llm_provider, same
+    for model) so tracker lookups here always match what TrackedLLMClient
+    recorded when HyDE actually ran.
+    """
+    if role == "main_llm":
+        return _normalize_llm_provider(settings.llm_provider), settings.llm_model
+    if role == "hyde_llm":
+        return (
+            _normalize_llm_provider(settings.hyde_llm_provider or settings.llm_provider),
+            settings.hyde_llm_model or settings.llm_model,
+        )
+    if role == "embedding":
+        return "embedding", settings.embedding_model
+    if role == "reranker":
+        return "reranker", settings.reranker_model
+    raise ValueError(f"unknown model role: {role}")  # pragma: no cover - internal roles are fixed
+
+
+def _build_model_status(role: str) -> dict[str, Any]:
+    """Build one /status/models row for the given role from tracker data + live settings."""
+    provider, model = _resolve_role_provider_model(role)
+    entry = get_model_usage_tracker().get(provider, model)
+
+    main_provider, main_model = _resolve_role_provider_model("main_llm")
+    shares_usage_with = (
+        "main_llm" if role != "main_llm" and (provider, model) == (main_provider, main_model) else None
+    )
+
+    enabled = True
+    status = entry.status if entry else "never_called"
+    if role == "reranker":
+        enabled = settings.reranker_enabled
+        if not enabled:
+            # Config always wins: report "disabled" even if the reranker was
+            # enabled and called successfully earlier in this process's life.
+            status = "disabled"
+    elif role == "hyde_llm":
+        enabled = settings.hyde_enabled
+        if not enabled:
+            # Same "config always wins" override as the reranker branch above:
+            # report "disabled" even if HyDE ran successfully before being
+            # turned off in settings during this process's life.
+            status = "disabled"
+
+    return {
+        "role": role,
+        "role_label": _MODEL_ROLES[role],
+        "provider": provider,
+        "model": model,
+        "enabled": enabled,
+        "status": status,
+        "total_tokens": entry.total_tokens if entry else 0,
+        "call_count_ok": entry.call_count_ok if entry else 0,
+        "call_count_error": entry.call_count_error if entry else 0,
+        "last_called_at": entry.last_called_at.isoformat() if entry and entry.last_called_at else None,
+        "last_latency_ms": entry.last_latency_ms if entry else None,
+        "last_error": entry.last_error if entry else None,
+        "shares_usage_with": shares_usage_with,
+    }
+
+
+@router.get("/models")
+async def get_model_statuses():
+    """Return connection status + cumulative token usage for all 4 tracked AI model roles.
+
+    Passive: reads tracker state + settings only, makes no outbound network calls.
+    """
+    return {"models": [_build_model_status(role) for role in _MODEL_ROLES]}
+
+
+async def _ping_main_or_hyde(role: str) -> None:
+    """Send one minimal chat completion to the LLM configured for `role`.
+
+    Skipped entirely for "hyde_llm" when settings.hyde_enabled is False,
+    mirroring _ping_reranker()'s disabled-skip pattern: when HyDE is turned
+    off (or reuses the main LLM, the default), issuing this ping would just be
+    a redundant duplicate chat call against the same model for no benefit.
+    "main_llm" is always pinged regardless of this check.
+    """
+    if role == "hyde_llm" and not settings.hyde_enabled:
+        return
+    provider, model = _resolve_role_provider_model(role)
+    try:
+        client = get_llm_client(provider=provider, model=model)
+    except Exception as exc:  # noqa: BLE001 - record, then re-raise so gather() still isolates this ping
+        # get_llm_client() can fail before any TrackedLLMClient exists to
+        # record the outcome itself (e.g. missing API key, unsupported
+        # provider string), so record the failure here directly, otherwise it
+        # would be invisible on the /status/models page afterward.
+        get_model_usage_tracker().record(provider=provider, model=model, success=False, error=str(exc))
+        raise
+    await asyncio.to_thread(client.chat, [{"role": "user", "content": "ping"}], max_tokens=1)
+
+
+async def _ping_embedding() -> None:
+    """Send one minimal embedding request."""
+    await asyncio.to_thread(get_embedding_provider().embed_query, "ping")
+
+
+async def _ping_reranker() -> None:
+    """Send one minimal rerank request, only when the reranker is enabled."""
+    reranker = get_reranker()
+    if reranker is None:
+        return
+    # Minimal single-chunk probe — real content doesn't matter, only round-trip success.
+    placeholder = RetrievedChunk(chunk_id="ping", doc_id="ping", doc_title="ping", text="ping", score=0.0)
+    await asyncio.to_thread(reranker.rerank, "ping", [placeholder], 1)
+
+
+@router.post("/models/ping")
+async def ping_model_connections():
+    """Actively test each configured model with a minimal request, then return fresh statuses.
+
+    Each ping is isolated with return_exceptions=True so one model timing out
+    or erroring does not prevent the other three from completing and being
+    reported. Failures are still visible afterwards via _build_model_status()
+    because the underlying clients record their own outcome into the tracker.
+    """
+    tasks = [
+        _ping_main_or_hyde("main_llm"),
+        _ping_main_or_hyde("hyde_llm"),
+        _ping_embedding(),
+        _ping_reranker(),
+    ]
+    await asyncio.gather(*tasks, return_exceptions=True)
+    return {"models": [_build_model_status(role) for role in _MODEL_ROLES]}

backend/app/application/agent/__init__.py

@@ -1,7 +1,13 @@
 """Initialize the app.application.agent package."""
 
 from .services import AgentConversationService, AgentSessionFeedbackResult, AgentSessionService
+from .agentic_service import AgenticConversationService
 # Keep package boundaries explicit so backend imports stay predictable.
 
 
-__all__ = ["AgentConversationService", "AgentSessionFeedbackResult", "AgentSessionService"]
+__all__ = [
+    "AgentConversationService",
+    "AgentSessionFeedbackResult",
+    "AgentSessionService",
+    "AgenticConversationService",
+]

backend/app/application/agent/agentic_service.py

@@ -0,0 +1,453 @@
+"""Implement the Agentic RAG pipeline for multi-step reasoning (P0-1).
+
+Architecture
+------------
+The pipeline adds four explicit reasoning steps before answer generation:
+
+  1. Intent Analysis  — classify query type (simple_qa / compare / multi_hop / ambiguous)
+  2. Query Planning   — for complex intents, decompose into focused sub-queries
+  3. Iterative Retrieval — retrieve for each sub-query, merge with deduplication
+  4. Grounding Check  — verify retrieved context is sufficient; refine query when not
+  5. Answer Generation — stream final answer with citations (reuses AnswerGenerator)
+
+Each step emits SSE ``thinking`` events so the frontend can render the live
+reasoning trace.  The pipeline is entirely synchronous and returns a generator so
+it plugs into the same ``iter_in_thread`` pattern used by the existing chat routes.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from typing import Generator
+
+from loguru import logger
+
+from app.application.knowledge import KnowledgeRetrievalService
+from app.application.agent.hyde_expander import HyDEExpander
+from app.config.settings import settings
+from app.domain.conversation import ConversationStore
+from app.domain.retrieval import RetrievedChunk
+from app.infrastructure.llm.openai_compatible_answer_generator import OpenAICompatibleAnswerGenerator
+from app.services.llm.llm_factory import get_llm_client
+
+# ── Prompts ───────────────────────────────────────────────────────────────────
+# Each prompt is kept module-level for easy review and fine-tuning.
+
+_INTENT_SYSTEM = (
+    "You are a query classifier for a Chinese regulatory compliance knowledge base.\n\n"
+    "Classify the query into exactly one of:\n"
+    '- "simple_qa"   : Single-hop, factual question about one regulation or clause\n'
+    '- "compare"     : Comparison between two or more regulations, standards, or versions\n'
+    '- "multi_hop"   : Requires chaining facts across multiple regulations to answer\n'
+    '- "ambiguous"   : Too vague or broad to retrieve effectively\n\n'
+    "Return ONLY valid JSON — no markdown, no extra text:\n"
+    '{"type": "...", "reason": "one sentence", "requires_decomposition": true/false}\n\n'
+    '"requires_decomposition" must be true for compare and multi_hop types.'
+)
+
+_PLAN_SYSTEM = (
+    "You are a query planner for a Chinese regulatory compliance knowledge base.\n\n"
+    "Decompose the query into 2-4 focused, self-contained sub-queries that together fully "
+    "address the original question.  Each sub-query must target one specific regulation, "
+    "clause, or concept and be independently searchable.\n\n"
+    "Return ONLY a valid JSON array — no markdown, no extra text:\n"
+    '["sub-query 1", "sub-query 2", ...]'
+)
+
+_GROUNDING_SYSTEM = (
+    "You are a grounding verifier for a regulatory compliance QA system.\n\n"
+    "Given a query and retrieved regulation passages, decide whether the passages contain "
+    "sufficient, accurate information to answer the query.\n\n"
+    "Return ONLY valid JSON — no markdown, no extra text:\n"
+    '{"sufficient": true/false, "confidence": 0.0-1.0, "reason": "one sentence", '
+    '"refined_query": "a more specific search query if not sufficient, else null"}'
+)
+
+
+# ── Result dataclasses ────────────────────────────────────────────────────────
+
+@dataclass
+class IntentResult:
+    """Capture the output of the intent-analysis step."""
+
+    type: str = "simple_qa"
+    reason: str = ""
+    requires_decomposition: bool = False
+
+
+@dataclass
+class GroundingResult:
+    """Capture the output of the grounding-check step."""
+
+    sufficient: bool = True
+    confidence: float = 1.0
+    reason: str = ""
+    refined_query: str | None = None
+
+
+# ── Service ───────────────────────────────────────────────────────────────────
+
+class AgenticConversationService:
+    """Multi-step Agentic RAG pipeline with live reasoning trace via SSE.
+
+    The service is intentionally synchronous so it can be wrapped in
+    ``iter_in_thread`` by the route layer without any async boilerplate.
+    """
+
+    def __init__(
+        self,
+        *,
+        retrieval_service: KnowledgeRetrievalService,
+        answer_generator: OpenAICompatibleAnswerGenerator,
+        conversation_store: ConversationStore,
+    ) -> None:
+        """Initialise with injected dependencies from the composition root."""
+        self.retrieval_service = retrieval_service
+        self.answer_generator = answer_generator
+        self.conversation_store = conversation_store
+        # HyDE expander is stateless — one instance shared for all requests.
+        self._hyde = HyDEExpander()
+
+    # ── Private helpers ───────────────────────────────────────────────────────
+
+    def _llm_json(
+        self,
+        system: str,
+        user: str,
+        provider: str | None,
+        model: str | None,
+        max_tokens: int = 300,
+    ) -> dict | list | None:
+        """Call the LLM with a JSON-only prompt and return the parsed result.
+
+        Returns ``None`` on any API or parse failure so callers can degrade
+        gracefully without raising.
+        """
+        client = get_llm_client(
+            provider=provider or settings.llm_provider,
+            model=model or settings.llm_model,
+        )
+        resp = client.chat(
+            [{"role": "system", "content": system}, {"role": "user", "content": user}],
+            max_tokens=max_tokens,
+            temperature=0.1,
+        )
+        if not resp.is_success:
+            logger.warning("AgenticService LLM call failed: {}", resp.error)
+            return None
+        try:
+            raw = resp.content.strip()
+            # Strip accidental markdown code fences the model may add.
+            if raw.startswith("```"):
+                parts = raw.split("```")
+                raw = parts[1] if len(parts) > 1 else raw
+                if raw.startswith("json"):
+                    raw = raw[4:]
+            return json.loads(raw.strip())
+        except (json.JSONDecodeError, IndexError) as exc:
+            logger.debug("AgenticService JSON parse failed: {} | raw={}", exc, resp.content[:200])
+            return None
+
+    def _analyze_intent(
+        self, query: str, provider: str | None, model: str | None
+    ) -> IntentResult:
+        """Classify query intent to select the appropriate retrieval strategy."""
+        data = self._llm_json(
+            _INTENT_SYSTEM,
+            f"Query: {query}",
+            provider,
+            model,
+            max_tokens=settings.agentic_intent_max_tokens,
+        )
+        if isinstance(data, dict):
+            return IntentResult(
+                type=str(data.get("type", "simple_qa")),
+                reason=str(data.get("reason", "")),
+                requires_decomposition=bool(data.get("requires_decomposition", False)),
+            )
+        return IntentResult(type="simple_qa", reason="fallback — classifier returned no JSON", requires_decomposition=False)
+
+    def _plan_queries(
+        self, query: str, intent_type: str, provider: str | None, model: str | None
+    ) -> list[str]:
+        """Decompose a complex query into focused, independently-retrievable sub-queries."""
+        data = self._llm_json(
+            _PLAN_SYSTEM,
+            f"Original query ({intent_type}): {query}",
+            provider,
+            model,
+            max_tokens=settings.agentic_plan_max_tokens,
+        )
+        if isinstance(data, list) and data:
+            # Cap at configured maximum to keep latency predictable.
+            return [str(q) for q in data[:settings.agentic_max_sub_queries] if q]
+        return [query]
+
+    def _check_grounding(
+        self,
+        query: str,
+        chunks: list[RetrievedChunk],
+        provider: str | None,
+        model: str | None,
+    ) -> GroundingResult:
+        """Verify whether retrieved chunks are sufficient to ground an accurate answer.
+
+        Uses a fast score-threshold heuristic first; falls back to an LLM call only
+        when scores are borderline so that the happy-path adds no extra latency.
+        """
+        if not chunks:
+            return GroundingResult(
+                sufficient=False,
+                confidence=0.0,
+                reason="未检索到相关内容",
+                refined_query=None,
+            )
+
+        avg_score = sum(c.score for c in chunks) / len(chunks)
+        # Fast path: high-confidence retrieval → skip extra LLM call.
+        if avg_score > settings.agentic_grounding_threshold and len(chunks) >= 3:
+            return GroundingResult(
+                sufficient=True,
+                confidence=round(avg_score, 3),
+                reason="检索置信度充足，无需二次查询",
+                refined_query=None,
+            )
+
+        # LLM-based grounding check for borderline retrievals.
+        context_preview = "\n".join(
+            f"[{i + 1}] (score={c.score:.2f}) {c.text[:200]}" for i, c in enumerate(chunks[:5])
+        )
+        data = self._llm_json(
+            _GROUNDING_SYSTEM,
+            f"Query: {query}\n\nRetrieved passages:\n{context_preview}",
+            provider,
+            model,
+            max_tokens=settings.agentic_grounding_max_tokens,
+        )
+        if isinstance(data, dict):
+            return GroundingResult(
+                sufficient=bool(data.get("sufficient", True)),
+                confidence=float(data.get("confidence", 0.5)),
+                reason=str(data.get("reason", "")),
+                refined_query=data.get("refined_query") or None,
+            )
+        return GroundingResult(sufficient=True, confidence=0.5, reason="grounding check skipped (parse error)", refined_query=None)
+
+    @staticmethod
+    def _intent_to_template(intent_type: str) -> str:
+        """Map an intent type to the best prompt template name for answer generation."""
+        mapping = {
+            "compare": "comparison",
+            "multi_hop": "compliance_qa",
+            "simple_qa": "compliance_qa",
+            "ambiguous": "compliance_qa",
+        }
+        return mapping.get(intent_type, "compliance_qa")
+
+    @staticmethod
+    def _deduplicate(chunks: list[RetrievedChunk], max_chunks: int) -> list[RetrievedChunk]:
+        """Remove duplicate chunk IDs, preserving first-occurrence order up to max_chunks."""
+        seen: set[str] = set()
+        result: list[RetrievedChunk] = []
+        for chunk in chunks:
+            if chunk.chunk_id not in seen:
+                seen.add(chunk.chunk_id)
+                result.append(chunk)
+                if len(result) >= max_chunks:
+                    break
+        return result
+
+    # ── Public interface ──────────────────────────────────────────────────────
+
+    def stream_agentic_chat(
+        self,
+        *,
+        query: str,
+        session_id: str | None = None,
+        filters: str | None = None,
+        provider: str | None = None,
+        model: str | None = None,
+        top_k: int = 5,
+        context_text: str | None = None,
+        context_filename: str | None = None,
+    ) -> tuple[str, Generator[dict, None, None]]:
+        """Run the full Agentic RAG pipeline and return ``(session_id, event_generator)``.
+
+        When context_text is provided (user-attached document) it is:
+        - Summarised and prepended to the intent-analysis prompt so the classifier
+          understands what kind of question is being asked.
+        - Treated as baseline grounding so the pipeline skips unnecessary retries
+          when the document itself is the primary source.
+        - Passed to the answer generator so the LLM sees the full document alongside
+          retrieved regulation chunks.
+
+        The generator yields SSE event dicts compatible with the route's
+        ``iter_in_thread`` pattern.
+        """
+        session = self.conversation_store.get_session(session_id) if session_id else None
+        if session is None:
+            session = self.conversation_store.create_session()
+        self.conversation_store.save_message(session.session_id, role="user", content=query)
+        history = [{"role": msg.role, "content": msg.content} for msg in session.messages[-10:]]
+        active_session_id = session.session_id
+
+        # Build a brief document summary for classifier/planner prompts (avoid
+        # passing the full text which could overwhelm small-context LLMs).
+        _doc_summary: str = ""
+        if context_text and context_text.strip():
+            _doc_label = context_filename or "document"
+            _preview = context_text.strip()[:400]
+            _doc_summary = f"[User has attached document: {_doc_label}]\nDocument preview: {_preview}…\n\n"
+
+        def event_stream() -> Generator[dict, None, None]:
+            """Execute all pipeline steps and yield SSE events."""
+            # ── Step 1: Intent Analysis ──────────────────────────────────────
+            yield {"event": "thinking", "data": {"step": "intent_analysis", "status": "running"}}
+            # Prepend doc summary so the classifier knows what the user is asking about
+            intent_user_msg = f"{_doc_summary}Query: {query}" if _doc_summary else f"Query: {query}"
+            data = self._llm_json(
+                _INTENT_SYSTEM, intent_user_msg, provider, model,
+                max_tokens=settings.agentic_intent_max_tokens,
+            )
+            if isinstance(data, dict):
+                intent = IntentResult(
+                    type=str(data.get("type", "simple_qa")),
+                    reason=str(data.get("reason", "")),
+                    requires_decomposition=bool(data.get("requires_decomposition", False)),
+                )
+            else:
+                intent = IntentResult(type="simple_qa", reason="fallback", requires_decomposition=False)
+            logger.debug("Agentic intent: type={} decompose={}", intent.type, intent.requires_decomposition)
+            yield {
+                "event": "thinking",
+                "data": {
+                    "step": "intent_analysis",
+                    "status": "done",
+                    "intent_type": intent.type,
+                    "reason": intent.reason,
+                    "requires_decomposition": intent.requires_decomposition,
+                },
+            }
+
+            # ── Step 2: Query Planning ───────────────────────────────────────
+            sub_queries: list[str] = [query]
+            if intent.requires_decomposition:
+                yield {"event": "thinking", "data": {"step": "query_planning", "status": "running"}}
+                plan_user_msg = f"{_doc_summary}Original query ({intent.type}): {query}" if _doc_summary else f"Original query ({intent.type}): {query}"
+                data_plan = self._llm_json(
+                    _PLAN_SYSTEM, plan_user_msg, provider, model,
+                    max_tokens=settings.agentic_plan_max_tokens,
+                )
+                if isinstance(data_plan, list) and data_plan:
+                    sub_queries = [str(q) for q in data_plan[:settings.agentic_max_sub_queries] if q]
+                logger.debug("Agentic sub-queries ({}): {}", len(sub_queries), sub_queries)
+                yield {
+                    "event": "thinking",
+                    "data": {"step": "query_planning", "status": "done", "sub_queries": sub_queries},
+                }
+
+            # ── Step 3: Iterative Retrieval ──────────────────────────────────
+            # Always retrieve using the user's original question (NOT the document
+            # text) so embedding quality is preserved for regulation matching.
+            # HyDE enriches the retrieval query with a short hypothetical answer
+            # to close the vocabulary gap between terse queries and long documents.
+            candidate_k = max(top_k * 3, 15)
+            all_chunks: list[RetrievedChunk] = []
+
+            # For simple_qa with a single query, HyDE gives the biggest benefit
+            # (bridging vague/colloquial questions to formal document language).
+            # For compare/multi_hop, the planner already decomposed into precise
+            # sub-queries, so HyDE is less critical but still applied per sub-query.
+            for idx, sq in enumerate(sub_queries, start=1):
+                yield {
+                    "event": "thinking",
+                    "data": {"step": "retrieving", "status": "running", "query": sq, "index": idx, "total": len(sub_queries)},
+                }
+                # HyDE expansion: generate hypothetical answer, embed it for retrieval.
+                # Falls back to original sub-query if LLM call fails.
+                retrieval_query = self._hyde.expand(sq)
+                chunks = self.retrieval_service.retrieve(query=retrieval_query, top_k=candidate_k, filters=filters)
+                all_chunks.extend(chunks)
+                yield {
+                    "event": "thinking",
+                    "data": {"step": "retrieving", "status": "done", "query": sq, "index": idx, "total": len(sub_queries), "found": len(chunks)},
+                }
+
+            unique_chunks = self._deduplicate(all_chunks, max_chunks=top_k * 4)
+
+            # ── Step 4: Grounding Check ──────────────────────────────────────
+            yield {"event": "thinking", "data": {"step": "grounding_check", "status": "running"}}
+
+            # When the user has attached a document, the document itself provides
+            # baseline grounding — skip the re-query loop to avoid the LLM asking
+            # "please provide the document text" as a refined query.
+            if context_text and context_text.strip():
+                grounding = GroundingResult(
+                    sufficient=True,
+                    confidence=0.95,
+                    reason="用户已附件上传文档，以文档内容为基础作答",
+                    refined_query=None,
+                )
+            else:
+                grounding = self._check_grounding(query, unique_chunks, provider, model)
+
+            yield {
+                "event": "thinking",
+                "data": {
+                    "step": "grounding_check",
+                    "status": "done",
+                    "sufficient": grounding.sufficient,
+                    "confidence": grounding.confidence,
+                    "reason": grounding.reason,
+                },
+            }
+
+            # Only retry from vector store when no document is attached and grounding failed
+            if not grounding.sufficient and grounding.refined_query and not context_text:
+                logger.info("Grounding insufficient — re-querying: {}", grounding.refined_query)
+                yield {
+                    "event": "thinking",
+                    "data": {"step": "retrieving", "status": "running", "query": grounding.refined_query, "index": 1, "total": 1, "retry": True},
+                }
+                # Apply HyDE to the refined query as well for better retrieval.
+                refined_hyde_query = self._hyde.expand(grounding.refined_query)
+                refined_chunks = self.retrieval_service.retrieve(query=refined_hyde_query, top_k=candidate_k, filters=filters)
+                all_chunks.extend(refined_chunks)
+                unique_chunks = self._deduplicate(all_chunks, max_chunks=top_k * 4)
+                yield {
+                    "event": "thinking",
+                    "data": {"step": "retrieving", "status": "done", "query": grounding.refined_query, "index": 1, "total": 1, "found": len(refined_chunks), "retry": True},
+                }
+
+            final_chunks = unique_chunks[:top_k]
+
+            # ── Step 5: Answer Generation ────────────────────────────────────
+            sources_payload = [s.__dict__ for s in self.answer_generator._sources(final_chunks)]
+            yield {"event": "sources", "data": sources_payload}
+
+            answer_parts: list[str] = []
+            for event in self.answer_generator.stream_generate(
+                query=query,
+                retrieved_chunks=final_chunks,
+                history=history,
+                provider=provider,
+                model=model,
+                prompt_template=self._intent_to_template(intent.type),
+                context_text=context_text,
+                context_filename=context_filename,
+            ):
+                if event.get("event") == "content":
+                    answer_parts.append(str(event.get("data", "")))
+                yield event
+
+            full_answer = "".join(answer_parts)
+            self.conversation_store.save_message(
+                active_session_id,
+                role="assistant",
+                content=full_answer,
+                sources=sources_payload,
+            )
+
+        return active_session_id, event_stream()
+

backend/app/application/agent/hyde_expander.py

@@ -0,0 +1,105 @@
+"""Implement HyDE (Hypothetical Document Embeddings) query expansion.
+
+HyDE improves dense retrieval by addressing the vocabulary gap between
+short user queries and longer document passages:
+
+  User query  →  [LLM generates hypothetical answer]
+                         ↓
+             embed hypothetical answer (not original query)
+                         ↓
+             retrieve similar real passages from Milvus
+
+The hypothetical answer uses the same vocabulary and phrasing as documents,
+so its embedding is much closer to relevant chunks than a terse query embedding.
+
+Usage:
+    expander = HyDEExpander()
+    retrieval_query = expander.expand(query, provider=..., model=...)
+    chunks = retrieval_service.retrieve(query=retrieval_query, ...)
+
+When the LLM call fails, expand() falls back to the original query so the
+retrieval pipeline degrades gracefully.
+
+References:
+    Gao et al. (2022), "Precise Zero-Shot Dense Retrieval without Relevance Labels"
+    https://arxiv.org/abs/2212.10496
+"""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from app.config.settings import settings
+from app.services.llm.llm_factory import get_llm_client
+
+# Maximum chars to trim from the hypothetical answer to avoid token overrun.
+_MAX_HYPOTHESIS_CHARS = 600
+
+# System prompt that instructs the LLM to write a passage *as if* it were
+# from a regulatory document, not a conversation answer.
+_HYDE_SYSTEM = (
+    "你是一位法规知识库专家。用户提出了一个问题，"
+    "请用50-120字写一段话，模拟如果相关法规文档中存在完美答案，"
+    "该段落会是什么内容。\n\n"
+    "要求：\n"
+    "- 使用与法规文档相同的正式书面语气\n"
+    "- 包含可能的条款编号、标准名称等关键术语\n"
+    "- 不要解释你在做什么，直接输出假设性段落\n"
+    "- 如问题过于模糊，写一段合理的通用法规说明"
+)
+
+
+class HyDEExpander:
+    """Generate a hypothetical document passage to improve dense retrieval.
+
+    The expander is stateless — instantiate once and call expand() per query.
+    It requires no external dependencies beyond the project's existing LLM
+    client infrastructure.
+    """
+
+    def expand(self, query: str) -> str:
+        """Return a combined retrieval query: original query + hypothetical passage.
+
+        The combination ensures:
+        - Dense retrieval uses the enriched hypothetical text (semantic match).
+        - BM25 retrieval still benefits from the original query keywords.
+
+        The model used is ``settings.hyde_llm_model`` (dedicated lightweight model)
+        falling back to the main ``settings.llm_model`` when not configured.
+
+        If the LLM call fails for any reason, returns the original query unchanged.
+        """
+        if not settings.hyde_enabled:
+            return query
+
+        # Use the dedicated HyDE model when configured; fall back to main LLM.
+        # A lightweight model (e.g. qwen3.5-flash) is sufficient for generating
+        # a short hypothetical passage and significantly reduces cost + latency.
+        provider = settings.hyde_llm_provider or settings.llm_provider
+        model = settings.hyde_llm_model or settings.llm_model
+
+        try:
+            client = get_llm_client(provider=provider, model=model)
+            resp = client.chat(
+                messages=[
+                    {"role": "system", "content": _HYDE_SYSTEM},
+                    {"role": "user", "content": f"问题：{query}"},
+                ],
+                max_tokens=settings.hyde_max_tokens,
+                # Low temperature: we want a plausible, deterministic passage.
+                temperature=0.3,
+            )
+            if not resp.is_success or not resp.content:
+                logger.debug("HyDE LLM call failed or empty — using original query")
+                return query
+
+            hypothesis = resp.content.strip()[:_MAX_HYPOTHESIS_CHARS]
+            logger.debug("HyDE expanded query ({}→{} chars)", len(query), len(hypothesis))
+
+            # Concatenate: the embedding model will see the full combined text,
+            # so the resulting vector leans toward the hypothetical document style.
+            return f"{query}\n\n{hypothesis}"
+
+        except Exception as exc:  # noqa: BLE001 — intentional broad catch for graceful fallback
+            logger.warning("HyDE expansion failed: {} — using original query", exc)
+            return query

backend/app/application/agent/services.py

@@ -9,6 +9,7 @@ from app.domain.conversation import AnswerGenerator, AnswerResult, ConversationS
 from app.domain.retrieval import RetrievedChunk
 
 from app.application.knowledge import KnowledgeRetrievalService
+from app.application.agent.hyde_expander import HyDEExpander
 # Keep orchestration logic centralized so use-case flow stays easy to trace.
 
 
@@ -26,6 +27,8 @@ class AgentConversationService:
         self.retrieval_service = retrieval_service
         self.answer_generator = answer_generator
         self.conversation_store = conversation_store
+        # Shared HyDE expander — stateless, safe for reuse across requests.
+        self._hyde = HyDEExpander()
 
     def ask(
         self,
@@ -108,14 +111,26 @@ class AgentConversationService:
         model: str | None = None,
         top_k: int = 5,
         prompt_template: str | None = None,
+        context_text: str | None = None,
+        context_filename: str | None = None,
     ) -> tuple[str, Generator[dict, None, None]]:
-        """Stream chat for the Agent Conversation Service instance."""
+        """Stream chat for the Agent Conversation Service instance.
+
+        When context_text is provided the user's document is passed directly to
+        the answer generator — RAG retrieval still runs on the user's question
+        (not the document text) to find relevant regulation passages.
+        """
         session = self.conversation_store.get_session(session_id) if session_id else None
         if session is None:
             session = self.conversation_store.create_session()
         self.conversation_store.save_message(session.session_id, role="user", content=query)
         history = [{"role": msg.role, "content": msg.content} for msg in session.messages[-10:]]
-        retrieved = self.retrieval_service.retrieve(query=query, top_k=top_k, filters=filters)
+        # HyDE: expand the query with a hypothetical answer to improve dense retrieval.
+        # For document-context queries, skip HyDE since the document itself guides retrieval.
+        retrieval_query = self._hyde.expand(query) if not context_text else query
+        # Retrieve using the enriched query — NOT the document text —
+        # so embedding quality is preserved for regulation chunk matching.
+        retrieved = self.retrieval_service.retrieve(query=retrieval_query, top_k=top_k, filters=filters)
 
         def event_stream() -> Generator[dict, None, None]:
             """Handle event stream for the Agent Conversation Service instance."""
@@ -129,6 +144,8 @@ class AgentConversationService:
                 provider=provider,
                 model=model,
                 prompt_template=prompt_template,
+                context_text=context_text,
+                context_filename=context_filename,
             ):
                 if event.get("event") == "sources":
                     sources_payload = event.get("data", [])
@@ -189,3 +206,4 @@ class AgentSessionService:
             raise ValueError("消息索引不存在")
         # Preserve the existing API behavior until a persistent feedback store is introduced.
         return AgentSessionFeedbackResult(session_id=session_id, message_index=message_index)
+

backend/app/application/compliance/pipeline.py

@@ -5,6 +5,7 @@ All functions are synchronous — call them via asyncio.to_thread() in async SSE
 
 from __future__ import annotations
 
+import asyncio
 import json
 import os
 import re
@@ -12,10 +13,20 @@ import tempfile
 from typing import TYPE_CHECKING
 
 from loguru import logger
+from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential
+
+# Shared retry policy for LLM calls: 3 attempts, exponential back-off 1–4 s.
+_llm_retry = retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=1, max=4),
+    retry=retry_if_exception_type((ValueError, TimeoutError, ConnectionError)),
+    reraise=True,
+)
 
 if TYPE_CHECKING:
     from app.application.knowledge import KnowledgeRetrievalService
     from app.domain.retrieval import RetrievedChunk
+    from app.domain.compliance.ports import AnalysisRecord, FindingRecord
     from app.services.llm.base_client import BaseLLMClient
 
 
@@ -40,19 +51,36 @@ def _extract_json(text: str):
 
 
 def extract_text_from_doc_id(doc_id: str) -> str:
+    """Fetch the full text of a document by retrieving its chunks filtered by doc_id.
+
+    Uses a high top_k and doc_id filter to reconstruct the document in chunk order,
+    avoiding the previous approach of semantic search by doc_name which could return
+    chunks from unrelated documents.
+    """
     from app.shared.bootstrap import get_document_query_service, get_retrieval_service
     doc = get_document_query_service().get(doc_id)
     if not doc:
         raise ValueError(f"Document '{doc_id}' not found")
     service = get_retrieval_service()
-    chunks = service.retrieve(query=doc.doc_name, top_k=30)
+    # Use doc_name as a broad query, filter strictly by doc_id so we only get
-    doc_chunks = [c for c in chunks if c.doc_id == doc_id]
+    # this document's chunks; top_k=100 covers most real-world documents.
+    chunks = service.retrieve(query=doc.doc_name, top_k=100, filters=doc_id)
+    doc_chunks = [c for c in chunks if getattr(c, "doc_id", None) == doc_id]
     if not doc_chunks:
-        doc_chunks = chunks[:15]
+        # Fallback: use top results even without doc_id match (e.g., legacy store)
-    return "\n\n".join(c.text for c in doc_chunks[:15])
+        doc_chunks = chunks[:30]
+    # Sort by chunk_index to preserve document reading order
+    doc_chunks.sort(key=lambda c: getattr(c, "chunk_index", 0))
+    return "\n\n".join(c.text for c in doc_chunks[:40])
 
 
 def extract_text_from_file(content: bytes, filename: str) -> str:
+    """Parse an uploaded file and return its full text content.
+
+    Removed previous 4000-char cap so large specifications and standards are
+    fully analysed. The caller is responsible for splitting the text into
+    clause-sized chunks before passing to the LLM.
+    """
     from app.shared.bootstrap import get_document_command_service
     suffix = os.path.splitext(filename or "doc.pdf")[1] or ".pdf"
     tmp_path = ""
@@ -63,10 +91,11 @@ def extract_text_from_file(content: bytes, filename: str) -> str:
         service = get_document_command_service()
         parsed = service.parser.parse(file_path=tmp_path, doc_id="tmp_analysis", doc_name=filename)
         if parsed.raw_text:
-            return parsed.raw_text[:4000]
+            # Return full text — truncation happens in split_into_clauses()
+            return parsed.raw_text
         return "\n".join(
-            b.get("text", "") for b in parsed.semantic_blocks[:30] if b.get("text")
+            b.get("text", "") for b in parsed.semantic_blocks if b.get("text")
-        )[:4000]
+        )
     except Exception as exc:
         logger.warning("File text extraction failed: {}", exc)
         return ""
@@ -77,27 +106,68 @@ def extract_text_from_file(content: bytes, filename: str) -> str:
 
 
 def split_into_clauses(text: str, client: "BaseLLMClient") -> list[str]:
+    """Split a compliance document into semantically independent clauses.
+
+    For long texts (> 2 000 chars) the document is processed in overlapping
+    2 000-char windows so no content is missed. Each window produces up to 4
+    clauses; results are deduplicated and capped at 12 total to keep analysis
+    latency reasonable.
+    """
+    # Window size and step for sliding-window clause extraction
+    _WINDOW = 2000
+    _STEP = 1800  # 200-char overlap to avoid cutting clauses at boundaries
+    _MAX_CLAUSES = 12
+
+    windows = []
+    if len(text) <= _WINDOW:
+        windows = [text]
+    else:
+        pos = 0
+        while pos < len(text):
+            windows.append(text[pos: pos + _WINDOW])
+            pos += _STEP
+
+    all_clauses: list[str] = []
+    for window in windows:
         prompt = (
-        "You are a compliance analysis expert. Split the following text into 3-8 "
+            "You are a compliance analysis expert. Split the following text into "
-        "semantically complete compliance clauses. Each clause should be an independent "
+            "3-4 semantically complete compliance clauses. Each clause must be an "
-        "compliance requirement or technical statement.\n"
+            "independent requirement or technical statement. Omit section headings, "
+            "definitions, and non-normative text.\n"
             "Return as JSON array of strings, e.g.:\n"
             '["Clause one...", "Clause two..."]\n'
             "Return ONLY the JSON array.\n\n"
-        f"Text:\n{text[:2000]}"
+            f"Text:\n{window}"
         )
-    response = client.chat([{"role": "user", "content": prompt}], max_tokens=1000)
+        response = client.chat([{"role": "user", "content": prompt}], max_tokens=800)
         if response.is_success:
             try:
                 result = _extract_json(response.content)
                 if isinstance(result, list):
                     clauses = [str(c).strip() for c in result if str(c).strip()]
-                if clauses:
+                    all_clauses.extend(clauses[:4])
-                    return clauses[:8]
             except (ValueError, TypeError):
-            logger.warning("Clause split JSON parse failed, using fallback")
+                logger.warning("Clause split JSON parse failed for window, using sentence fallback")
-    sentences = re.split(r"[.?!;\n]+", text)
+                sentences = re.split(r"[.?!;\n]+", window)
-    return [s.strip() for s in sentences if len(s.strip()) > 20][:6]
+                all_clauses.extend(s.strip() for s in sentences if len(s.strip()) > 20)
+        else:
+            # LLM unavailable — fall back to sentence splitting for this window
+            sentences = re.split(r"[.?!;\n]+", window)
+            all_clauses.extend(s.strip() for s in sentences if len(s.strip()) > 20)
+
+        if len(all_clauses) >= _MAX_CLAUSES:
+            break
+
+    # Deduplicate near-duplicates (same first 80 chars) that span window boundaries
+    seen: set[str] = set()
+    deduped: list[str] = []
+    for c in all_clauses:
+        key = c[:80].lower()
+        if key not in seen:
+            seen.add(key)
+            deduped.append(c)
+
+    return deduped[:_MAX_CLAUSES]
 
 
 def retrieve_for_clause(
@@ -106,7 +176,130 @@ def retrieve_for_clause(
     top_k: int = 5,
     domains: str | None = None,
 ) -> list["RetrievedChunk"]:
-    return retrieval_service.retrieve(query=clause, top_k=top_k, filters=domains)
+    """Retrieve regulation chunks relevant to a clause.
+
+    If the best retrieval score is below 0.55, rewrite the clause into a more
+    technical query and retry once to improve coverage.
+    """
+    chunks = retrieval_service.retrieve(query=clause, top_k=top_k, filters=domains)
+    if not chunks:
+        return chunks
+
+    best_score = max((getattr(c, "score", 0) for c in chunks), default=0)
+    if best_score < 0.55:
+        # Rewrite clause as technical keyword query and retry
+        keywords = " ".join(
+            w for w in re.split(r"\W+", clause) if len(w) > 3
+        )[:200]
+        retry_chunks = retrieval_service.retrieve(query=keywords, top_k=top_k, filters=domains)
+        if retry_chunks:
+            # Merge: keep unique chunks, prefer higher-score version
+            seen_ids: set[str] = {getattr(c, "chunk_id", str(i)) for i, c in enumerate(chunks)}
+            for rc in retry_chunks:
+                rid = getattr(rc, "chunk_id", "")
+                if rid not in seen_ids:
+                    chunks.append(rc)
+                    seen_ids.add(rid)
+            chunks.sort(key=lambda c: getattr(c, "score", 0), reverse=True)
+            chunks = chunks[:top_k]
+    return chunks
+
+
+def process_single_clause(
+    clause: str,
+    index: int,
+    retrieval_service: "KnowledgeRetrievalService",
+    client: "BaseLLMClient",
+    top_k: int = 5,
+    domains: str | None = None,
+) -> dict:
+    """Process one clause: retrieve relevant regulations then check compliance.
+
+    Returns a dict with keys:
+      - index: clause position (for ordering)
+      - chunks: list of RetrievedChunk (for source events)
+      - finding: dict with title/desc/status/clause_ref/confidence (may be None on LLM failure)
+
+    Designed to run inside asyncio.to_thread() for parallel execution.
+    The finding now includes a 'source_refs' list linking back to the chunks
+    that informed the verdict, enabling the frontend to correlate sources with findings.
+    """
+    chunks = retrieve_for_clause(clause, retrieval_service, top_k, domains)
+    finding = check_clause_compliance(clause, chunks, client)
+    if finding is not None:
+        # Attach source references so the frontend can link finding ↔ sources
+        finding["source_refs"] = [
+            {
+                "standard": getattr(c, "doc_title", "") or getattr(c, "doc_name", ""),
+                "clause": getattr(c, "section_title", "") or "",
+                "score": round(float(getattr(c, "score", 0)), 3),
+            }
+            for c in chunks[:3]
+        ]
+    return {"index": index, "chunks": chunks, "finding": finding}
+
+
+async def run_clauses_streaming(
+    clauses: list[str],
+    retrieval_service: "KnowledgeRetrievalService",
+    client: "BaseLLMClient",
+    top_k: int = 5,
+    domains: str | None = None,
+):
+    """Process all clauses concurrently and yield each result as it completes.
+
+    Unlike the old gather()-based approach, this uses asyncio.Queue so that
+    findings are emitted to the SSE stream immediately when each clause
+    finishes — the user sees results progressively rather than waiting for
+    the slowest clause before seeing any output.
+
+    Yields dicts with keys: index, chunks, finding (same schema as
+    process_single_clause, plus a sentinel {"_done": True} at the end).
+    """
+    queue: asyncio.Queue[dict] = asyncio.Queue()
+    total = len(clauses)
+
+    async def _worker(clause: str, i: int) -> None:
+        """Run one clause in a thread and push the result into the queue."""
+        try:
+            result = await asyncio.to_thread(
+                process_single_clause,
+                clause, i, retrieval_service, client, top_k, domains,
+            )
+        except Exception as exc:
+            logger.warning("Clause {} processing failed: {}", i, exc)
+            result = {"index": i, "chunks": [], "finding": None}
+        await queue.put(result)
+
+    # Launch all workers concurrently
+    tasks = [asyncio.create_task(_worker(clause, i)) for i, clause in enumerate(clauses)]
+
+    received = 0
+    while received < total:
+        result = await queue.get()
+        yield result
+        received += 1
+
+    # Wait for all tasks to complete (they should already be done by now)
+    await asyncio.gather(*tasks, return_exceptions=True)
+
+
+async def run_clauses_parallel(
+    clauses: list[str],
+    retrieval_service: "KnowledgeRetrievalService",
+    client: "BaseLLMClient",
+    top_k: int = 5,
+    domains: str | None = None,
+) -> list[dict]:
+    """Legacy batch API kept for backward compatibility.
+
+    Collects all streaming results and returns them sorted by clause index.
+    New code should use run_clauses_streaming() directly.
+    """
+    results: list[dict] = []
+    async for result in run_clauses_streaming(clauses, retrieval_service, client, top_k, domains):
+        results.append(result)
+    return sorted(results, key=lambda r: r["index"])
 
 
 def check_clause_compliance(
@@ -114,30 +307,50 @@ def check_clause_compliance(
     chunks: list["RetrievedChunk"],
     client: "BaseLLMClient",
 ) -> dict | None:
-    if not chunks:
+    """Check whether a business clause complies with the retrieved regulations.
-        return None
+
+    The prompt explicitly instructs the LLM to:
+    - extract clause_ref from the retrieved text (not invent it)
+    - include a confidence score (0-1) reflecting how well the retrieved
+      chunks cover the clause topic
+
+    Returns None only when the LLM call fails after all retries.
+    """
     reg_context = "\n".join(
         f"[{i+1}] {c.doc_title} {c.section_title or ''}: {c.text[:300]}"
         for i, c in enumerate(chunks[:5])
-    )
+    ) if chunks else "(no regulatory context retrieved)"
     prompt = (
         "You are a compliance expert. Judge whether the following business clause "
         "complies with the retrieved regulations.\n\n"
         f"Business clause:\n{clause}\n\n"
         f"Retrieved regulations:\n{reg_context}\n\n"
-        "Return JSON:\n"
+        "Return JSON with these exact fields:\n"
         "{\n"
         '  "status": "ok" | "warn" | "risk",\n'
         '  "title": "Short finding title (max 30 chars)",\n'
         '  "desc": "Description (50-120 chars)",\n'
-        '  "clause_ref": "Regulation clause reference e.g. Art.9.1 or Sec.3.1"\n'
+        '  "clause_ref": "Exact clause/article reference copied from the retrieved text above, '
+        'e.g. Art.9.1 or Sec.3.1. Use null if no specific clause number appears in the retrieved text.",\n'
+        '  "confidence": 0.0-1.0  // how well the retrieved context covers this clause topic\n'
         "}\n"
         "status: ok=compliant, warn=gap exists, risk=critical/missing\n"
+        "IMPORTANT: copy clause_ref verbatim from the retrieved text; do NOT invent references.\n"
         "Return ONLY the JSON object."
     )
-    response = client.chat([{"role": "user", "content": prompt}], max_tokens=500)
+
-    if not response.is_success:
+    def _do_check():
+        resp = client.chat([{"role": "user", "content": prompt}], max_tokens=500)
+        if not resp.is_success:
+            raise ValueError("LLM returned non-success for gap check")
+        return resp
+
+    try:
+        response = _llm_retry(_do_check)()
+    except Exception as exc:
+        logger.warning("check_clause_compliance LLM call failed after retries: {}", exc)
         return None
+
     try:
         result = _extract_json(response.content)
         if isinstance(result, dict) and "status" in result:
@@ -145,7 +358,10 @@ def check_clause_compliance(
                 "title": str(result.get("title", "Compliance finding")),
                 "desc": str(result.get("desc", "")),
                 "status": result.get("status", "info"),
-                "clause_ref": result.get("clause_ref"),
+                # None if LLM correctly found no clause number in retrieved text
+                "clause_ref": result.get("clause_ref") or None,
+                # Confidence score helps frontend show retrieval quality indicator
+                "confidence": float(result.get("confidence", 0.5)),
             }
     except (ValueError, TypeError) as exc:
         logger.warning("Gap check JSON parse failed: {}", exc)
@@ -182,12 +398,11 @@ def synthesize_conclusion(
         '    {"label": "Priority", "value": "High/Medium/Low", "risk": true}\n'
         '  ],\n'
         '  "risk_score": 0-100 (integer, higher=riskier),\n'
-        '  "highlight_terms": ["Key terms to highlight, max 10 terms"],\n'
+        '  "highlight_terms": ["term1", "term2"],  // up to 10 key technical/legal terms actually present in the text\n'
         '  "para_text": "Original text or summary (max 600 chars)"\n'
         "}\n"
         "Return ONLY the JSON object."
     )
-    response = client.chat([{"role": "user", "content": prompt}], max_tokens=1200)
     fallback = {
         "conclusion": "Compliance analysis complete. Review findings and create remediation plan.",
         "actions": [
@@ -198,8 +413,19 @@ def synthesize_conclusion(
         "highlight_terms": [],
         "para_text": para_text[:800],
     }
-    if not response.is_success:
+
+    def _do_synthesize():
+        resp = client.chat([{"role": "user", "content": prompt}], max_tokens=1200)
+        if not resp.is_success:
+            raise ValueError("LLM returned non-success for synthesis")
+        return resp
+
+    try:
+        response = _llm_retry(_do_synthesize)()
+    except Exception as exc:
+        logger.warning("synthesize_conclusion LLM call failed after retries: {}", exc)
         return fallback
+
     try:
         result = _extract_json(response.content)
         if isinstance(result, dict):
@@ -213,3 +439,132 @@ def synthesize_conclusion(
     except (ValueError, TypeError) as exc:
         logger.warning("Conclusion synthesis JSON parse failed: {}", exc)
     return fallback
+
+
+_SUGGESTION_FOCUS = {
+    "risk": "Focus on remediation steps, required certifications, and timeline to resolve.",
+    "warn": "Focus on identifying the specific compliance gap and how to close it.",
+    "ok": "Focus on maintaining compliance evidence and monitoring future changes.",
+}
+
+_SUGGESTION_FALLBACK = {
+    "risk": [
+        "What specific certifications or documents are required to remediate this finding?",
+        "What is the typical remediation timeline for this type of non-compliance?",
+        "Which regulation clause defines the exact requirement?",
+    ],
+    "warn": [
+        "What is the exact gap between the current state and the requirement?",
+        "What evidence would demonstrate partial compliance?",
+        "Which regulation clause applies to this warning?",
+    ],
+    "ok": [
+        "What documentation should be maintained to evidence this compliance?",
+        "How should this area be monitored as regulations evolve?",
+        "Are there related clauses that may affect this compliant area?",
+    ],
+}
+
+
+def build_finding_context(finding: "FindingRecord", analysis: "AnalysisRecord") -> str:
+    """Build a grounded system context string for a finding chat thread.
+
+    Combines finding details with analysis metadata so the LLM has full
+    context without relying on the frontend to pass segment_context.
+    """
+    return (
+        f"Document: {analysis.doc_name}\n"
+        f"Standard: {analysis.standard_name}\n"
+        f"Finding [{finding.seq + 1}]: {finding.title}\n"
+        f"Status: {finding.status}\n"
+        f"Clause reference: {finding.clause_ref or 'N/A'}\n"
+        f"Description: {finding.description}\n"
+        f"Overall conclusion: {analysis.conclusion}\n"
+    )
+
+
+def generate_suggestions(
+    finding: "FindingRecord",
+    analysis: "AnalysisRecord",
+    client: "BaseLLMClient",
+) -> list[str]:
+    """Generate 3 context-aware follow-up questions for a finding chat thread.
+
+    Returns exactly 3 question strings. Falls back to static templates on error.
+    """
+    fallback = _SUGGESTION_FALLBACK.get(finding.status, _SUGGESTION_FALLBACK["warn"])
+    context = build_finding_context(finding, analysis)
+    focus = _SUGGESTION_FOCUS.get(finding.status, _SUGGESTION_FOCUS["warn"])
+    prompt = (
+        f"{context}\n\n"
+        f"Task: {focus}\n"
+        "Generate exactly 3 concise follow-up questions a compliance analyst would ask.\n"
+        'Return JSON: {"questions": ["question 1", "question 2", "question 3"]}\n'
+        "Return ONLY the JSON object."
+    )
+    response = client.chat([{"role": "user", "content": prompt}], max_tokens=300)
+    if not response.is_success:
+        return fallback
+    try:
+        result = _extract_json(response.content)
+        questions = result.get("questions", [])
+        if isinstance(questions, list) and len(questions) >= 3:
+            return [str(q) for q in questions[:3]]
+    except (ValueError, TypeError) as exc:
+        logger.warning("generate_suggestions JSON parse failed: {}", exc)
+    return fallback
+
+
+def detect_cross_clause_conflicts(
+    findings: list[dict],
+    client: "BaseLLMClient",
+) -> list[dict]:
+    """Detect contradictions and missing cross-references across all findings.
+
+    Runs a single LLM call after all per-clause findings are collected.
+    Returns a list of conflict dicts: {type, finding_a, finding_b, desc}.
+    Returns an empty list on LLM failure so the caller can proceed without it.
+    """
+    if len(findings) < 2:
+        # Need at least 2 findings to compare
+        return []
+
+    findings_text = "\n".join(
+        f"[{i+1}] [{f['status'].upper()}] {f['title']}: {f['desc']}"
+        + (f" (Ref: {f['clause_ref']})" if f.get("clause_ref") else "")
+        for i, f in enumerate(findings)
+    )
+    prompt = (
+        "You are a compliance expert. Review the following compliance findings from the same document "
+        "and identify any cross-clause issues:\n\n"
+        f"Findings:\n{findings_text}\n\n"
+        "Return JSON array of conflicts (empty array [] if none found):\n"
+        "[\n"
+        "  {\n"
+        '    "type": "contradiction" | "missing_ref" | "cumulative_risk",\n'
+        '    "finding_a": <1-based index>,\n'
+        '    "finding_b": <1-based index or null>,\n'
+        '    "desc": "Brief description of the cross-clause issue (max 100 chars)"\n'
+        "  }\n"
+        "]\n"
+        "Return ONLY the JSON array."
+    )
+    try:
+        response = client.chat([{"role": "user", "content": prompt}], max_tokens=600)
+        if not response.is_success:
+            return []
+        result = _extract_json(response.content)
+        if isinstance(result, list):
+            return [
+                {
+                    "type": str(c.get("type", "contradiction")),
+                    "finding_a": int(c.get("finding_a", 0)),
+                    "finding_b": c.get("finding_b"),
+                    "desc": str(c.get("desc", "")),
+                }
+                for c in result
+                if isinstance(c, dict)
+            ]
+    except Exception as exc:
+        logger.warning("detect_cross_clause_conflicts failed: {}", exc)
+    return []

backend/app/application/documents/services.py

@@ -277,7 +277,6 @@ class DocumentCommandService:
             message="Document record created",
         )
 
-        temp_path = ""
         try:
             self.binary_store.save(
                 object_name=object_name,
@@ -297,117 +296,20 @@ class DocumentCommandService:
                 stage="store",
                 message="Source file stored",
             )
-
+            # Delegate parse → embed → index to the shared processing method.
-            suffix = os.path.splitext(file_name)[1]
+            # This same method is invoked by the Celery worker for async processing.
-            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
+            return self._process_document(
-                temp_file.write(content)
-                temp_path = temp_file.name
-
-            parsed_document = self.parser.parse(
-                file_path=temp_path,
                 doc_id=doc_id,
-                doc_name=final_doc_name,
+                file_name=file_name,
-            )
+                final_doc_name=final_doc_name,
-            self._safe_mark_run_parsed(doc_id=doc_id, run_id=run_id, parsed_document=parsed_document)
+                content=content,
-
-            artifact_keys: dict[str, str] = {}
-            try:
-                artifact_keys = self._save_parse_artifacts(doc_id=doc_id, parsed_document=parsed_document)
-            except Exception:
-                logger.warning("Parse artifact binary persistence failed for doc_id={}", doc_id)
-            self.document_repository.update_status(
-                doc_id,
-                DocumentStatus.PARSED,
-                parser_name=parsed_document.parser_name,
-                metadata={
-                    "parser_backend": parsed_document.parser_name,
-                    "parse_task_id": parsed_document.metadata.get("task_id", ""),
-                    "layout_count": parsed_document.metadata.get("layout_count", len(parsed_document.raw_layouts)),
-                    "structure_node_count": len(parsed_document.structure_nodes),
-                    "semantic_block_count": len(parsed_document.semantic_blocks),
-                    "vector_chunk_count": len(parsed_document.vector_chunks),
-                    "artifact_keys": artifact_keys,
-                    "processing_stage": "parsed",
-                },
-            )
-            current_status = DocumentStatus.PARSED
-            current_stage = "embed"
-            self._safe_replace_processing_artifacts(doc_id=doc_id, run_id=run_id, artifact_keys=artifact_keys)
-            self._safe_append_status_event(
-                doc_id=doc_id,
-                run_id=run_id,
-                from_status=DocumentStatus.STORED.value,
-                to_status=DocumentStatus.PARSED.value,
-                stage="parse",
-                message="Document parsed",
-                metadata={"artifact_count": len(artifact_keys)},
-            )
-            if self.parse_artifact_store:
-                try:
-                    self.parse_artifact_store.save(
-                        doc_id,
-                        parsed_document.structure_nodes,
-                        parsed_document.semantic_blocks,
-                    )
-                except Exception:
-                    logger.warning("ParseArtifactStore.save failed for doc_id={}", doc_id)
-
-            chunks = self.chunk_builder.build(
-                parsed_document=parsed_document,
                 regulation_type=regulation_type,
                 version=version,
-            )
+                generate_summary=generate_summary,
-            if not chunks:
-                raise ValueError("解析完成但没有生成可入库的 chunks")
-
-            vectors = self.embedding_provider.embed_texts([chunk.embedding_text for chunk in chunks])
-            current_stage = "index"
-            inserted = self.vector_index.upsert(chunks, vectors)
-            if inserted != len(chunks):
-                logger.warning("Milvus upsert count mismatched: inserted={}, chunks={}", inserted, len(chunks))
-
-            health = self.vector_index.health()
-            self.document_repository.update_status(
-                doc_id,
-                DocumentStatus.INDEXED,
-                chunk_count=len(chunks),
-                summary="",
-                summary_latency_ms=0,
-                index_name=health.get("collection_name", ""),
-                metadata={
-                    "index_collection": health.get("collection_name", ""),
-                    "processing_stage": "indexed",
-                },
-            )
-            current_status = DocumentStatus.INDEXED
-            index_name = health.get("collection_name", "")
-            self._safe_mark_run_indexed(
-                doc_id=doc_id,
                 run_id=run_id,
-                chunk_count=len(chunks),
-                index_name=index_name,
-            )
-            self._safe_append_status_event(
-                doc_id=doc_id,
-                run_id=run_id,
-                from_status=DocumentStatus.PARSED.value,
-                to_status=DocumentStatus.INDEXED.value,
-                stage="index",
-                message="Document indexed",
-                metadata={"chunk_count": len(chunks), "index_name": index_name},
-            )
-            stored = self.document_repository.get(doc_id)
-            return DocumentProcessResult(
-                doc_id=doc_id,
-                doc_name=final_doc_name,
-                status=(stored.status.value if stored else DocumentStatus.INDEXED.value),
-                message="处理成功",
-                num_chunks=len(chunks),
-                summary=stored.summary if stored else "",
-                summary_latency_ms=stored.summary_latency_ms if stored else 0,
             )
         except Exception as exc:
-            logger.exception("文档处理失败: doc_id={}", doc_id)
+            logger.exception("文档存储失败: doc_id={}", doc_id)
             failure_stage = current_stage
             self.document_repository.update_status(
                 doc_id,
@@ -439,6 +341,183 @@ class DocumentCommandService:
                 status=DocumentStatus.FAILED.value,
                 message=f"文档处理失败: {exc}",
             )
+
+    def store_document(
+        self,
+        *,
+        doc_id: str | None = None,
+        file_name: str,
+        content: bytes,
+        content_type: str,
+        doc_name: str | None,
+        regulation_type: str,
+        version: str,
+        generate_summary: bool,
+    ) -> tuple[str, str | None]:
+        """Store the binary file and create the Document record.
+
+        Returns (doc_id, run_id). Does NOT parse, embed, or index.
+        This is the fast synchronous first step; processing is enqueued separately.
+        The caller is responsible for enqueuing the follow-up process_document_task.
+        """
+        doc_id = doc_id or str(uuid.uuid4())[:8]
+        final_doc_name = doc_name or file_name
+        object_name = f"{doc_id}/{file_name}"
+
+        document = Document(
+            doc_id=doc_id,
+            doc_name=final_doc_name,
+            file_name=file_name,
+            object_name=object_name,
+            content_type=content_type,
+            size_bytes=len(content),
+            regulation_type=regulation_type,
+            version=version,
+            metadata={"generate_summary": generate_summary},
+        )
+        self.document_repository.create(document)
+        run_id = self._safe_create_processing_run(
+            doc_id=doc_id, trigger_type="upload", generate_summary=generate_summary
+        )
+        self.binary_store.save(
+            object_name=object_name, data=content,
+            content_type=content_type, metadata={"doc_id": doc_id},
+        )
+        self.document_repository.update_status(doc_id, DocumentStatus.STORED)
+        self._safe_mark_run_stored(doc_id=doc_id, run_id=run_id)
+        self._safe_append_status_event(
+            doc_id=doc_id, run_id=run_id,
+            from_status=DocumentStatus.PENDING.value, to_status=DocumentStatus.STORED.value,
+            stage="store", message="Source file stored",
+        )
+        return doc_id, run_id
+
+    def _process_document(
+        self,
+        *,
+        doc_id: str,
+        file_name: str,
+        final_doc_name: str,
+        content: bytes,
+        regulation_type: str,
+        version: str,
+        generate_summary: bool,
+        run_id: str | None = None,
+    ) -> DocumentProcessResult:
+        """Run parse → chunk → embed → index for a document that is already stored.
+
+        Called both synchronously (from upload_and_process) and asynchronously
+        (from the Celery process_document_task worker). All side-effects write
+        through DocumentProcessingStore so callers can poll progress.
+        """
+        current_status = DocumentStatus.STORED
+        current_stage = "parse"
+        temp_path = ""
+        try:
+            suffix = os.path.splitext(file_name)[1]
+            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
+                temp_file.write(content)
+                temp_path = temp_file.name
+
+            parsed_document = self.parser.parse(
+                file_path=temp_path,
+                doc_id=doc_id,
+                doc_name=final_doc_name,
+            )
+            self._safe_mark_run_parsed(doc_id=doc_id, run_id=run_id, parsed_document=parsed_document)
+
+            artifact_keys: dict[str, str] = {}
+            try:
+                artifact_keys = self._save_parse_artifacts(doc_id=doc_id, parsed_document=parsed_document)
+            except Exception:
+                logger.warning("Parse artifact binary persistence failed for doc_id={}", doc_id)
+
+            self.document_repository.update_status(
+                doc_id,
+                DocumentStatus.PARSED,
+                parser_name=parsed_document.parser_name,
+                metadata={
+                    "parser_backend": parsed_document.parser_name,
+                    "parse_task_id": parsed_document.metadata.get("task_id", ""),
+                    "layout_count": parsed_document.metadata.get("layout_count", len(parsed_document.raw_layouts)),
+                    "structure_node_count": len(parsed_document.structure_nodes),
+                    "semantic_block_count": len(parsed_document.semantic_blocks),
+                    "vector_chunk_count": len(parsed_document.vector_chunks),
+                    "artifact_keys": artifact_keys,
+                    "processing_stage": "parsed",
+                },
+            )
+            current_status = DocumentStatus.PARSED
+            current_stage = "embed"
+            self._safe_replace_processing_artifacts(doc_id=doc_id, run_id=run_id, artifact_keys=artifact_keys)
+            self._safe_append_status_event(
+                doc_id=doc_id, run_id=run_id,
+                from_status=DocumentStatus.STORED.value, to_status=DocumentStatus.PARSED.value,
+                stage="parse", message="Document parsed", metadata={"artifact_count": len(artifact_keys)},
+            )
+            if self.parse_artifact_store:
+                try:
+                    self.parse_artifact_store.save(
+                        doc_id, parsed_document.structure_nodes, parsed_document.semantic_blocks,
+                    )
+                except Exception:
+                    logger.warning("ParseArtifactStore.save failed for doc_id={}", doc_id)
+
+            chunks = self.chunk_builder.build(
+                parsed_document=parsed_document,
+                regulation_type=regulation_type,
+                version=version,
+            )
+            if not chunks:
+                raise ValueError("解析完成但没有生成可入库的 chunks")
+
+            vectors = self.embedding_provider.embed_texts([chunk.embedding_text for chunk in chunks])
+            current_stage = "index"
+            inserted = self.vector_index.upsert(chunks, vectors)
+            if inserted != len(chunks):
+                logger.warning("Milvus upsert count mismatched: inserted={}, chunks={}", inserted, len(chunks))
+
+            health = self.vector_index.health()
+            index_name = health.get("collection_name", "")
+            self.document_repository.update_status(
+                doc_id, DocumentStatus.INDEXED,
+                chunk_count=len(chunks), summary="", summary_latency_ms=0,
+                index_name=index_name,
+                metadata={"index_collection": index_name, "processing_stage": "indexed"},
+            )
+            self._safe_mark_run_indexed(doc_id=doc_id, run_id=run_id, chunk_count=len(chunks), index_name=index_name)
+            self._safe_append_status_event(
+                doc_id=doc_id, run_id=run_id,
+                from_status=DocumentStatus.PARSED.value, to_status=DocumentStatus.INDEXED.value,
+                stage="index", message="Document indexed",
+                metadata={"chunk_count": len(chunks), "index_name": index_name},
+            )
+            stored = self.document_repository.get(doc_id)
+            return DocumentProcessResult(
+                doc_id=doc_id, doc_name=final_doc_name,
+                status=(stored.status.value if stored else DocumentStatus.INDEXED.value),
+                message="处理成功", num_chunks=len(chunks),
+                summary=stored.summary if stored else "",
+                summary_latency_ms=stored.summary_latency_ms if stored else 0,
+            )
+        except Exception as exc:
+            logger.exception("文档处理失败: doc_id={}", doc_id)
+            self.document_repository.update_status(
+                doc_id, DocumentStatus.FAILED, error_message=str(exc),
+                metadata={"failure_reason": str(exc), "processing_stage": "failed", "failure_stage": current_stage},
+            )
+            self._safe_mark_run_failed(
+                doc_id=doc_id, run_id=run_id, failure_stage=current_stage, error_message=str(exc)
+            )
+            self._safe_append_status_event(
+                doc_id=doc_id, run_id=run_id,
+                from_status=current_status.value, to_status=DocumentStatus.FAILED.value,
+                stage=current_stage, message=str(exc),
+            )
+            return DocumentProcessResult(
+                doc_id=doc_id, doc_name=final_doc_name,
+                status=DocumentStatus.FAILED.value, message=f"文档处理失败: {exc}",
+            )
         finally:
             if temp_path and os.path.exists(temp_path):
                 try:
@@ -446,12 +525,29 @@ class DocumentCommandService:
                 except OSError:
                     logger.warning("临时文件清理失败: {}", temp_path)
 
-
     def delete(self, doc_id: str) -> bool:
-        """Delete document record, binary file, and vector chunks."""
+        """Delete document record, binary file, and vector chunks.
+
+        Handles two cases:
+        - Normal docs: have a metadata record in the document repository.
+        - Milvus-only (synthetic) docs: visible in management-list because they
+          have Milvus vectors but no JSON/PG metadata record. We still clean up
+          the Milvus chunks so the document disappears from the list.
+        """
         document = self.document_repository.get(doc_id)
         if not document:
+            # No metadata record — might be a Milvus-only synthetic document.
+            # Attempt vector cleanup directly; treat as success if any chunks deleted.
+            try:
+                deleted_count = self.vector_index.delete_by_document(doc_id)
+                if deleted_count > 0:
+                    logger.info("Deleted Milvus-only doc (no metadata record): doc_id={} chunks={}", doc_id, deleted_count)
+                    return True
+            except Exception as exc:
+                logger.warning("Milvus-only delete failed for doc_id={}: {}", doc_id, exc)
             return False
+
+        # Normal doc: clean up binary, vectors, artifacts, processing records, metadata.
         try:
             self.binary_store.delete(document.object_name)
         except Exception:
@@ -549,13 +645,16 @@ class DocumentQueryService:
             result.append(doc)
 
         # Surface Milvus-only docs that have no metadata record at all.
+        # MinIO almost certainly has their binaries (they were uploaded), so
+        # set object_name to the sentinel "{doc_id}/" so the route marks
+        # has_file=True; the download endpoint will list MinIO to find the file.
         for doc_id, row in milvus_by_id.items():
             if doc_id not in meta_by_id:
                 synthetic = Document(
                     doc_id=doc_id,
                     doc_name=row.get("doc_title", doc_id),
                     file_name=row.get("doc_title", doc_id),
-                    object_name="",
+                    object_name=f"{doc_id}/",  # sentinel: MinIO prefix exists
                     content_type="",
                     size_bytes=0,
                     status=DocumentStatus.INDEXED,
@@ -568,9 +667,63 @@ class DocumentQueryService:
         result.sort(key=lambda d: d.updated_at, reverse=True)
         return result[:limit] if limit is not None else result
 
-    def download(self, doc_id: str) -> tuple[Document, bytes]:
+    def download(self, doc_id: str) -> tuple["Document", bytes]:
-        """Handle download for the Document Query Service instance."""
+        """Return the document record and its binary content from MinIO.
+
+        Fallback strategy for Milvus-only docs (no JSON/PG metadata record):
+          1. Try metadata repository first (normal path).
+          2. If metadata is missing, list MinIO objects with prefix ``{doc_id}/``
+             and synthesise a minimal Document from the first object found.
+             This handles documents whose metadata records were lost but whose
+             binary files are still in object storage.
+          3. If neither source has the file, raise FileNotFoundError.
+        """
+        from app.domain.documents import Document, DocumentStatus
+
         document = self.document_repository.get(doc_id)
-        if not document:
+
-            raise FileNotFoundError(f"文档不存在: {doc_id}")
+        if document and document.object_name and not document.object_name.endswith("/"):
+            # Normal doc with a concrete object_name — read directly.
+            return document, self.binary_store.read(document.object_name)
+
+        if document and not document.object_name:
+            raise FileNotFoundError(f"该文档无原始文件（仅含索引数据，无法下载）: {doc_id}")
+
+        if not document or document.object_name.endswith("/"):
+            # Metadata missing — try to find the file in MinIO by doc_id prefix.
+            try:
+                objects = self.binary_store.list_objects(prefix=f"{doc_id}/")
+                # Filter out artifact JSON files; prefer the source document.
+                candidates = [o for o in objects if not o.endswith(".json")]
+                if not candidates:
+                    candidates = objects  # fall back to all objects if only JSON found
+                if not candidates:
+                    raise FileNotFoundError(f"文档不存在（MinIO 和元数据均无记录）: {doc_id}")
+                object_name = candidates[0]
+                file_name = object_name.split("/", 1)[-1] if "/" in object_name else object_name
+                # Guess content type from extension.
+                ext = file_name.rsplit(".", 1)[-1].lower() if "." in file_name else ""
+                _ct_map = {
+                    "pdf": "application/pdf",
+                    "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+                    "doc": "application/msword",
+                    "txt": "text/plain",
+                }
+                content_type = _ct_map.get(ext, "application/octet-stream")
+                # Synthesise a minimal Document so the route can build the response.
+                document = Document(
+                    doc_id=doc_id,
+                    doc_name=file_name,
+                    file_name=file_name,
+                    object_name=object_name,
+                    content_type=content_type,
+                    size_bytes=0,
+                    status=DocumentStatus.INDEXED,
+                )
+                logger.info("MinIO fallback download: doc_id={} object={}", doc_id, object_name)
+            except FileNotFoundError:
+                raise
+            except Exception as exc:
+                raise FileNotFoundError(f"文档不存在: {doc_id}") from exc
+
         return document, self.binary_store.read(document.object_name)

backend/app/application/perception/crawl_service.py

@@ -0,0 +1,147 @@
+"""Orchestrates regulatory source crawlers and LLM enrichment pipeline."""
+
+from __future__ import annotations
+
+import hashlib
+from typing import Any, Generator
+
+from loguru import logger
+
+from app.infrastructure.perception.base_event_store import BaseEventStore
+from app.infrastructure.perception.crawlers.base import BaseCrawler, RawEvent
+from app.infrastructure.perception.llm_pipeline import LlmPipeline
+
+
+def _event_id(source: str, standard_code: str) -> str:
+    """Deterministic 12-char ID from source + standard_code."""
+    return hashlib.sha256(f"{source}-{standard_code}".encode()).hexdigest()[:12]
+
+
+def _content_hash(raw_text: str) -> str:
+    return hashlib.sha256(raw_text.encode()).hexdigest()
+
+
+def _raw_to_dict(raw: RawEvent, event_id: str, content_hash: str) -> dict:
+    return {
+        "id": event_id,
+        "source": raw.source,
+        "source_label": raw.source_label,
+        "standard_code": raw.standard_code,
+        "title": raw.title,
+        "summary": raw.summary,
+        "full_text_url": raw.full_text_url,
+        "status": raw.status,
+        "impact_level": "medium",
+        "published_at": raw.published_at,
+        "effective_at": raw.effective_at,
+        "category": raw.category,
+        "tags": raw.tags,
+        "content_hash": content_hash,
+        "previous_hash": None,
+    }
+
+
+class CrawlService:
+    """Orchestrate crawlers, hash-based change detection, and LLM enrichment."""
+
+    def __init__(
+        self,
+        crawlers: dict[str, BaseCrawler],
+        event_store: BaseEventStore,
+        llm_pipeline: LlmPipeline,
+        retrieval_service: Any,
+    ) -> None:
+        self._crawlers = crawlers
+        self._store = event_store
+        self._pipeline = llm_pipeline
+        self._retrieval = retrieval_service
+
+    def run_crawl(
+        self, sources: list[str] | None = None
+    ) -> Generator[dict, None, None]:
+        """Run crawl for selected sources. Yields SSE-ready progress dicts."""
+        targets = sources or list(self._crawlers.keys())
+        total_new = 0
+        total_updated = 0
+
+        for source_key in targets:
+            crawler = self._crawlers.get(source_key)
+            if not crawler:
+                yield {"event": "error", "data": f"Unknown source: {source_key}"}
+                continue
+
+            yield {"event": "progress", "data": {"source": source_key, "stage": "fetching"}}
+            try:
+                raw_events = crawler.fetch(limit=100)
+            except Exception as exc:
+                logger.exception("Crawler failed source={}", source_key)
+                yield {"event": "error", "data": {"source": source_key, "message": str(exc)}}
+                continue
+
+            yield {
+                "event": "progress",
+                "data": {"source": source_key, "stage": "processing", "fetched": len(raw_events)},
+            }
+
+            new_count = 0
+            updated_count = 0
+
+            for raw in raw_events:
+                eid = _event_id(raw.source, raw.standard_code)
+                new_hash = _content_hash(raw.raw_text or raw.title)
+                existing = self._store.get(eid)
+
+                if existing and existing.get("content_hash") == new_hash:
+                    continue
+
+                is_update = existing is not None
+                old_text = existing.get("summary", "") if is_update else ""
+                previous_hash = existing.get("content_hash") if is_update else None
+
+                event_dict = _raw_to_dict(raw, eid, new_hash)
+                event_dict["previous_hash"] = previous_hash
+
+                try:
+                    structure = self._pipeline.extract_structure(event_dict)
+                    event_dict.update(structure)
+                except Exception as exc:
+                    logger.warning("Structure extraction failed id={} err={}", eid, exc)
+
+                try:
+                    affected = self._pipeline.assess_impact(event_dict, self._retrieval)
+                    event_dict["affected_docs"] = affected
+                except Exception as exc:
+                    logger.warning("Impact assessment failed id={} err={}", eid, exc)
+
+                if is_update and old_text and raw.raw_text:
+                    try:
+                        diff = self._pipeline.compute_diff(old_text, raw.raw_text)
+                        event_dict["change_summary"] = diff.get("change_summary")
+                        event_dict["changed_sections"] = diff.get("changed_sections")
+                    except Exception as exc:
+                        logger.warning("Diff failed id={} err={}", eid, exc)
+
+                self._store.upsert(event_dict)
+
+                if is_update:
+                    updated_count += 1
+                else:
+                    new_count += 1
+
+            total_new += new_count
+            total_updated += updated_count
+
+            yield {
+                "event": "progress",
+                "data": {
+                    "source": source_key,
+                    "stage": "done",
+                    "new": new_count,
+                    "updated": updated_count,
+                },
+            }
+
+        yield {
+            "event": "done",
+            "data": {"total_new": total_new, "total_updated": total_updated},
+        }

backend/app/application/perception/services.py

@@ -6,7 +6,7 @@ import json
 from typing import Generator
 
 from app.application.knowledge.services import KnowledgeRetrievalService
-from app.infrastructure.perception.mock_event_store import MockEventStore
+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
 
@@ -22,7 +22,7 @@ class PerceptionService:
 
     def __init__(
         self,
-        event_store: MockEventStore,
+        event_store: BaseEventStore,
         retrieval_service: KnowledgeRetrievalService,
     ) -> None:
         self._store = event_store

backend/app/config/settings.py

@@ -82,6 +82,22 @@ class Settings(BaseSettings):
     parser_backend: str = Field(default="aliyun", description="解析后端(local/aliyun)")
     chunk_backend: str = Field(default="aliyun", description="分块后端(local/aliyun)")
     document_repository_backend: str = Field(default="json", description="文档元数据存储后端 (json/postgres)")
+    # When True, document processing is enqueued to Celery workers via Redis.
+    # When False (default), processing runs in a FastAPI BackgroundTask in the same process —
+    # no external worker needed. Switch to True only when a Celery worker is running.
+    use_celery_worker: bool = Field(default=False, description="使用 Celery Worker 异步处理文档 (需要 Worker 运行中)")
+
+    # ── Perception crawl ──────────────────────────────────────────────────────
+    perception_crawl_timeout_seconds: int = Field(
+        default=120, description="HTTP timeout for regulatory source crawlers."
+    )
+    perception_max_events_per_source: int = Field(
+        default=100, description="Maximum events fetched per source per crawl run."
+    )
+    perception_diff_similarity_threshold: float = Field(
+        default=0.85,
+        description="Cosine similarity below which a paragraph is flagged as changed.",
+    )
 
     # Keep configuration setup explicit so runtime behavior is easy to reason about.
     api_host: str = Field(default="0.0.0.0", description="API服务地址")
@@ -109,6 +125,7 @@ class Settings(BaseSettings):
     rag_retrieval_top_k: int = Field(default=20, description="精排前召回候选数量（reranker 启用时生效）")
     rag_max_context_tokens: int = Field(default=2000, description="RAG最大上下文token数")
     rag_summary_max_tokens: int = Field(default=10240, description="文档摘要最大token数")
+    rag_skills_max_tokens: int = Field(default=2048, description="技能类 RAG 最大 token 数")
 
     reranker_enabled: bool = Field(default=False, description="是否启用 Cross-Encoder 精排")
     reranker_base_url: str = Field(default="", description="Reranker API 地址")
@@ -116,6 +133,42 @@ class Settings(BaseSettings):
     reranker_api_key: str = Field(default="", description="Reranker API 密钥")
     reranker_top_k: int = Field(default=5, description="精排后保留的最终结果数量")
 
+    # ── HyDE (Hypothetical Document Embeddings) ──────────────────────────────
+    # When enabled, the agentic and standard RAG pipelines generate a short
+    # hypothetical answer before retrieval, then embed that text instead of the
+    # raw query. This closes the vocabulary gap between terse queries and longer
+    # document passages, typically improving recall by 15-30% on vague queries.
+    hyde_enabled: bool = Field(default=True, description="启用 HyDE 查询增强（假设文档嵌入）")
+    hyde_max_tokens: int = Field(default=200, description="HyDE 假设段落最大 token 数")
+    # Use a lightweight model for HyDE to reduce latency and cost.
+    # HyDE only needs a short plausible passage — a fast cheap model is sufficient.
+    # Leave empty to fall back to the main llm_provider / llm_model.
+    hyde_llm_provider: str = Field(default="", description="HyDE 专用 LLM 提供商（空则复用主 LLM）")
+    hyde_llm_model: str = Field(default="", description="HyDE 专用 LLM 模型（空则复用主 LLM）")
+
+    # ── Agentic RAG (P0-1) ───────────────────────────────────────────────────
+    # Controls the multi-step reasoning pipeline exposed at /agent/agentic/stream.
+    agentic_max_sub_queries: int = Field(
+        default=4,
+        description="Agentic 模式最大子查询分解数量（compare / multi_hop 意图触发）",
+    )
+    agentic_grounding_threshold: float = Field(
+        default=0.65,
+        description=(
+            "引文锚定 fast-path 阈值：avg_score > 此值且 chunks ≥ 3 时跳过 LLM grounding check，"
+            "直接判定为充分；降低此值可让更多问题触发 LLM 二次验证。"
+        ),
+    )
+    agentic_intent_max_tokens: int = Field(
+        default=200, description="意图分析步骤 LLM 最大 token 数"
+    )
+    agentic_plan_max_tokens: int = Field(
+        default=400, description="查询分解步骤 LLM 最大 token 数"
+    )
+    agentic_grounding_max_tokens: int = Field(
+        default=250, description="引文锚定步骤 LLM 最大 token 数"
+    )
+
     # Keep configuration setup explicit so runtime behavior is easy to reason about.
     milvus_index_type: str = Field(default="IVF_FLAT", description="Milvus索引类型")
     milvus_nlist: int = Field(default=128, description="Milvus nlist参数")
@@ -124,6 +177,26 @@ class Settings(BaseSettings):
     # Keep configuration setup explicit so runtime behavior is easy to reason about.
     session_max_sessions: int = Field(default=100, description="最大会话数量")
     session_timeout_minutes: int = Field(default=30, description="会话超时时间(分钟)")
+    session_backend: str = Field(
+        default="memory",
+        description="会话存储后端 (memory | redis)。redis 需要 Redis 可用。",
+    )
+
+    # ── Auth ──────────────────────────────────────────────────────────────────
+    # Generate a strong secret: python -c "import secrets; print(secrets.token_hex(32))"
+    auth_secret_key: str = Field(
+        default="change-me-in-production-must-be-32-or-more-characters-long",
+        description="JWT signing secret. MUST be changed in production.",
+    )
+    auth_algorithm: str = Field(default="HS256", description="JWT signing algorithm.")
+    auth_token_expire_minutes: int = Field(default=480, description="JWT TTL in minutes (default 8 hours).")
+    auth_enabled: bool = Field(default=True, description="Set False to bypass auth (development only).")
+
+    # ── CORS ──────────────────────────────────────────────────────────────────
+    cors_allow_origins: str = Field(
+        default="http://localhost:5173",
+        description="Comma-separated allowed CORS origins. Never use * in production.",
+    )
 
 @lru_cache
 def get_settings() -> Settings:

backend/app/domain/auth/__init__.py

@@ -0,0 +1,10 @@
+"""Auth domain: role definitions and token claim models.
+
+The domain layer defines what a user identity looks like (UserClaims) and
+what roles exist (UserRole). Infrastructure details (JWT, bcrypt, PostgreSQL)
+live under infrastructure/auth and never leak into this package.
+"""
+
+from .models import UserClaims, UserRole
+
+__all__ = ["UserClaims", "UserRole"]

backend/app/domain/auth/models.py

@@ -0,0 +1,42 @@
+"""Auth domain models: roles and token claims.
+
+UserRole defines the four roles from PPT Slide 12.
+UserClaims is what the JWT decodes to — it is the identity object passed
+through FastAPI dependency injection to route handlers.
+"""
+
+from __future__ import annotations
+
+import enum
+from dataclasses import dataclass
+
+
+class UserRole(str, enum.Enum):
+    """Access roles mirroring the four-role RBAC matrix from the product spec.
+
+    ADMIN      — full platform access including system management.
+    LEGAL      — knowledge query, document review, compliance checks.
+    EHS        — knowledge query, perception/regulatory signals.
+    READONLY   — knowledge query only.
+    """
+
+    ADMIN = "admin"
+    LEGAL = "legal"
+    EHS = "ehs"
+    READONLY = "readonly"
+
+
+@dataclass
+class UserClaims:
+    """Decoded JWT payload representing an authenticated user.
+
+    Instances are created by JWTHandler.decode_token() and injected into
+    route handlers via the get_current_user FastAPI dependency.
+    """
+
+    # Unique user identifier (UUID string stored in PostgreSQL users table).
+    user_id: str
+    # Display name used for audit log entries.
+    username: str
+    # Role determines which resources the user may access.
+    role: UserRole

backend/app/domain/compliance/ports.py

@@ -0,0 +1,66 @@
+"""Domain ports for compliance history persistence."""
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional
+
+
+@dataclass
+class FindingRecord:
+    """Single finding row linked to an analysis."""
+    id: str
+    analysis_id: str
+    seq: int
+    title: str
+    description: str
+    status: str          # "ok" | "warn" | "risk"
+    clause_ref: Optional[str] = None
+
+
+@dataclass
+class AnalysisRecord:
+    """Full compliance analysis record with nested findings."""
+    id: str              # UUID string; empty string means not yet persisted
+    created_at: datetime
+    created_by: Optional[str]
+    doc_name: str
+    standard_name: str
+    risk_score: int
+    conclusion: str
+    actions: list        # list[dict] — serialised action items
+    para_text: str
+    highlight_terms: list  # list[str]
+    findings: list[FindingRecord] = field(default_factory=list)
+
+
+class ComplianceRepository(ABC):
+    """Port for persisting and retrieving compliance analysis records."""
+
+    @abstractmethod
+    def save_analysis(self, record: AnalysisRecord) -> str:
+        """Persist a new analysis record and return the assigned UUID string."""
+
+    @abstractmethod
+    def list_analyses(self, limit: int = 50, offset: int = 0) -> list[AnalysisRecord]:
+        """Return analyses ordered by created_at DESC, without nested findings."""
+
+    @abstractmethod
+    def get_analysis(self, analysis_id: str) -> Optional[AnalysisRecord]:
+        """Return a single analysis with all nested findings, or None."""
+
+    @abstractmethod
+    def delete_analysis(self, analysis_id: str) -> None:
+        """Delete an analysis and all related findings and chat messages (cascade)."""
+
+    @abstractmethod
+    def save_message(self, analysis_id: str, finding_id: str, role: str, content: str) -> str:
+        """Persist a chat message and return its UUID string."""
+
+    @abstractmethod
+    def get_messages(self, finding_id: str) -> list[dict]:
+        """Return chat messages for a finding ordered by created_at ASC.
+
+        Each dict has keys: id, role, content, created_at (ISO string).
+        """

backend/app/infrastructure/auth/__init__.py

@@ -0,0 +1,5 @@
+"""JWT token creation and validation infrastructure.
+
+JWTHandler is the only component in this package. It is wired through
+shared/bootstrap.py and injected into FastAPI dependencies.
+"""

backend/app/infrastructure/auth/jwt_handler.py

@@ -0,0 +1,82 @@
+"""JWT access token creation and decoding.
+
+Uses python-jose for HS256 token signing. Token expiry is enforced at
+decode time so expired tokens are rejected even if the signature is valid.
+"""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime, timedelta
+from typing import Any
+
+from jose import JWTError, jwt
+from loguru import logger
+
+from app.domain.auth.models import UserClaims, UserRole
+
+
+class JWTHandler:
+    """Create and validate HS256 JWT access tokens.
+
+    A single shared instance is wired by bootstrap.py. Use
+    get_jwt_handler() from shared.bootstrap for all token operations.
+    """
+
+    def __init__(
+        self,
+        *,
+        secret_key: str,
+        algorithm: str = "HS256",
+        expire_minutes: int = 480,
+    ) -> None:
+        """Initialise the handler with signing credentials and token lifetime."""
+        self._secret = secret_key
+        self._algorithm = algorithm
+        self._expire_minutes = expire_minutes
+
+    def create_access_token(
+        self,
+        *,
+        user_id: str,
+        username: str,
+        role: str,
+    ) -> str:
+        """Return a signed JWT containing user identity and role claims."""
+        now = datetime.now(UTC)
+        payload: dict[str, Any] = {
+            "sub": user_id,
+            "username": username,
+            "role": role,
+            "iat": now,
+            "exp": now + timedelta(minutes=self._expire_minutes),
+        }
+        return jwt.encode(payload, self._secret, algorithm=self._algorithm)
+
+    def decode_token(self, token: str) -> UserClaims:
+        """Decode and validate a JWT, returning UserClaims.
+
+        Raises ValueError with a descriptive message on expiry, tampering,
+        or any other validation failure so callers do not need to know jose.
+        """
+        try:
+            payload = jwt.decode(token, self._secret, algorithms=[self._algorithm])
+        except JWTError as exc:
+            msg = str(exc).lower()
+            if "expired" in msg:
+                raise ValueError("Token expired") from exc
+            raise ValueError(f"Invalid token: {exc}") from exc
+
+        user_id = payload.get("sub")
+        username = payload.get("username", "")
+        role_str = payload.get("role", UserRole.READONLY.value)
+
+        if not user_id:
+            raise ValueError("Token missing subject claim")
+
+        try:
+            role = UserRole(role_str)
+        except ValueError:
+            logger.warning("Unknown role in token: {}, defaulting to readonly", role_str)
+            role = UserRole.READONLY
+
+        return UserClaims(user_id=user_id, username=username, role=role)

backend/app/infrastructure/auth/user_store.py

@@ -0,0 +1,113 @@
+"""PostgreSQL-backed user store for authentication.
+
+Manages a `users` table with hashed passwords and roles.
+Provides lookup by username for the login flow.
+Table DDL is auto-applied on first connection.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import psycopg2
+import psycopg2.extras
+from loguru import logger
+from passlib.context import CryptContext
+
+from app.config.settings import settings
+
+
+# bcrypt context — work factor 12 is a good production default.
+_PWD_CTX = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+# DDL executed once to ensure the table exists.
+_CREATE_TABLE_SQL = """
+CREATE TABLE IF NOT EXISTS users (
+    id          UUID        PRIMARY KEY DEFAULT gen_random_uuid(),
+    username    VARCHAR(100) UNIQUE NOT NULL,
+    hashed_pw   TEXT         NOT NULL,
+    role        VARCHAR(50)  NOT NULL DEFAULT 'readonly',
+    is_active   BOOLEAN      NOT NULL DEFAULT TRUE,
+    created_at  TIMESTAMPTZ  NOT NULL DEFAULT NOW()
+);
+"""
+
+
+@dataclass
+class UserRecord:
+    """A single row from the users table."""
+
+    id: str
+    username: str
+    hashed_pw: str
+    role: str
+    is_active: bool
+
+
+class PostgresUserStore:
+    """Read and verify users stored in the PostgreSQL users table.
+
+    The connection is opened on first use and shared for the lifetime
+    of the singleton instance wired by bootstrap.
+    """
+
+    def __init__(self) -> None:
+        """Initialise the store and ensure the users table exists."""
+        self._conn = psycopg2.connect(
+            host=settings.postgres_host,
+            port=settings.postgres_port,
+            user=settings.postgres_user,
+            password=settings.postgres_password,
+            dbname=settings.postgres_db,
+            cursor_factory=psycopg2.extras.RealDictCursor,
+        )
+        self._conn.autocommit = True
+        self._ensure_table()
+
+    def _ensure_table(self) -> None:
+        """Create the users table if it does not already exist."""
+        with self._conn.cursor() as cur:
+            # Enable pgcrypto so gen_random_uuid() is available for UUID primary keys.
+            try:
+                cur.execute("CREATE EXTENSION IF NOT EXISTS pgcrypto;")
+            except Exception:
+                self._conn.rollback()
+            cur.execute(_CREATE_TABLE_SQL)
+
+    def get_by_username(self, username: str) -> Optional[UserRecord]:
+        """Return a UserRecord for the given username, or None if not found."""
+        with self._conn.cursor() as cur:
+            cur.execute(
+                "SELECT id, username, hashed_pw, role, is_active "
+                "FROM users WHERE username = %s",
+                (username,),
+            )
+            row = cur.fetchone()
+        if row is None:
+            return None
+        return UserRecord(
+            id=str(row["id"]),
+            username=row["username"],
+            hashed_pw=row["hashed_pw"],
+            role=row["role"],
+            is_active=row["is_active"],
+        )
+
+    def verify_password(self, plain: str, hashed: str) -> bool:
+        """Return True if `plain` matches the stored bcrypt hash."""
+        return _PWD_CTX.verify(plain, hashed)
+
+    def authenticate(self, username: str, password: str) -> Optional[UserRecord]:
+        """Return the UserRecord if credentials are valid, else None."""
+        user = self.get_by_username(username)
+        if user is None or not user.is_active:
+            return None
+        if not self.verify_password(password, user.hashed_pw):
+            return None
+        return user
+
+    @staticmethod
+    def hash_password(plain: str) -> str:
+        """Hash a plain-text password with bcrypt."""
+        return _PWD_CTX.hash(plain)

backend/app/infrastructure/compliance/docx_export.py

@@ -0,0 +1,101 @@
+"""DOCX report generator for compliance analysis results.
+
+Uses python-docx (already in requirements.txt). Returns raw bytes so the
+caller can stream the response without writing to disk.
+"""
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from io import BytesIO
+
+from docx import Document
+from docx.shared import Pt, RGBColor
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+
+from app.domain.compliance.ports import AnalysisRecord
+
+_STATUS_LABEL = {"ok": "Compliant", "warn": "Warning", "risk": "Non-Compliant"}
+_STATUS_COLOR = {
+    "ok": RGBColor(0x22, 0x8B, 0x22),
+    "warn": RGBColor(0xFF, 0x8C, 0x00),
+    "risk": RGBColor(0xDC, 0x14, 0x3C),
+}
+
+
+def generate_docx(record: AnalysisRecord) -> bytes:
+    """Generate a compliance report DOCX and return its raw bytes.
+
+    Structure:
+    - Cover: document name, standard, date, risk score
+    - Executive summary (conclusion)
+    - Findings table
+    - Recommended actions
+    - Footer note
+    """
+    doc = Document()
+
+    # ── Cover ──────────────────────────────────────────────────────────────────
+    title_para = doc.add_heading("Compliance Analysis Report", level=0)
+    title_para.alignment = WD_ALIGN_PARAGRAPH.CENTER
+
+    doc.add_paragraph("")
+    meta_table = doc.add_table(rows=4, cols=2)
+    meta_table.style = "Table Grid"
+    labels = ["Document", "Standard", "Date", "Risk Score"]
+    values = [
+        record.doc_name,
+        record.standard_name,
+        record.created_at.strftime("%Y-%m-%d %H:%M UTC") if record.created_at else "",
+        f"{record.risk_score} / 100",
+    ]
+    for i, (label, value) in enumerate(zip(labels, values)):
+        meta_table.cell(i, 0).text = label
+        meta_table.cell(i, 1).text = value
+
+    # ── Executive Summary ──────────────────────────────────────────────────────
+    doc.add_heading("Executive Summary", level=1)
+    doc.add_paragraph(record.conclusion)
+
+    # ── Findings ───────────────────────────────────────────────────────────────
+    doc.add_heading("Findings", level=1)
+    if record.findings:
+        table = doc.add_table(rows=1, cols=4)
+        table.style = "Table Grid"
+        hdr = table.rows[0].cells
+        for i, h in enumerate(["#", "Status", "Title", "Description / Clause"]):
+            hdr[i].text = h
+            for run in hdr[i].paragraphs[0].runs:
+                run.bold = True
+
+        for f in record.findings:
+            row = table.add_row().cells
+            row[0].text = str(f.seq + 1)
+            row[1].text = _STATUS_LABEL.get(f.status, f.status)
+            row[2].text = f.title
+            desc = f.description
+            if f.clause_ref:
+                desc += f"\n[{f.clause_ref}]"
+            row[3].text = desc
+    else:
+        doc.add_paragraph("No findings recorded.")
+
+    # ── Recommended Actions ────────────────────────────────────────────────────
+    doc.add_heading("Recommended Actions", level=1)
+    for i, action in enumerate(record.actions, start=1):
+        label = action.get("label", "Action")
+        value = action.get("value", "")
+        doc.add_paragraph(f"{i}. {label}: {value}", style="List Number")
+
+    # ── Footer note ────────────────────────────────────────────────────────────
+    doc.add_paragraph("")
+    footer = doc.add_paragraph(
+        f"Generated by AI Regulation Analysis System — {datetime.now(timezone.utc).strftime('%Y-%m-%d')}"
+    )
+    footer.alignment = WD_ALIGN_PARAGRAPH.CENTER
+    for run in footer.runs:
+        run.font.size = Pt(8)
+        run.font.color.rgb = RGBColor(0x88, 0x88, 0x88)
+
+    buf = BytesIO()
+    doc.save(buf)
+    return buf.getvalue()

backend/app/infrastructure/compliance/repository.py

@@ -0,0 +1,280 @@
+# backend/app/infrastructure/compliance/repository.py
+"""PostgreSQL-backed compliance analysis repository.
+
+Follows the same psycopg2 pattern as PostgresDocumentRepository:
+ThreadedConnectionPool + RealDictCursor for reads, _ensure_schema on init.
+"""
+from __future__ import annotations
+
+import json
+from contextlib import contextmanager
+from datetime import datetime
+from typing import Optional
+
+import psycopg2
+import psycopg2.extras
+import psycopg2.pool
+from loguru import logger
+
+from app.domain.compliance.ports import (
+    AnalysisRecord,
+    ComplianceRepository,
+    FindingRecord,
+)
+
+
+class PostgresComplianceRepository(ComplianceRepository):
+    """Stores compliance analyses, findings, and finding chat messages in PostgreSQL."""
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        user: str,
+        password: str,
+        dbname: str,
+        minconn: int = 1,
+        maxconn: int = 5,
+    ) -> None:
+        self._pool = psycopg2.pool.ThreadedConnectionPool(
+            minconn=minconn,
+            maxconn=maxconn,
+            host=host,
+            port=port,
+            user=user,
+            password=password,
+            dbname=dbname,
+        )
+        self._ensure_schema()
+
+    @contextmanager
+    def _conn(self):
+        conn = self._pool.getconn()
+        try:
+            yield conn
+        finally:
+            self._pool.putconn(conn)
+
+    def _ensure_schema(self) -> None:
+        """Create tables if they do not exist."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS compliance_analyses (
+                        id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        created_at      TIMESTAMPTZ NOT NULL DEFAULT now(),
+                        created_by      VARCHAR(255),
+                        doc_name        VARCHAR(500),
+                        standard_name   VARCHAR(500),
+                        risk_score      INTEGER,
+                        conclusion      TEXT,
+                        actions         JSONB,
+                        para_text       TEXT,
+                        highlight_terms JSONB
+                    );
+                """)
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS compliance_findings (
+                        id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+                        seq         INTEGER NOT NULL,
+                        title       VARCHAR(500),
+                        description TEXT,
+                        status      VARCHAR(50),
+                        clause_ref  VARCHAR(200)
+                    );
+                """)
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS finding_chat_messages (
+                        id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+                        finding_id  UUID NOT NULL REFERENCES compliance_findings(id) ON DELETE CASCADE,
+                        role        VARCHAR(20) NOT NULL,
+                        content     TEXT NOT NULL,
+                        created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
+                    );
+                """)
+            conn.commit()
+
+    def save_analysis(self, record: AnalysisRecord) -> str:
+        """Insert analysis + findings; return the new analysis UUID."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    INSERT INTO compliance_analyses
+                        (created_by, doc_name, standard_name, risk_score,
+                         conclusion, actions, para_text, highlight_terms)
+                    VALUES
+                        (%(created_by)s, %(doc_name)s, %(standard_name)s, %(risk_score)s,
+                         %(conclusion)s, %(actions)s, %(para_text)s, %(highlight_terms)s)
+                    RETURNING id
+                    """,
+                    {
+                        "created_by": record.created_by,
+                        "doc_name": record.doc_name,
+                        "standard_name": record.standard_name,
+                        "risk_score": record.risk_score,
+                        "conclusion": record.conclusion,
+                        "actions": json.dumps(record.actions, ensure_ascii=False),
+                        "para_text": record.para_text,
+                        "highlight_terms": json.dumps(record.highlight_terms, ensure_ascii=False),
+                    },
+                )
+                row = cur.fetchone()
+                analysis_id = str(row["id"])
+
+            if record.findings:
+                with conn.cursor() as cur:
+                    for f in record.findings:
+                        cur.execute(
+                            """
+                            INSERT INTO compliance_findings
+                                (analysis_id, seq, title, description, status, clause_ref)
+                            VALUES
+                                (%(analysis_id)s, %(seq)s, %(title)s, %(desc)s, %(status)s, %(clause_ref)s)
+                            """,
+                            {
+                                "analysis_id": analysis_id,
+                                "seq": f.seq,
+                                "title": f.title,
+                                "desc": f.description,
+                                "status": f.status,
+                                "clause_ref": f.clause_ref,
+                            },
+                        )
+            conn.commit()
+        return analysis_id
+
+    def list_analyses(self, limit: int = 50, offset: int = 0) -> list[AnalysisRecord]:
+        """Return analyses without nested findings, ordered newest first."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    SELECT id, created_at, created_by, doc_name, standard_name,
+                           risk_score, conclusion, actions, para_text, highlight_terms
+                    FROM compliance_analyses
+                    ORDER BY created_at DESC
+                    LIMIT %(limit)s OFFSET %(offset)s
+                    """,
+                    {"limit": limit, "offset": offset},
+                )
+                rows = cur.fetchall()
+        return [self._row_to_record(dict(r)) for r in rows]
+
+    def get_analysis(self, analysis_id: str) -> Optional[AnalysisRecord]:
+        """Return analysis with nested findings list."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    "SELECT * FROM compliance_analyses WHERE id = %(id)s",
+                    {"id": analysis_id},
+                )
+                row = cur.fetchone()
+                if not row:
+                    return None
+                record = self._row_to_record(dict(row))
+
+                cur.execute(
+                    """
+                    SELECT id, analysis_id, seq, title, description, status, clause_ref
+                    FROM compliance_findings
+                    WHERE analysis_id = %(id)s
+                    ORDER BY seq
+                    """,
+                    {"id": analysis_id},
+                )
+                findings = [
+                    FindingRecord(
+                        id=str(r["id"]),
+                        analysis_id=str(r["analysis_id"]),
+                        seq=r["seq"],
+                        title=r["title"] or "",
+                        description=r["description"] or "",
+                        status=r["status"] or "ok",
+                        clause_ref=r["clause_ref"],
+                    )
+                    for r in cur.fetchall()
+                ]
+                record.findings = findings
+        return record
+
+    def delete_analysis(self, analysis_id: str) -> None:
+        """Delete analysis; findings and chat messages cascade automatically."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "DELETE FROM compliance_analyses WHERE id = %(id)s",
+                    {"id": analysis_id},
+                )
+            conn.commit()
+
+    def save_message(self, analysis_id: str, finding_id: str, role: str, content: str) -> str:
+        """Persist a chat message; return its UUID."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    INSERT INTO finding_chat_messages
+                        (analysis_id, finding_id, role, content)
+                    VALUES
+                        (%(analysis_id)s, %(finding_id)s, %(role)s, %(content)s)
+                    RETURNING id
+                    """,
+                    {
+                        "analysis_id": analysis_id,
+                        "finding_id": finding_id,
+                        "role": role,
+                        "content": content,
+                    },
+                )
+                row = cur.fetchone()
+            conn.commit()
+        return str(row["id"])
+
+    def get_messages(self, finding_id: str) -> list[dict]:
+        """Return messages for a finding, oldest first."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    SELECT id, role, content, created_at
+                    FROM finding_chat_messages
+                    WHERE finding_id = %(finding_id)s
+                    ORDER BY created_at ASC
+                    """,
+                    {"finding_id": finding_id},
+                )
+                rows = cur.fetchall()
+        return [
+            {
+                "id": str(r["id"]),
+                "role": r["role"],
+                "content": r["content"],
+                "created_at": r["created_at"].isoformat() if r["created_at"] else "",
+            }
+            for r in rows
+        ]
+
+    def _row_to_record(self, row: dict) -> AnalysisRecord:
+        """Convert a RealDictCursor row to an AnalysisRecord (no findings)."""
+        actions = row.get("actions") or []
+        if isinstance(actions, str):
+            actions = json.loads(actions)
+        highlight_terms = row.get("highlight_terms") or []
+        if isinstance(highlight_terms, str):
+            highlight_terms = json.loads(highlight_terms)
+        return AnalysisRecord(
+            id=str(row["id"]),
+            created_at=row["created_at"] if isinstance(row["created_at"], datetime) else datetime.utcnow(),
+            created_by=row.get("created_by"),
+            doc_name=row.get("doc_name") or "",
+            standard_name=row.get("standard_name") or "",
+            risk_score=int(row.get("risk_score") or 0),
+            conclusion=row.get("conclusion") or "",
+            actions=actions,
+            para_text=row.get("para_text") or "",
+            highlight_terms=highlight_terms,
+            findings=[],
+        )

backend/app/infrastructure/embedding/openai_compatible_embedding_provider.py

@@ -3,11 +3,13 @@
 from __future__ import annotations
 
 import os
+import time
 
 import httpx
 
 from app.config.settings import settings
 from app.domain.retrieval import EmbeddingProvider
+from app.shared.model_usage_tracker import get_model_usage_tracker
 # Keep adapter behavior explicit so integration details remain easy to audit.
 
 EMBEDDING_BATCH_SIZE = 8
@@ -45,6 +47,8 @@ class OpenAICompatibleEmbeddingProvider(EmbeddingProvider):
         """Handle request for this module for the Open A I Compatible Embedding Provider instance."""
         if not self.api_key:
             raise ValueError("缺少 EMBEDDING_API_KEY / OPENAI_API_KEY")
+        start = time.time()
+        try:
             response = httpx.post(
                 f"{self.base_url}/embeddings",
                 headers={
@@ -56,9 +60,28 @@ class OpenAICompatibleEmbeddingProvider(EmbeddingProvider):
             )
             self._raise_for_status(response, batch_size=len(texts))
             data = response.json()
+        except Exception as exc:
+            # Record the failed call so the Status page can show it as an error,
+            # then re-raise unchanged so existing callers keep their current behavior.
+            get_model_usage_tracker().record(
+                provider="embedding",
+                model=self.model,
+                success=False,
+                latency_ms=int((time.time() - start) * 1000),
+                error=str(exc),
+            )
+            raise
         vectors = [item["embedding"] for item in sorted(data.get("data", []), key=lambda item: item["index"])]
         if any(len(vector) != self.dimension for vector in vectors):
             raise ValueError(f"embedding 维度不匹配，期望 {self.dimension}")
+        # Record token usage from the OpenAI-compatible response, e.g. {"total_tokens": N}.
+        get_model_usage_tracker().record(
+            provider="embedding",
+            model=self.model,
+            success=True,
+            usage=data.get("usage", {}),
+            latency_ms=int((time.time() - start) * 1000),
+        )
         return vectors
 
     def embed_texts(self, texts: list[str]) -> list[list[float]]:

backend/app/infrastructure/llm/openai_compatible_answer_generator.py

@@ -9,10 +9,12 @@ from app.config.settings import settings
 from app.domain.conversation import AnswerGenerator, AnswerResult, AnswerSource
 from app.domain.retrieval import RetrievedChunk
 from app.services.llm.llm_factory import get_llm_client
+from app.services.rag.prompt_templates import PromptTemplates
 # Keep adapter behavior explicit so integration details remain easy to audit.
 
 
-PROMPT_TEMPLATES = {
+# Fallback system prompts used when no rich template matches.
+_FALLBACK_PROMPTS = {
     "default": "你是法规知识问答助手。请仅依据提供的上下文回答；如果上下文不足，明确说明。",
     "compliance_qa": "你是法规合规问答助手。优先引用给定法规原文，回答要准确、克制，并注明依据来源。",
 }
@@ -38,33 +40,80 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
         retrieved_chunks: list[RetrievedChunk],
         history: list[dict[str, str]] | None,
         prompt_template: str | None,
+        context_text: str | None = None,
+        context_filename: str | None = None,
     ) -> tuple[list[dict[str, str]], int]:
-        """Handle build messages for this module for the Open A I Compatible Answer Generator instance."""
+        """Build the message list to send to the LLM.
-        system_prompt = PROMPT_TEMPLATES.get(prompt_template or "compliance_qa", PROMPT_TEMPLATES["default"])
+
+        When context_text is provided the user's document is injected as a
+        dedicated section BEFORE the retrieved regulation chunks so the LLM
+        can reason about the document directly while still referencing regulations.
+        The retrieval step uses only the user's question, not the document text,
+        so embedding quality is preserved.
+
+        System prompt selection priority:
+          1. Rich template from PromptTemplates (compliance_qa / comparison /
+             compliance_check / clause_interpretation / …)
+          2. Fallback hardcoded prompt when no rich template matches.
+        """
+        # Look up the rich template first; fall back to simple hardcoded prompts.
+        tpl_name = prompt_template or "compliance_qa"
+        rich_tpl = PromptTemplates.get_template(tpl_name)
+        if rich_tpl:
+            system_prompt = rich_tpl.system_prompt
+        else:
+            system_prompt = _FALLBACK_PROMPTS.get(tpl_name, _FALLBACK_PROMPTS["default"])
         context_blocks = []
         context_tokens = 0
+
+        # ── User document context (if attached) ───────────────────────────────
+        if context_text and context_text.strip():
+            doc_label = f"附件文档：{context_filename}" if context_filename else "附件文档"
+            doc_block = f"[{doc_label}]\n{context_text.strip()}"
+            doc_tokens = self._estimate_tokens(doc_block)
+            # Reserve at most half the context budget for the user document
+            half_budget = settings.rag_max_context_tokens // 2
+            if doc_tokens > half_budget:
+                # Truncate document to fit half the budget
+                ratio = half_budget / doc_tokens
+                doc_block = doc_block[: int(len(doc_block) * ratio)] + "\n…（文档已截断）"
+                doc_tokens = half_budget
+            context_blocks.append(doc_block)
+            context_tokens += doc_tokens
+
+        # ── Retrieved regulation chunks ────────────────────────────────────────
+        remaining_budget = settings.rag_max_context_tokens - context_tokens
         for idx, chunk in enumerate(retrieved_chunks, start=1):
             block = (
-                f"[{idx}] 文档: {chunk.doc_title}\n"
+                f"[法规{idx}] 文档: {chunk.doc_title}\n"
                 f"章节: {chunk.section_title or '未标注'}\n"
                 f"页码: {chunk.page_start}" + (f"-{chunk.page_end}" if chunk.page_end and chunk.page_end != chunk.page_start else "") + "\n"
                 f"内容: {chunk.text}"
             )
             block_tokens = self._estimate_tokens(block)
-            if context_tokens + block_tokens > settings.rag_max_context_tokens:
+            if block_tokens > remaining_budget:
                 break
+            remaining_budget -= block_tokens
             context_tokens += block_tokens
             context_blocks.append(block)
+
         context = "\n\n".join(context_blocks)
         messages = [{"role": "system", "content": system_prompt}]
         for item in history or []:
             messages.append({"role": item["role"], "content": item["content"]})
-        messages.append(
+
-            {
+        # Craft the user turn differently when a document is attached
-                "role": "user",
+        if context_text and context_text.strip():
-                "content": f"问题：{query}\n\n参考上下文：\n{context}\n\n请在回答后给出简要引用编号。",
+            user_content = (
-            }
+                f"问题：{query}\n\n"
+                f"请先基于上方附件文档内容进行分析，再结合法规参考上下文给出合规评估。"
+                f"\n\n参考上下文：\n{context}\n\n"
+                f"请在回答中注明引用来源编号（如适用）。"
             )
+        else:
+            user_content = f"问题：{query}\n\n参考上下文：\n{context}\n\n请在回答后给出简要引用编号。"
+
+        messages.append({"role": "user", "content": user_content})
         return messages, context_tokens
 
     def _is_context_truncated(self, *, retrieved_chunks: list[RetrievedChunk], context_tokens: int) -> bool:
@@ -112,6 +161,8 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
         provider: str | None = None,
         model: str | None = None,
         prompt_template: str | None = None,
+        context_text: str | None = None,
+        context_filename: str | None = None,
     ) -> AnswerResult:
         """Handle generate for the Open A I Compatible Answer Generator instance."""
         start = time.time()
@@ -120,6 +171,8 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
             retrieved_chunks=retrieved_chunks,
             history=history,
             prompt_template=prompt_template,
+            context_text=context_text,
+            context_filename=context_filename,
         )
         client = get_llm_client(provider=provider or settings.llm_provider, model=model or settings.llm_model)
         response = client.chat(messages)
@@ -147,6 +200,8 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
         provider: str | None = None,
         model: str | None = None,
         prompt_template: str | None = None,
+        context_text: str | None = None,
+        context_filename: str | None = None,
     ) -> Generator[dict, None, AnswerResult]:
         """Stream generate for the Open A I Compatible Answer Generator instance."""
         start = time.time()
@@ -155,6 +210,8 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
             retrieved_chunks=retrieved_chunks,
             history=history,
             prompt_template=prompt_template,
+            context_text=context_text,
+            context_filename=context_filename,
         )
         sources = [source.__dict__ for source in self._sources(retrieved_chunks)]
         yield {"event": "sources", "data": sources}

backend/app/infrastructure/perception/base_event_store.py

@@ -0,0 +1,39 @@
+"""Abstract base class for regulatory event stores."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+
+class BaseEventStore(ABC):
+    """Port interface for regulatory event persistence."""
+
+    @abstractmethod
+    def all(self) -> list[dict]:
+        """Return all events, most-recent first."""
+
+    @abstractmethod
+    def get(self, event_id: str) -> dict | None:
+        """Return a single event by ID, or None."""
+
+    @abstractmethod
+    def filter(
+        self,
+        *,
+        source: str | None = None,
+        impact_level: str | None = None,
+        limit: int = 50,
+    ) -> list[dict]:
+        """Return filtered events sorted by published_at descending."""
+
+    @abstractmethod
+    def stats(self) -> dict:
+        """Return {total, high_impact, medium_impact, low_impact, recent_90d}."""
+
+    @abstractmethod
+    def upsert(self, event: dict) -> None:
+        """Insert or update an event record."""
+
+    @abstractmethod
+    def get_by_standard_code(self, standard_code: str) -> dict | None:
+        """Return the most-recent event with matching standard_code, or None."""

backend/app/infrastructure/perception/crawlers/_utils.py

@@ -0,0 +1,43 @@
+"""Shared utility functions for crawlers."""
+
+from __future__ import annotations
+
+import re
+from datetime import date
+
+
+def parse_date(text: str) -> str:
+    """Return YYYY-MM-DD from common Chinese date formats, or today's date."""
+    text = text.strip()
+    if not text:
+        return date.today().isoformat()
+    m = re.search(r"(\d{4})[/-](\d{1,2})[/-](\d{1,2})", text)
+    if m:
+        try:
+            return date(int(m.group(1)), int(m.group(2)), int(m.group(3))).isoformat()
+        except ValueError:
+            pass
+    m2 = re.search(r"(\d{4})年(\d{1,2})月(\d{1,2})日?", text)
+    if m2:
+        try:
+            return date(int(m2.group(1)), int(m2.group(2)), int(m2.group(3))).isoformat()
+        except ValueError:
+            pass
+    return date.today().isoformat()
+
+
+def extract_tags(standard_code: str, title: str) -> list[str]:
+    """Derive simple keyword tags from standard code and title."""
+    tags: list[str] = []
+    code_upper = standard_code.upper()
+    if "GB" in code_upper:
+        tags.append("国家标准")
+    if "/T" in code_upper:
+        tags.append("推荐性")
+    else:
+        tags.append("强制性")
+    keywords = ["电动", "安全", "自动驾驶", "充电", "智能网联", "碰撞", "排放", "网络安全"]
+    for kw in keywords:
+        if kw in title:
+            tags.append(kw)
+    return tags[:5]

backend/app/infrastructure/perception/crawlers/base.py

@@ -0,0 +1,32 @@
+"""Shared contracts for regulatory source crawlers."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+
+
+@dataclass
+class RawEvent:
+    """Raw regulatory event returned by a crawler before enrichment."""
+
+    source: str
+    source_label: str
+    standard_code: str
+    title: str
+    summary: str
+    full_text_url: str
+    status: str           # 'enacted' | 'draft' | 'consultation'
+    published_at: str     # YYYY-MM-DD string
+    effective_at: str | None
+    category: str
+    tags: list[str] = field(default_factory=list)
+    raw_text: str = ""    # full crawled text for hashing + LLM
+
+
+class BaseCrawler(ABC):
+    """Abstract regulatory source crawler."""
+
+    @abstractmethod
+    def fetch(self, limit: int = 50) -> list[RawEvent]:
+        """Fetch up to `limit` recent events from the data source."""

backend/app/infrastructure/perception/crawlers/catarc_crawler.py

@@ -0,0 +1,83 @@
+"""Crawler for CATARC automotive standard catalogue."""
+
+from __future__ import annotations
+
+from urllib.parse import urljoin
+
+import httpx
+from bs4 import BeautifulSoup
+from loguru import logger
+
+from app.infrastructure.perception.crawlers.base import BaseCrawler, RawEvent
+from ._utils import extract_tags, parse_date
+
+_BASE_URL = "https://www.catarc.org.cn/bzzxd/qcbz/index.html"
+_HOST = "https://www.catarc.org.cn"
+
+_STATUS_MAP = {
+    "现行": "enacted",
+    "即将实施": "enacted",
+    "废止": "enacted",
+    "征求意见": "consultation",
+    "报批": "draft",
+}
+
+
+class CatarcCrawler(BaseCrawler):
+    """Scrape the CATARC automotive standard list page."""
+
+    def fetch(self, limit: int = 50) -> list[RawEvent]:
+        events: list[RawEvent] = []
+        page = 1
+        max_pages = max(10, limit)
+        while len(events) < limit and page <= max_pages:
+            url = f"{_BASE_URL}?page={page}"
+            try:
+                resp = httpx.get(url, timeout=30, follow_redirects=True)
+                resp.raise_for_status()
+            except Exception as exc:
+                logger.warning("CATARC fetch failed page={} err={}", page, exc)
+                break
+
+            soup = BeautifulSoup(resp.text, "lxml")
+            rows = soup.select("table tr")
+            if not rows:
+                break
+
+            batch: list[RawEvent] = []
+            for row in rows:
+                cells = row.find_all("td")
+                if len(cells) < 3:
+                    continue
+                link = cells[0].find("a")
+                standard_code = link.get_text(strip=True) if link else cells[0].get_text(strip=True)
+                title = cells[1].get_text(strip=True) if len(cells) > 1 else standard_code
+                date_text = cells[2].get_text(strip=True) if len(cells) > 2 else ""
+                published_at = parse_date(date_text)
+                status_text = cells[3].get_text(strip=True) if len(cells) > 3 else ""
+                status = _STATUS_MAP.get(status_text, "enacted")
+                detail_url = urljoin(_HOST, link["href"]) if link and link.get("href") else url
+                raw_text = f"{standard_code} {title}"
+                batch.append(RawEvent(
+                    source="CATARC",
+                    source_label="全国汽车标准化技术委员会",
+                    standard_code=standard_code,
+                    title=title,
+                    summary=title,
+                    full_text_url=detail_url,
+                    status=status,
+                    published_at=published_at,
+                    effective_at=None,
+                    category="汽车标准",
+                    tags=extract_tags(standard_code, title),
+                    raw_text=raw_text,
+                ))
+
+            if not batch:
+                break
+            events.extend(batch)
+            page += 1
+
+        return events[:limit]
+
+

backend/app/infrastructure/perception/crawlers/eurlex_crawler.py

@@ -0,0 +1,117 @@
+"""Crawler for EUR-Lex RSS feeds covering EU AI Act and automotive regulations."""
+
+from __future__ import annotations
+
+import re
+from email.utils import parsedate_to_datetime
+
+import httpx
+from bs4 import BeautifulSoup
+from loguru import logger
+
+from app.infrastructure.perception.crawlers.base import BaseCrawler, RawEvent
+from ._utils import parse_date
+
+_EURLEX_RSS_URLS = [
+    "https://eur-lex.europa.eu/rss-feed/OJ-L.rss",
+]
+
+_AUTOMOTIVE_KEYWORDS = [
+    "vehicle", "automotive", "motor", "tyre", "emission", "ADAS", "autonomous",
+    "AI Act", "artificial intelligence", "cybersecurity", "software update",
+    "R155", "R156", "汽车", "车辆",
+]
+
+
+_AUTOMOTIVE_KEYWORDS_LOWER = [kw.lower() for kw in _AUTOMOTIVE_KEYWORDS]
+
+
+def _is_automotive_relevant(title: str, description: str) -> bool:
+    combined = (title + " " + description).lower()
+    return any(kw in combined for kw in _AUTOMOTIVE_KEYWORDS_LOWER)
+
+
+def _extract_celex(url: str) -> str:
+    m = re.search(r"CELEX[:/]([0-9A-Z]+)", url)
+    return m.group(1) if m else ""
+
+
+def _parse_rss_date(rfc2822: str) -> str:
+    try:
+        dt = parsedate_to_datetime(rfc2822)
+        return dt.date().isoformat()
+    except Exception:
+        return parse_date(rfc2822)
+
+
+class EurlexCrawler(BaseCrawler):
+    """Fetch automotive-relevant EU regulations from EUR-Lex RSS feeds."""
+
+    def fetch(self, limit: int = 50) -> list[RawEvent]:
+        events: list[RawEvent] = []
+        for rss_url in _EURLEX_RSS_URLS:
+            if len(events) >= limit:
+                break
+            try:
+                resp = httpx.get(rss_url, timeout=30, follow_redirects=True)
+                resp.raise_for_status()
+            except Exception as exc:
+                logger.warning("EUR-Lex RSS fetch failed url={} err={}", rss_url, exc)
+                continue
+
+            soup = BeautifulSoup(resp.content, "lxml-xml")
+            for item in soup.find_all("item"):
+                if len(events) >= limit:
+                    break
+                title_tag = item.find("title")
+                title = title_tag.get_text(strip=True) if title_tag else ""
+                desc_tag = item.find("description")
+                description = desc_tag.get_text(strip=True) if desc_tag else ""
+                link_tag = item.find("link")
+                link = link_tag.get_text(strip=True) if link_tag else ""
+                pub_date_tag = item.find("pubDate")
+                pub_date = pub_date_tag.get_text(strip=True) if pub_date_tag else ""
+
+                if not _is_automotive_relevant(title, description):
+                    continue
+
+                celex = _extract_celex(link)
+                standard_code = celex if celex else title[:60]
+                published_at = _parse_rss_date(pub_date) if pub_date else ""
+
+                events.append(RawEvent(
+                    source="EUR-Lex",
+                    source_label="欧盟官方公报",
+                    standard_code=standard_code,
+                    title=title,
+                    summary=description[:500],
+                    full_text_url=link,
+                    status="enacted",
+                    published_at=published_at,
+                    effective_at=None,
+                    category="EU法规",
+                    tags=_extract_eurlex_tags(title, description),
+                    raw_text=f"{title}\n{description}",
+                ))
+
+        return events[:limit]
+
+
+def _extract_eurlex_tags(title: str, description: str) -> list[str]:
+    combined = title + " " + description
+    tag_map = {
+        "AI Act": "EU AI Act",
+        "artificial intelligence": "EU AI Act",
+        "R155": "UN R155",
+        "R156": "UN R156",
+        "cybersecurity": "网络安全",
+        "emission": "排放",
+        "autonomous": "自动驾驶",
+        "ADAS": "ADAS",
+    }
+    combined_lower = combined.lower()
+    tags = []
+    for kw, tag in tag_map.items():
+        if kw.lower() in combined_lower:
+            tags.append(tag)
+    return tags[:5]

backend/app/infrastructure/perception/crawlers/guobiao_crawler.py

@@ -0,0 +1,92 @@
+"""Crawlers for the 国标委 (SAMR) standard information platform."""
+
+from __future__ import annotations
+
+import httpx
+from loguru import logger
+
+from app.infrastructure.perception.crawlers.base import BaseCrawler, RawEvent
+from ._utils import extract_tags, parse_date
+
+_BASE_URL = "https://openstd.samr.gov.cn/bzgk/std/std_list_type"
+_HEADERS = {"User-Agent": "Mozilla/5.0 (compatible; RegulatoryBot/1.0)"}
+
+
+def _fetch_page(std_type: int, page: int, page_size: int) -> list[dict]:
+    params = {
+        "p.p1": std_type,
+        "p.p2": "车",
+        "p.p90": "circulation_date",
+        "p.p91": "desc",
+        "p.p6": page,
+        "p.p7": page_size,
+    }
+    try:
+        resp = httpx.get(_BASE_URL, params=params, headers=_HEADERS, timeout=30)
+        resp.raise_for_status()
+        data = resp.json()
+        return data.get("rows", []) or []
+    except Exception as exc:
+        logger.warning("国标委 fetch failed type={} page={} err={}", std_type, page, exc)
+        return []
+
+
+def _row_to_raw_event(row: dict, source_label: str) -> RawEvent:
+    standard_code = row.get("std_code", "")
+    title = row.get("std_name", standard_code)
+    published_at = parse_date(row.get("release_date", ""))
+    effective_at_raw = row.get("implement_date", "")
+    effective_at = parse_date(effective_at_raw) if effective_at_raw else None
+    status_text = row.get("std_status", "")
+    if "征求意见" in status_text:
+        status = "consultation"
+    elif "报批" in status_text or "草案" in status_text:
+        status = "draft"
+    else:
+        status = "enacted"
+    return RawEvent(
+        source="国标委",
+        source_label=source_label,
+        standard_code=standard_code,
+        title=title,
+        summary=title,
+        full_text_url=f"https://openstd.samr.gov.cn/bzgk/std/detail?id={row.get('id', '')}",
+        status=status,
+        published_at=published_at,
+        effective_at=effective_at,
+        category=row.get("std_type", "国家标准"),
+        tags=extract_tags(standard_code, title),
+        raw_text=f"{standard_code} {title}",
+    )
+
+
+class GuobiaoMandatoryCrawler(BaseCrawler):
+    """Fetch mandatory national standards (强制性) related to vehicles."""
+
+    def fetch(self, limit: int = 50) -> list[RawEvent]:
+        events: list[RawEvent] = []
+        page = 1
+        max_pages = max(10, limit)
+        while len(events) < limit and page <= max_pages:
+            rows = _fetch_page(std_type=1, page=page, page_size=20)
+            if not rows:
+                break
+            events.extend(_row_to_raw_event(r, "国标委·强制性") for r in rows)
+            page += 1
+        return events[:limit]
+
+
+class GuobiaoRecommendedCrawler(BaseCrawler):
+    """Fetch recommended national standards (推荐性) related to vehicles."""
+
+    def fetch(self, limit: int = 50) -> list[RawEvent]:
+        events: list[RawEvent] = []
+        page = 1
+        max_pages = max(10, limit)
+        while len(events) < limit and page <= max_pages:
+            rows = _fetch_page(std_type=2, page=page, page_size=20)
+            if not rows:
+                break
+            events.extend(_row_to_raw_event(r, "国标委·推荐性") for r in rows)
+            page += 1
+        return events[:limit]

backend/app/infrastructure/perception/llm_pipeline.py

@@ -0,0 +1,241 @@
+"""LLM-driven pipeline for regulatory event enrichment."""
+
+from __future__ import annotations
+
+import json
+import math
+from typing import Any
+
+from loguru import logger
+
+from app.config.settings import settings
+from app.infrastructure.embedding.openai_compatible_embedding_provider import (
+    OpenAICompatibleEmbeddingProvider,
+)
+from app.services.llm.llm_factory import get_llm_client
+
+_EXTRACT_SYSTEM = (
+    "You are a regulatory compliance expert specialising in automotive standards "
+    "(GB, UN-ECE, ISO, EU). Extract structured information from regulation text. "
+    "Return valid JSON only — no markdown fences, no extra keys."
+)
+
+_ASSESS_SYSTEM = (
+    "You are an automotive compliance analyst. Given a regulation and related document excerpts, "
+    "identify which documents are affected and what actions are required. "
+    "Return a JSON array only."
+)
+
+_DIFF_SYSTEM = (
+    "You are a regulatory change analyst. Given an old and new version of a regulation paragraph, "
+    "classify the type of change and summarise it. "
+    "Return JSON only: {\"change_type\": \"tightened|relaxed|added|removed\", \"summary\": \"...\"}"
+)
+
+_SIMILARITY_THRESHOLD = 0.85
+
+
+def _cosine(a: list[float], b: list[float]) -> float:
+    dot = sum(x * y for x, y in zip(a, b))
+    norm_a = math.sqrt(sum(x * x for x in a))
+    norm_b = math.sqrt(sum(x * x for x in b))
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+    return dot / (norm_a * norm_b)
+
+
+def _llm_json(client: Any, messages: list[dict]) -> Any:
+    """Call LLM and parse JSON response; return None on failure."""
+    try:
+        resp = client.chat(messages)
+        text = (resp.content or "").strip()
+        if text.startswith("```"):
+            text = text.split("```")[1]
+            if text.startswith("json"):
+                text = text[4:]
+        return json.loads(text)
+    except Exception as exc:
+        logger.warning("LLM JSON parse failed: {}", exc)
+        return None
+
+
+class LlmPipeline:
+    """Three-step enrichment pipeline for crawled regulatory events."""
+
+    def __init__(self) -> None:
+        self._client = get_llm_client(
+            provider=settings.llm_provider,
+            model=settings.llm_model,
+        )
+        self._embedder = OpenAICompatibleEmbeddingProvider()
+
+    # ------------------------------------------------------------------
+    # Step 1: Structure extraction
+    # ------------------------------------------------------------------
+
+    def extract_structure(self, event: dict) -> dict:
+        """Extract obligations, deadlines, scope, penalties, impact_level from event text."""
+        prompt = f"""Extract structured compliance information from this regulation:
+
+Standard: {event.get('standard_code', '')}
+Title: {event.get('title', '')}
+Source: {event.get('source_label', '')}
+Summary: {event.get('summary', '')}
+Tags: {', '.join(event.get('tags') or [])}
+
+Return JSON with exactly these keys:
+{{
+  "obligations": [{{"text": "...", "deontic": "must|shall|may|prohibited", "subject": "...", "object": "...", "condition": ""}}],
+  "deadlines": [{{"date": "YYYY-MM-DD or null", "description": "..."}}],
+  "scope": "one sentence describing who/what this applies to",
+  "penalties": "one sentence on consequences of non-compliance, or null",
+  "impact_level": "high|medium|low"
+}}"""
+
+        messages = [
+            {"role": "system", "content": _EXTRACT_SYSTEM},
+            {"role": "user", "content": prompt},
+        ]
+        result = _llm_json(self._client, messages)
+        if not isinstance(result, dict):
+            return {
+                "obligations": [],
+                "deadlines": [],
+                "scope": "",
+                "penalties": "",
+                "impact_level": "medium",
+            }
+        return result
+
+    # ------------------------------------------------------------------
+    # Step 2: Impact assessment
+    # ------------------------------------------------------------------
+
+    def assess_impact(self, event: dict, retrieval_service: Any) -> list[dict]:
+        """Use RAG to find affected documents and generate recommendations."""
+        obligations = event.get("obligations") or []
+        obligation_texts = " ".join(o.get("text", "") for o in obligations[:3])
+        query = f"{event.get('standard_code', '')} {event.get('title', '')} {obligation_texts}"
+
+        try:
+            chunks = retrieval_service.retrieve(query=query, top_k=5)
+        except Exception as exc:
+            logger.warning("RAG retrieval failed: {}", exc)
+            return []
+
+        if not chunks:
+            return []
+
+        seen: set[str] = set()
+        doc_excerpts: list[dict] = []
+        for chunk in chunks:
+            if chunk.doc_id not in seen:
+                seen.add(chunk.doc_id)
+                doc_excerpts.append({
+                    "doc_id": chunk.doc_id,
+                    "doc_name": chunk.doc_title,
+                    "score": round(float(chunk.score if chunk.score is not None else 0), 4),
+                    "snippet": (chunk.text or "")[:300],
+                    "clause": getattr(chunk, "section_title", "") or "",
+                })
+
+        context = "\n".join(
+            f"[{d['doc_name']} {d['clause']}] score={d['score']}: {d['snippet']}"
+            for d in doc_excerpts
+        )
+        prompt = f"""Regulation: {event.get('standard_code')} — {event.get('title')}
+Obligations: {obligation_texts or event.get('summary', '')}
+
+Affected documents found in knowledge base:
+{context}
+
+For each document, assess impact and recommend action. Return JSON array:
+[{{"doc_id":"...","doc_name":"...","score":0.0,"key_clauses":"...","recommendation":"one sentence action"}}]"""
+
+        messages = [
+            {"role": "system", "content": _ASSESS_SYSTEM},
+            {"role": "user", "content": prompt},
+        ]
+        result = _llm_json(self._client, messages)
+        if isinstance(result, list):
+            score_map = {d["doc_id"]: d["score"] for d in doc_excerpts}
+            for item in result:
+                if isinstance(item, dict) and item.get("doc_id") in score_map:
+                    item["score"] = score_map[item["doc_id"]]
+            return result
+        return doc_excerpts
+
+    # ------------------------------------------------------------------
+    # Step 3: Semantic diff
+    # ------------------------------------------------------------------
+
+    def compute_diff(self, old_text: str, new_text: str) -> dict:
+        """Compare old and new regulation text; return changed sections and summary."""
+        old_paras = [p.strip() for p in old_text.split("\n") if p.strip()]
+        new_paras = [p.strip() for p in new_text.split("\n") if p.strip()]
+
+        if not old_paras or not new_paras:
+            return {"changed_sections": [], "change_summary": "No comparable text."}
+
+        all_paras = old_paras + new_paras
+        try:
+            all_embeddings = self._embedder.embed_texts(all_paras)
+        except Exception as exc:
+            logger.warning("Embedding for diff failed: {}", exc)
+            return {"changed_sections": [], "change_summary": "Diff unavailable (embedding error)."}
+
+        old_embeddings = all_embeddings[: len(old_paras)]
+        new_embeddings = all_embeddings[len(old_paras):]
+
+        changed_sections: list[dict] = []
+        max_len = max(len(old_paras), len(new_paras))
+
+        for i in range(max_len):
+            if i >= len(old_paras):
+                # New paragraph added
+                changed_sections.append({
+                    "old_text": "",
+                    "new_text": new_paras[i][:300],
+                    "similarity": 0.0,
+                    "change_type": "added",
+                    "summary": "New paragraph added.",
+                })
+                continue
+            if i >= len(new_paras):
+                # Old paragraph removed
+                changed_sections.append({
+                    "old_text": old_paras[i][:300],
+                    "new_text": "",
+                    "similarity": 0.0,
+                    "change_type": "removed",
+                    "summary": "Paragraph removed.",
+                })
+                continue
+            # Both exist — compare via embeddings
+            sim = _cosine(old_embeddings[i], new_embeddings[i])
+            if sim < _SIMILARITY_THRESHOLD:
+                messages = [
+                    {"role": "system", "content": _DIFF_SYSTEM},
+                    {"role": "user", "content": f"OLD: {old_paras[i][:500]}\nNEW: {new_paras[i][:500]}"},
+                ]
+                classification = _llm_json(self._client, messages) or {}
+                changed_sections.append({
+                    "old_text": old_paras[i][:300],
+                    "new_text": new_paras[i][:300],
+                    "similarity": round(sim, 3),
+                    "change_type": classification.get("change_type", "modified"),
+                    "summary": classification.get("summary", ""),
+                })
+
+        if not changed_sections:
+            change_summary = "No substantive changes detected between versions."
+        else:
+            types = [s["change_type"] for s in changed_sections]
+            change_summary = (
+                f"{len(changed_sections)} paragraph(s) changed: "
+                + ", ".join(f"{t}" for t in set(types))
+                + ". "
+                + (changed_sections[0].get("summary", "") if changed_sections else "")
+            )
+
+        return {"changed_sections": changed_sections, "change_summary": change_summary}

backend/app/infrastructure/perception/mock_event_store.py

@@ -4,6 +4,8 @@ from __future__ import annotations
 
 from typing import Any
 
+from app.infrastructure.perception.base_event_store import BaseEventStore
+
 MOCK_EVENTS: list[dict[str, Any]] = [
     # ------------------------------------------------------------------ HIGH
     {
@@ -379,18 +381,18 @@ MOCK_EVENTS: list[dict[str, Any]] = [
     },
 ]
 
-# Index for fast lookup
+class MockEventStore(BaseEventStore):
-_EVENT_INDEX: dict[str, dict] = {e["id"]: e for e in MOCK_EVENTS}
-
-
-class MockEventStore:
     """In-memory mock store for regulatory events."""
 
+    def __init__(self) -> None:
+        self._events: list[dict] = [dict(e) for e in MOCK_EVENTS]
+        self._index: dict[str, dict] = {e["id"]: e for e in self._events}
+
     def all(self) -> list[dict]:
-        return list(MOCK_EVENTS)
+        return list(self._events)
 
     def get(self, event_id: str) -> dict | None:
-        return _EVENT_INDEX.get(event_id)
+        return self._index.get(event_id)
 
     def filter(
         self,
@@ -399,23 +401,39 @@ class MockEventStore:
         impact_level: str | None = None,
         limit: int = 50,
     ) -> list[dict]:
-        events = list(MOCK_EVENTS)
+        events = list(self._events)
         if source:
             events = [e for e in events if e["source"] == source]
         if impact_level:
             events = [e for e in events if e["impact_level"] == impact_level]
-        events.sort(key=lambda e: e["published_at"], reverse=True)
+        events.sort(key=lambda e: e.get("published_at") or "", reverse=True)
         return events[:limit]
 
     def stats(self) -> dict:
         from datetime import date, timedelta
 
-        events = MOCK_EVENTS
+        events = self._events
         cutoff = (date.today() - timedelta(days=90)).isoformat()
         return {
             "total": len(events),
             "high_impact": sum(1 for e in events if e["impact_level"] == "high"),
             "medium_impact": sum(1 for e in events if e["impact_level"] == "medium"),
             "low_impact": sum(1 for e in events if e["impact_level"] == "low"),
-            "recent_90d": sum(1 for e in events if e["published_at"] >= cutoff),
+            "recent_90d": sum(1 for e in events if (e.get("published_at") or "") >= cutoff),
         }
+
+    def upsert(self, event: dict) -> None:
+        """Insert or update event in the in-memory list (used in tests)."""
+        existing = self._index.get(event["id"])
+        if existing:
+            existing.update(event)
+        else:
+            self._events.append(event)
+            self._index[event["id"]] = event
+
+    def get_by_standard_code(self, standard_code: str) -> dict | None:
+        """Return most-recent event with matching standard_code."""
+        matches = [e for e in self._events if e.get("standard_code") == standard_code]
+        if not matches:
+            return None
+        return max(matches, key=lambda e: e.get("published_at", ""))

backend/app/infrastructure/perception/postgres_event_store.py

@@ -0,0 +1,225 @@
+"""PostgreSQL-backed regulatory event store."""
+
+from __future__ import annotations
+
+import json
+from contextlib import contextmanager
+from datetime import UTC, date, datetime, timedelta
+from typing import Any
+
+import psycopg2
+import psycopg2.extras
+from psycopg2.pool import ThreadedConnectionPool
+
+from app.config.settings import settings
+from app.infrastructure.perception.base_event_store import BaseEventStore
+
+_CREATE_TABLE = """
+CREATE TABLE IF NOT EXISTS regulation_events (
+    id               TEXT PRIMARY KEY,
+    source           TEXT NOT NULL,
+    source_label     TEXT,
+    standard_code    TEXT NOT NULL,
+    title            TEXT NOT NULL,
+    summary          TEXT,
+    full_text_url    TEXT,
+    status           TEXT,
+    impact_level     TEXT,
+    published_at     DATE,
+    effective_at     DATE,
+    category         TEXT,
+    tags             TEXT[],
+    obligations      JSONB,
+    deadlines        JSONB,
+    scope            TEXT,
+    penalties        TEXT,
+    content_hash     TEXT,
+    previous_hash    TEXT,
+    change_summary   TEXT,
+    changed_sections JSONB,
+    affected_docs    JSONB,
+    crawled_at       TIMESTAMPTZ DEFAULT now(),
+    processed_at     TIMESTAMPTZ,
+    raw_storage_key  TEXT
+);
+CREATE INDEX IF NOT EXISTS reg_events_source_date
+    ON regulation_events (source, published_at DESC);
+CREATE INDEX IF NOT EXISTS reg_events_impact_date
+    ON regulation_events (impact_level, published_at DESC);
+"""
+
+_ALL_COLUMNS = (
+    "id", "source", "source_label", "standard_code", "title", "summary",
+    "full_text_url", "status", "impact_level", "published_at", "effective_at",
+    "category", "tags", "obligations", "deadlines", "scope", "penalties",
+    "content_hash", "previous_hash", "change_summary", "changed_sections",
+    "affected_docs", "crawled_at", "processed_at", "raw_storage_key",
+)
+
+
+def _row_to_dict(row: dict[str, Any]) -> dict:
+    """Convert a psycopg2 RealDictRow to a plain dict with serialized JSON fields."""
+    d = dict(row)
+    for field in ("obligations", "deadlines", "changed_sections", "affected_docs"):
+        val = d.get(field)
+        if isinstance(val, str):
+            d[field] = json.loads(val)
+    for date_field in ("published_at", "effective_at"):
+        val = d.get(date_field)
+        if isinstance(val, datetime):
+            d[date_field] = val.date().isoformat()
+        elif isinstance(val, date):
+            d[date_field] = val.isoformat()
+    for ts_field in ("crawled_at", "processed_at"):
+        val = d.get(ts_field)
+        if isinstance(val, datetime):
+            d[ts_field] = val.isoformat()
+    return d
+
+
+class PostgresEventStore(BaseEventStore):
+    """Regulatory event store backed by PostgreSQL."""
+
+    def __init__(self) -> None:
+        self._pool = ThreadedConnectionPool(
+            minconn=1,
+            maxconn=5,
+            host=settings.postgres_host,
+            port=settings.postgres_port,
+            user=settings.postgres_user,
+            password=settings.postgres_password,
+            dbname=settings.postgres_db,
+        )
+        self._ensure_schema()
+
+    def _ensure_schema(self) -> None:
+        with self._conn() as conn:
+            try:
+                with conn.cursor() as cur:
+                    cur.execute(_CREATE_TABLE)
+                conn.commit()
+            except Exception:
+                conn.rollback()
+                raise
+
+    @contextmanager
+    def _conn(self):
+        conn = None
+        try:
+            conn = self._pool.getconn()
+            yield conn
+        finally:
+            if conn is not None:
+                self._pool.putconn(conn)
+
+    def all(self) -> list[dict]:
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    "SELECT * FROM regulation_events ORDER BY published_at DESC NULLS LAST"
+                )
+                return [_row_to_dict(r) for r in cur.fetchall()]
+
+    def get(self, event_id: str) -> dict | None:
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    "SELECT * FROM regulation_events WHERE id = %s", (event_id,)
+                )
+                row = cur.fetchone()
+                return _row_to_dict(row) if row else None
+
+    def filter(
+        self,
+        *,
+        source: str | None = None,
+        impact_level: str | None = None,
+        limit: int = 50,
+    ) -> list[dict]:
+        conditions: list[str] = []
+        params: list[Any] = []
+        if source:
+            conditions.append("source = %s")
+            params.append(source)
+        if impact_level:
+            conditions.append("impact_level = %s")
+            params.append(impact_level)
+        where = ("WHERE " + " AND ".join(conditions)) if conditions else ""
+        params.append(limit)
+        sql = f"""
+            SELECT * FROM regulation_events
+            {where}
+            ORDER BY published_at DESC NULLS LAST
+            LIMIT %s
+        """
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(sql, params)
+                return [_row_to_dict(r) for r in cur.fetchall()]
+
+    def stats(self) -> dict:
+        cutoff = (date.today() - timedelta(days=90)).isoformat()
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute("SELECT COUNT(*) AS count FROM regulation_events")
+                total = (cur.fetchone() or {}).get("count", 0)
+                cur.execute(
+                    "SELECT COUNT(*) AS count FROM regulation_events WHERE impact_level = 'high'"
+                )
+                high = (cur.fetchone() or {}).get("count", 0)
+                cur.execute(
+                    "SELECT COUNT(*) AS count FROM regulation_events WHERE impact_level = 'medium'"
+                )
+                medium = (cur.fetchone() or {}).get("count", 0)
+                cur.execute(
+                    "SELECT COUNT(*) AS count FROM regulation_events WHERE published_at >= %s",
+                    (cutoff,),
+                )
+                recent = (cur.fetchone() or {}).get("count", 0)
+        return {
+            "total": int(total),
+            "high_impact": int(high),
+            "medium_impact": int(medium),
+            "recent_90d": int(recent),
+        }
+
+    def upsert(self, event: dict) -> None:
+        """Insert or update a regulation event."""
+        cols = [c for c in _ALL_COLUMNS if c in event]
+        placeholders = ", ".join(f"%({c})s" for c in cols)
+        updates = ", ".join(f"{c} = EXCLUDED.{c}" for c in cols if c != "id")
+        sql = f"""
+            INSERT INTO regulation_events ({', '.join(cols)})
+            VALUES ({placeholders})
+            ON CONFLICT (id) DO UPDATE SET {updates}
+        """
+        row: dict[str, Any] = {}
+        for c in cols:
+            val = event.get(c)
+            if c in ("obligations", "deadlines", "changed_sections", "affected_docs") and val is not None:
+                row[c] = json.dumps(val, ensure_ascii=False)
+            elif c == "tags" and isinstance(val, list):
+                row[c] = val
+            else:
+                row[c] = val
+        with self._conn() as conn:
+            try:
+                with conn.cursor() as cur:
+                    cur.execute(sql, row)
+                conn.commit()
+            except Exception:
+                conn.rollback()
+                raise
+
+    def get_by_standard_code(self, standard_code: str) -> dict | None:
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """SELECT * FROM regulation_events
+                       WHERE standard_code = %s
+                       ORDER BY published_at DESC NULLS LAST
+                       LIMIT 1""",
+                    (standard_code,),
+                )
+                row = cur.fetchone()
+                return _row_to_dict(row) if row else None

backend/app/infrastructure/session/redis_conversation_store.py

@@ -0,0 +1,169 @@
+"""Redis-backed conversation store for persistent chat sessions.
+
+Sessions are stored as JSON strings under the key `session:{session_id}`.
+The Redis TTL is refreshed on every write so active sessions stay alive.
+On expiry, `get_session` returns None — callers should create a new session.
+"""
+
+from __future__ import annotations
+
+import json
+import time
+import uuid
+from typing import Any
+
+from loguru import logger
+
+from app.domain.conversation import ConversationMessage, ConversationSession, ConversationStore
+
+
+class RedisConversationStore(ConversationStore):
+    """Store conversation sessions in Redis with automatic TTL expiry.
+
+    Each session is serialised as a JSON object at key ``session:{session_id}``.
+    The TTL is reset on every write so sessions stay alive as long as they are active.
+    """
+
+    # Prefix for all session keys to avoid collisions with other Redis consumers.
+    _PREFIX = "session:"
+
+    def __init__(self, *, redis_client: Any, timeout_seconds: int = 1800) -> None:
+        """Initialise the store with an existing Redis client and a TTL in seconds."""
+        self._redis = redis_client
+        self._ttl = timeout_seconds
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _key(self, session_id: str) -> str:
+        """Build the Redis key for a session."""
+        return f"{self._PREFIX}{session_id}"
+
+    def _serialise(self, session: ConversationSession) -> str:
+        """Serialise a ConversationSession to a JSON string."""
+        return json.dumps(
+            {
+                "session_id": session.session_id,
+                "created_at": session.created_at,
+                "updated_at": session.updated_at,
+                "metadata": session.metadata,
+                "messages": [
+                    {
+                        "role": msg.role,
+                        "content": msg.content,
+                        "timestamp": msg.timestamp,
+                        "sources": msg.sources,
+                    }
+                    for msg in session.messages
+                ],
+            },
+            ensure_ascii=False,
+        )
+
+    def _deserialise(self, raw: bytes | str) -> ConversationSession:
+        """Deserialise a JSON string back into a ConversationSession."""
+        data = json.loads(raw)
+        messages = [
+            ConversationMessage(
+                role=m["role"],
+                content=m["content"],
+                timestamp=m["timestamp"],
+                sources=m.get("sources", []),
+            )
+            for m in data.get("messages", [])
+        ]
+        session = ConversationSession(
+            session_id=data["session_id"],
+            created_at=data.get("created_at", 0),
+            updated_at=data.get("updated_at", 0),
+            metadata=data.get("metadata", {}),
+        )
+        session.messages = messages
+        return session
+
+    def _save(self, session: ConversationSession) -> None:
+        """Persist a session to Redis and refresh its TTL."""
+        self._redis.setex(self._key(session.session_id), self._ttl, self._serialise(session))
+
+    # ------------------------------------------------------------------
+    # ConversationStore protocol
+    # ------------------------------------------------------------------
+
+    def create_session(self, metadata: dict | None = None) -> ConversationSession:
+        """Create a new empty session and persist it immediately."""
+        now = int(time.time())
+        session = ConversationSession(
+            session_id=str(uuid.uuid4())[:8],
+            created_at=now,
+            updated_at=now,
+            metadata=metadata or {},
+        )
+        self._save(session)
+        return session
+
+    def get_session(self, session_id: str) -> ConversationSession | None:
+        """Return a session by ID, or None if it does not exist or has expired."""
+        raw = self._redis.get(self._key(session_id))
+        if raw is None:
+            return None
+        try:
+            return self._deserialise(raw)
+        except Exception:
+            logger.warning("Failed to deserialise session: {}", session_id)
+            return None
+
+    def save_message(
+        self,
+        session_id: str,
+        *,
+        role: str,
+        content: str,
+        sources: list[dict] | None = None,
+    ) -> ConversationSession | None:
+        """Append a message to a session and refresh its TTL."""
+        session = self.get_session(session_id)
+        if session is None:
+            return None
+        session.messages.append(
+            ConversationMessage(
+                role=role,
+                content=content,
+                timestamp=int(time.time()),
+                sources=sources or [],
+            )
+        )
+        session.updated_at = int(time.time())
+        self._save(session)
+        return session
+
+    def delete_session(self, session_id: str) -> bool:
+        """Delete a session. Returns True if it existed, False otherwise."""
+        deleted = self._redis.delete(self._key(session_id))
+        return bool(deleted)
+
+    def list_sessions(self) -> list[dict]:
+        """Return summary dicts for all live sessions visible in this Redis DB.
+
+        Note: KEYS is used for simplicity; replace with SCAN for large deployments.
+        """
+        pattern = f"{self._PREFIX}*"
+        keys = self._redis.keys(pattern)
+        result = []
+        for key in keys:
+            raw = self._redis.get(key)
+            if raw is None:
+                continue
+            try:
+                data = json.loads(raw)
+                result.append(
+                    {
+                        "session_id": data["session_id"],
+                        "message_count": len(data.get("messages", [])),
+                        "created_at": data.get("created_at", 0),
+                        "updated_at": data.get("updated_at", 0),
+                    }
+                )
+            except Exception:
+                continue
+        return result

backend/app/infrastructure/storage/minio_binary_store.py

@@ -41,6 +41,10 @@ class MinioDocumentBinaryStore(DocumentBinaryStore):
             raise FileNotFoundError(f"对象不存在: {object_name}")
         return data
 
+    def list_objects(self, prefix: str = "") -> list[str]:
+        """List object names in the bucket that start with the given prefix."""
+        return self.client.list_objects(prefix=prefix)
+
     def delete(self, object_name: str) -> None:
         """Handle delete for the Minio Document Binary Store instance."""
         if not self.client.delete_object(object_name):

backend/app/infrastructure/tasks/__init__.py

@@ -0,0 +1,5 @@
+"""Celery task definitions for background processing.
+
+This package exposes the shared Celery application instance and all
+registered task functions used by API routes to enqueue work.
+"""

backend/app/infrastructure/tasks/celery_app.py

@@ -0,0 +1,45 @@
+"""Shared Celery application instance for background task processing.
+
+All workers and enqueueing call sites import `celery_app` from this module
+so the broker/backend configuration stays in one place.
+"""
+
+from __future__ import annotations
+
+from celery import Celery
+
+from app.config.settings import settings
+
+
+def _redis_url() -> str:
+    """Return a Redis connection URL from application settings."""
+    if settings.redis_password:
+        return (
+            f"redis://:{settings.redis_password}@"
+            f"{settings.redis_host}:{settings.redis_port}/{settings.redis_db}"
+        )
+    return f"redis://{settings.redis_host}:{settings.redis_port}/{settings.redis_db}"
+
+
+_BROKER = _redis_url()
+_BACKEND = _redis_url()
+
+celery_app = Celery(
+    "compliance_hub",
+    broker=_BROKER,
+    backend=_BACKEND,
+    include=["app.infrastructure.tasks.document_tasks"],
+)
+
+celery_app.conf.update(
+    task_serializer="json",
+    result_serializer="json",
+    accept_content=["json"],
+    timezone="UTC",
+    enable_utc=True,
+    # Acknowledge task only after successful execution to avoid data loss.
+    task_acks_late=True,
+    task_reject_on_worker_lost=True,
+    # Keep results for 1 hour for status polling.
+    result_expires=3600,
+)

backend/app/infrastructure/tasks/document_tasks.py

@@ -0,0 +1,73 @@
+"""Celery tasks for document processing.
+
+Each task is a thin wrapper that retrieves the already-stored document
+binary and delegates to DocumentCommandService._process_document.
+The task does not accept raw file bytes — it reads them from the binary
+store using the doc_id, so the Celery message payload stays small.
+"""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from app.infrastructure.tasks.celery_app import celery_app
+
+
+@celery_app.task(
+    name="app.infrastructure.tasks.document_tasks.process_document_task",
+    bind=True,
+    max_retries=3,
+    default_retry_delay=30,
+    acks_late=True,
+)
+def process_document_task(
+    self,
+    doc_id: str,
+    file_name: str,
+    doc_name: str,
+    regulation_type: str,
+    version: str,
+    generate_summary: bool,
+    run_id: str | None = None,
+) -> dict:
+    """Parse, embed, and index a document that has already been stored.
+
+    The task reads the file binary from MinIO using doc_id so the Celery
+    message stays small. Retries up to 3 times with a 30-second delay on
+    transient infrastructure errors.
+    """
+    # Import inside the task function to avoid pickling issues and to ensure
+    # that each worker process initialises its own bootstrap singletons.
+    from app.shared.bootstrap import get_document_command_service, get_document_query_service
+
+    logger.info("process_document_task started: doc_id={}", doc_id)
+    try:
+        svc = get_document_command_service()
+        doc = get_document_query_service().get(doc_id)
+        if not doc:
+            raise ValueError(f"Document record not found: {doc_id}")
+
+        # Read the stored binary from MinIO — avoids passing raw bytes in the task message.
+        content = svc.binary_store.read(doc.object_name)
+
+        result = svc._process_document(
+            doc_id=doc_id,
+            file_name=file_name,
+            final_doc_name=doc_name,
+            content=content,
+            regulation_type=regulation_type,
+            version=version,
+            generate_summary=generate_summary,
+            run_id=run_id,
+        )
+        logger.info(
+            "process_document_task completed: doc_id={} status={} chunks={}",
+            doc_id, result.status, result.num_chunks,
+        )
+        return {"doc_id": result.doc_id, "status": result.status, "num_chunks": result.num_chunks}
+
+    except Exception as exc:
+        logger.exception("process_document_task failed: doc_id={}", doc_id)
+        # Retry on transient errors; permanent errors (bad file, parse failure)
+        # will exhaust retries and leave the document in FAILED state.
+        raise self.retry(exc=exc)

backend/app/infrastructure/vectorstore/cross_encoder_reranker.py

@@ -9,6 +9,7 @@ from loguru import logger
 
 from app.config.settings import settings
 from app.domain.retrieval import Reranker, RetrievedChunk
+from app.shared.model_usage_tracker import get_model_usage_tracker
 
 
 class OpenAICompatibleReranker(Reranker):
@@ -37,10 +38,26 @@ class OpenAICompatibleReranker(Reranker):
             scores = self._call_reranker(query, texts)
         except Exception as exc:
             logger.warning("Reranker call failed ({}), falling back to original order: {}", type(exc).__name__, exc)
+            # Record the failure so the Status page reflects real reranker health.
+            get_model_usage_tracker().record(
+                provider="reranker",
+                model=self._model,
+                success=False,
+                latency_ms=int((time.time() - start) * 1000),
+                error=str(exc),
+            )
             return chunks[:top_k]
 
         elapsed_ms = int((time.time() - start) * 1000)
         logger.debug("Reranker scored {} chunks in {}ms", len(chunks), elapsed_ms)
+        # TEI/Cohere-style rerank responses carry no token usage field —
+        # only call success/latency is meaningful for this role.
+        get_model_usage_tracker().record(
+            provider="reranker",
+            model=self._model,
+            success=True,
+            latency_ms=elapsed_ms,
+        )
 
         ranked = sorted(
             [(score, chunk) for score, chunk in zip(scores, chunks)],
@@ -54,22 +71,48 @@ class OpenAICompatibleReranker(Reranker):
         return result
 
     def _call_reranker(self, query: str, texts: list[str]) -> list[float]:
-        """Call the reranker API and return a score per text."""
+        """Call the reranker API and return a score per text.
+
+        Tries TEI format first (POST /rerank with model+texts), then falls back
+        to Cohere/OpenAI format (POST /v1/rerank with model+documents).
+        Both formats now include the model name, which most gateways require.
+        """
         headers = {"Content-Type": "application/json"}
         if self._api_key:
             headers["Authorization"] = f"Bearer {self._api_key}"
 
-        # Try TEI format first: POST /rerank
+        # TEI format: POST /rerank — include model name (required by gateway proxies)
-        payload = {"query": query, "texts": texts, "raw_scores": False, "return_text": False}
+        payload = {
+            "model": self._model,
+            "query": query,
+            "texts": texts,
+            "raw_scores": False,
+            "return_text": False,
+        }
         url = f"{self._base_url}/rerank"
         resp = requests.post(url, json=payload, headers=headers, timeout=self._timeout)
 
-        if resp.status_code == 404:
+        if resp.status_code in (404, 400):
-            # Fall back to Cohere / OpenAI-style: POST /v1/rerank
+            # Gateway returned an error — try Cohere/OpenAI-style format as fallback.
+            logger.debug(
+                "TEI rerank returned {} — trying Cohere format. Body: {}",
+                resp.status_code,
+                resp.text[:200],
+            )
             payload_v1 = {"model": self._model, "query": query, "documents": texts}
             url = f"{self._base_url}/v1/rerank"
             resp = requests.post(url, json=payload_v1, headers=headers, timeout=self._timeout)
 
+        if not resp.ok:
+            # Surface a clear error message so callers can log it meaningfully.
+            try:
+                err_body = resp.json()
+                err_msg = err_body.get("error", {}).get("message", resp.text[:200])
+            except Exception:
+                err_msg = resp.text[:200]
+            resp.raise_for_status()  # raises HTTPError with status code
+            raise ValueError(err_msg)  # unreachable but satisfies type checker
+
         resp.raise_for_status()
         data = resp.json()
 

backend/app/infrastructure/vectorstore/pass_through_reranker.py

@@ -0,0 +1,21 @@
+"""No-op reranker stub.
+
+Returns the original candidate list sliced to top_k.
+Replace with CrossEncoderReranker when a local cross-encoder model is available.
+"""
+from __future__ import annotations
+
+from app.domain.retrieval.models import RetrievedChunk
+from app.domain.retrieval.ports import Reranker
+
+
+class PassThroughReranker(Reranker):
+    """Pass-through reranker that preserves original retrieval order.
+
+    Acts as a placeholder for future cross-encoder reranking (e.g. ms-marco-MiniLM).
+    Wire via bootstrap.get_compliance_reranker() when ready to swap.
+    """
+
+    def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
+        """Return the first top_k chunks without reordering."""
+        return chunks[:top_k]

backend/app/schemas/rag.py

@@ -12,6 +12,11 @@ class RagChatRequest(BaseModel):
     top_k: int = 5
     session_id: Optional[str] = None
     filters: Optional[str] = None
+    # Optional document text to inject directly as LLM conversation context.
+    # When provided the document content is prepended to the query so the LLM
+    # can answer questions about it without requiring vector-store indexing.
+    context_text: Optional[str] = None
+    context_filename: Optional[str] = None
 
 
 class RetrievedDoc(BaseModel):

backend/app/services/llm/base_client.py

@@ -1,9 +1,16 @@
-"""Provide service-layer logic for base client."""
+"""Provide service-layer logic for base client.
+
+P0-0: ``LLMResponse`` now carries an optional ``tool_calls`` list so that any
+downstream code (agents, pipelines) can inspect and dispatch tool invocations
+without touching the provider-specific adapter layer.
+"""
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import List, Dict, Optional, Any
 from enum import Enum
+
+from app.services.llm.tool_types import Tool, ToolCall  # noqa: F401 – re-exported for callers
 # Keep provider-specific behavior explicit so debugging stays straightforward.
 
 
@@ -24,6 +31,8 @@ class LLMResponse:
     finish_reason: str = "stop"
     latency_ms: int = 0
     error: Optional[str] = None
+    # P0-0: populated when the model returns tool-call(s) instead of plain text.
+    tool_calls: List[ToolCall] = field(default_factory=list)
 
     @property
     def is_success(self) -> bool:
@@ -63,9 +72,19 @@ class BaseLLMClient(ABC):
         messages: List[Dict[str, str]],
         max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        tools: Optional[List["Tool"]] = None,
         **kwargs
     ) -> LLMResponse:
-        """Handle chat for the Base L L M Client instance."""
+        """Handle chat for the Base L L M Client instance.
+
+        Args:
+            messages: OpenAI-format message list.
+            max_tokens: Override config max_tokens when set.
+            temperature: Override config temperature when set.
+            tools: Optional list of Tool definitions to offer the model.
+                   When provided, the model may respond with tool_calls in the
+                   returned LLMResponse instead of (or in addition to) content.
+        """
         pass
 
     def complete(

backend/app/services/llm/deepseek_client.py

@@ -1,4 +1,8 @@
-"""Provide service-layer logic for deepseek client."""
+"""Provide service-layer logic for deepseek client.
+
+P0-0: ``chat()`` now accepts an optional ``tools`` list and parses ``tool_calls``
+from the model response so that callers can dispatch tool invocations.
+"""
 
 import time
 from typing import List, Dict, Optional
@@ -6,6 +10,7 @@ from loguru import logger
 import httpx
 
 from .base_client import BaseLLMClient, LLMResponse, LLMConfig, LLMProvider
+from .tool_types import Tool, ToolCall
 # Keep provider-specific behavior explicit so debugging stays straightforward.
 
 
@@ -46,13 +51,20 @@ class DeepSeekClient(BaseLLMClient):
         messages: List[Dict[str, str]],
         max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        tools: Optional[List[Tool]] = None,
         **kwargs
     ) -> LLMResponse:
-        """Handle chat for the Deep Seek Client instance."""
+        """Handle chat for the Deep Seek Client instance.
+
+        When ``tools`` is provided the request includes the tool definitions and
+        ``tool_choice="auto"``; any tool_calls returned by the model are parsed
+        into ``LLMResponse.tool_calls``.
+        """
+        import json
         start_time = time.time()
 
         try:
-            payload = {
+            payload: Dict = {
                 "model": self.config.model,
                 "messages": messages,
                 "max_tokens": max_tokens or self.config.max_tokens,
@@ -61,6 +73,11 @@ class DeepSeekClient(BaseLLMClient):
                 "stream": False
             }
 
+            # P0-0: inject tool definitions when provided.
+            if tools:
+                payload["tools"] = [t.to_openai_format() for t in tools]
+                payload["tool_choice"] = "auto"
+
             response = self._client.post("/chat/completions", json=payload)
             response.raise_for_status()
 
@@ -71,12 +88,24 @@ class DeepSeekClient(BaseLLMClient):
             choices = data.get("choices", [{}])
             message = choices[0].get("message", {})
 
+            # P0-0: parse tool_calls returned by the model.
+            raw_tool_calls = message.get("tool_calls") or []
+            parsed_tool_calls: List[ToolCall] = []
+            for tc in raw_tool_calls:
+                fn = tc.get("function", {})
+                try:
+                    args = json.loads(fn.get("arguments", "{}"))
+                except json.JSONDecodeError:
+                    args = {}
+                parsed_tool_calls.append(ToolCall(id=tc.get("id", ""), name=fn.get("name", ""), arguments=args))
+
             return LLMResponse(
-                content=message.get("content", ""),
+                content=message.get("content", "") or "",
                 model=data.get("model", self.config.model),
                 usage=data.get("usage", {}),
                 finish_reason=choices[0].get("finish_reason", "stop"),
-                latency_ms=latency_ms
+                latency_ms=latency_ms,
+                tool_calls=parsed_tool_calls,
             )
 
         except httpx.HTTPStatusError as e:

backend/app/services/llm/llm_factory.py

@@ -7,6 +7,8 @@ from functools import lru_cache
 from .base_client import BaseLLMClient, LLMConfig, LLMProvider, LLMResponse
 from .deepseek_client import DeepSeekClient
 from .qwen_client import QwenClient, QwenVLClient
+from .tracked_client import TrackedLLMClient
+from app.shared.model_usage_tracker import get_model_usage_tracker
 # Keep provider-specific behavior explicit so debugging stays straightforward.
 
 
@@ -45,7 +47,7 @@ class LLMFactory:
         max_tokens: int = 4096,
         temperature: float = 0.7,
         **kwargs
-    ) -> BaseLLMClient:
+    ) -> "BaseLLMClient | TrackedLLMClient":
         """Handle create for the L L M Factory instance."""
         provider_enum = self._parse_provider(provider)
 
@@ -76,11 +78,16 @@ class LLMFactory:
         # Keep provider-specific behavior explicit so debugging stays straightforward.
         client = self._create_client(config)
 
+        # Wrap in TrackedLLMClient so every call site (agentic, HyDE, perception,
+        # compliance, document summarization, main answer generation) is recorded
+        # without each of them needing to know about usage tracking.
+        tracked_client = TrackedLLMClient(client, get_model_usage_tracker())
+
         # Keep provider-specific behavior explicit so debugging stays straightforward.
-        LLMFactory._global_instances[cache_key] = client
+        LLMFactory._global_instances[cache_key] = tracked_client
 
         logger.info(f"LLM客户端创建成功并缓存: {provider} - {model}")
-        return client
+        return tracked_client
 
     def _parse_provider(self, provider: str) -> LLMProvider:
         """Handle parse provider for this module for the L L M Factory instance."""
@@ -137,7 +144,7 @@ class LLMFactory:
 
         return client_class(config)
 
-    def get_cached(self, provider: str, model: Optional[str] = None) -> Optional[BaseLLMClient]:
+    def get_cached(self, provider: str, model: Optional[str] = None) -> "BaseLLMClient | TrackedLLMClient | None":
         """Return cached for the L L M Factory instance."""
         provider_enum = self._parse_provider(provider)
         model = model or DEFAULT_MODELS.get(provider_enum)
@@ -200,7 +207,7 @@ def get_llm_client(
     provider: str = "qwen",
     model: Optional[str] = None,
     **kwargs
-) -> BaseLLMClient:
+) -> "BaseLLMClient | TrackedLLMClient":
     """Return llm client."""
     factory = get_llm_factory()
 

backend/app/services/llm/qwen_client.py

@@ -1,4 +1,8 @@
-"""Provide service-layer logic for qwen client."""
+"""Provide service-layer logic for qwen client.
+
+P0-0: ``chat()`` now accepts an optional ``tools`` list and parses ``tool_calls``
+from the model response so that callers can dispatch tool invocations.
+"""
 
 import time
 import json
@@ -7,6 +11,7 @@ from loguru import logger
 import httpx
 
 from .base_client import BaseLLMClient, LLMResponse, LLMConfig, LLMProvider
+from .tool_types import Tool, ToolCall
 # Keep provider-specific behavior explicit so debugging stays straightforward.
 
 
@@ -54,14 +59,20 @@ class QwenClient(BaseLLMClient):
         messages: List[Dict[str, str]],
         max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        tools: Optional[List[Tool]] = None,
         **kwargs
     ) -> LLMResponse:
-        """Handle chat for the Qwen Client instance."""
+        """Handle chat for the Qwen Client instance.
+
+        When ``tools`` is provided the request includes the tool definitions and
+        ``tool_choice="auto"``; any tool_calls returned by the model are parsed
+        into ``LLMResponse.tool_calls``.
+        """
         start_time = time.time()
 
         try:
             # Keep provider-specific behavior explicit so debugging stays straightforward.
-            payload = {
+            payload: Dict = {
                 "model": self.config.model,
                 "messages": messages,
                 "max_tokens": max_tokens or self.config.max_tokens,
@@ -70,6 +81,11 @@ class QwenClient(BaseLLMClient):
                 "stream": False
             }
 
+            # P0-0: inject tool definitions when provided.
+            if tools:
+                payload["tools"] = [t.to_openai_format() for t in tools]
+                payload["tool_choice"] = "auto"
+
             # Keep provider-specific behavior explicit so debugging stays straightforward.
             response = self._client.post("/chat/completions", json=payload)
             response.raise_for_status()
@@ -82,12 +98,24 @@ class QwenClient(BaseLLMClient):
             choices = data.get("choices", [{}])
             message = choices[0].get("message", {})
 
+            # P0-0: parse tool_calls returned by the model.
+            raw_tool_calls = message.get("tool_calls") or []
+            parsed_tool_calls: List[ToolCall] = []
+            for tc in raw_tool_calls:
+                fn = tc.get("function", {})
+                try:
+                    args = json.loads(fn.get("arguments", "{}"))
+                except json.JSONDecodeError:
+                    args = {}
+                parsed_tool_calls.append(ToolCall(id=tc.get("id", ""), name=fn.get("name", ""), arguments=args))
+
             return LLMResponse(
-                content=message.get("content", ""),
+                content=message.get("content", "") or "",
                 model=data.get("model", self.config.model),
                 usage=data.get("usage", {}),
                 finish_reason=choices[0].get("finish_reason", "stop"),
-                latency_ms=latency_ms
+                latency_ms=latency_ms,
+                tool_calls=parsed_tool_calls,
             )
 
         except httpx.HTTPStatusError as e:

backend/app/services/llm/tool_types.py

@@ -0,0 +1,80 @@
+"""Shared tool and tool-call type definitions for LLM function calling (P0-0).
+
+These types implement the OpenAI-compatible tool/function-calling interface so that
+any provider whose gateway supports the spec (DeepSeek, Qwen, etc.) can expose
+tools to the LLM and receive structured tool invocations in return.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class ToolCall:
+    """Represent a single tool invocation returned by the LLM.
+
+    The model fills in ``id``, ``name``, and ``arguments`` when it decides to call
+    a tool instead of (or in addition to) producing a text response.
+    """
+
+    # Unique identifier assigned by the model for this call.
+    id: str
+    # Name of the tool to invoke, matching the name registered in Tool.
+    name: str
+    # Parsed JSON arguments ready for direct use by the tool handler.
+    arguments: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ToolParameter:
+    """JSON-Schema–compatible parameter block for a tool definition."""
+
+    # Top-level schema type — always "object" for OpenAI-compatible tools.
+    type: str = "object"
+    # Map of parameter name → JSON-Schema property descriptor.
+    properties: dict[str, Any] = field(default_factory=dict)
+    # List of required parameter names.
+    required: list[str] = field(default_factory=list)
+
+
+@dataclass
+class Tool:
+    """Describe a callable tool that can be offered to the LLM.
+
+    Example usage::
+
+        search_tool = Tool(
+            name="search_regulations",
+            description="Search the compliance knowledge base for relevant regulation clauses.",
+            parameters=ToolParameter(
+                properties={"query": {"type": "string", "description": "Search query"}},
+                required=["query"],
+            ),
+        )
+        response = client.chat(messages, tools=[search_tool])
+    """
+
+    name: str
+    description: str
+    parameters: ToolParameter = field(default_factory=ToolParameter)
+
+    def to_openai_format(self) -> dict[str, Any]:
+        """Serialise this tool to the OpenAI-compatible function-calling schema.
+
+        The returned dict can be placed directly in the ``tools`` list of a chat
+        completions request without any further transformation.
+        """
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": {
+                    "type": self.parameters.type,
+                    "properties": self.parameters.properties,
+                    "required": self.parameters.required,
+                },
+            },
+        }

backend/app/services/llm/tracked_client.py

@@ -0,0 +1,86 @@
+"""Transparent decorator around BaseLLMClient implementations.
+
+Records per-call token usage, latency, and success/failure into a
+ModelUsageTracker without changing any caller-visible behavior.
+"""
+
+from __future__ import annotations
+
+import time
+from typing import Any, Dict, List, Optional
+
+from app.shared.model_usage_tracker import ModelUsageTracker
+
+from .base_client import BaseLLMClient, LLMResponse
+from .tool_types import Tool
+
+
+class TrackedLLMClient:
+    """Wrap any BaseLLMClient and record its usage into a ModelUsageTracker.
+
+    Deliberately does NOT subclass BaseLLMClient: that ABC declares abstract
+    methods (_init_client, get_available_models) with no meaningful override
+    here, and subclassing would make Python refuse to instantiate this class
+    ("Can't instantiate abstract class") before __getattr__ ever got a chance
+    to forward the call. Plain composition + __getattr__ delegation works
+    because every caller in this codebase only ever uses duck-typed access:
+    .chat(), .stream_chat(), .get_available_models(), .close(), .config.
+    """
+
+    def __init__(self, inner: BaseLLMClient, tracker: ModelUsageTracker) -> None:
+        """Store the wrapped client and the tracker to report into."""
+        self._inner = inner
+        self._tracker = tracker
+
+    def chat(
+        self,
+        messages: List[Dict[str, str]],
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        tools: Optional[List[Tool]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        """Delegate to the wrapped client's chat(), then record the outcome."""
+        start = time.time()
+        response = self._inner.chat(messages, max_tokens, temperature, tools, **kwargs)
+        # Key by the *configured* model, not response.model, so lookups driven
+        # by settings (llm_model / hyde_llm_model) always match what we recorded.
+        self._tracker.record(
+            provider=self._inner.config.provider.value,
+            model=self._inner.config.model,
+            success=response.is_success,
+            usage=response.usage,
+            latency_ms=int((time.time() - start) * 1000),
+            error=response.error,
+        )
+        return response
+
+    def stream_chat(self, messages: List[Dict[str, str]], *args: Any, **kwargs: Any):
+        """Delegate to the wrapped client's stream_chat(), recording call outcome only.
+
+        Token usage is NOT recorded here: none of the current provider
+        stream_chat() implementations parse a trailing usage chunk from the
+        gateway (see the design doc's Known Limitations), so accumulating a
+        token count here would silently be wrong. Only call success/failure
+        and latency are tracked for streaming calls.
+        """
+        start = time.time()
+        error: Optional[str] = None
+        try:
+            for chunk in self._inner.stream_chat(messages, *args, **kwargs):
+                yield chunk
+        except Exception as exc:  # noqa: BLE001 - report, then re-raise unchanged
+            error = str(exc)
+            raise
+        finally:
+            self._tracker.record(
+                provider=self._inner.config.provider.value,
+                model=self._inner.config.model,
+                success=error is None,
+                latency_ms=int((time.time() - start) * 1000),
+                error=error,
+            )
+
+    def __getattr__(self, name: str) -> Any:
+        """Forward any other attribute/method access to the wrapped client."""
+        return getattr(self._inner, name)

backend/app/shared/bootstrap.py

@@ -6,6 +6,7 @@ from functools import lru_cache
 from typing import Callable
 
 from app.application.agent import AgentConversationService, AgentSessionService
+from app.application.agent.agentic_service import AgenticConversationService
 from app.application.documents import DocumentCommandService, DocumentQueryService
 from app.application.knowledge import KnowledgeRetrievalService
 from app.application.perception.services import PerceptionService
@@ -19,6 +20,15 @@ from app.infrastructure.parser.local_chunk_builder import LocalRegulationChunkBu
 from app.infrastructure.parser.local_document_parser import LocalDocumentParser
 from app.infrastructure.parser.vector_chunk_builder import AliyunVectorChunkBuilder
 from app.infrastructure.perception.mock_event_store import MockEventStore
+from app.application.perception.crawl_service import CrawlService
+from app.infrastructure.perception.base_event_store import BaseEventStore
+from app.infrastructure.perception.crawlers.catarc_crawler import CatarcCrawler
+from app.infrastructure.perception.crawlers.guobiao_crawler import (
+    GuobiaoMandatoryCrawler,
+    GuobiaoRecommendedCrawler,
+)
+from app.infrastructure.perception.crawlers.eurlex_crawler import EurlexCrawler
+from app.infrastructure.perception.llm_pipeline import LlmPipeline
 from app.infrastructure.session.in_memory_conversation_store import InMemoryConversationStore
 from app.infrastructure.storage.json_document_processing_store import JsonDocumentProcessingStore
 from app.infrastructure.storage.json_document_repository import JsonDocumentRepository
@@ -31,6 +41,8 @@ from app.infrastructure.vectorstore.cross_encoder_reranker import OpenAICompatib
 from app.infrastructure.vectorstore.dense_retriever import DenseRetriever
 from app.infrastructure.vectorstore.milvus_vector_index import MilvusVectorIndex
 from app.services.llm.llm_factory import LLMFactory
+from app.domain.compliance.ports import ComplianceRepository
+from app.infrastructure.compliance.repository import PostgresComplianceRepository
 # Keep shared wiring centralized so dependency construction remains consistent.
 
 
@@ -252,7 +264,31 @@ def get_document_query_service() -> DocumentQueryService:
 
 @lru_cache
 def get_conversation_store() -> InMemoryConversationStore:
-    """Return conversation store."""
+    """Return the active conversation store based on settings.
+
+    When session_backend='redis', sessions survive backend restarts and scale
+    across multiple API worker processes. When session_backend='memory' (default),
+    sessions are process-local and lost on restart.
+    """
+    if settings.session_backend == "redis":
+        import redis as redis_lib
+        from app.infrastructure.session.redis_conversation_store import RedisConversationStore
+
+        # Build the Redis client from the same connection settings used by Celery.
+        kwargs: dict = {
+            "host": settings.redis_host,
+            "port": settings.redis_port,
+            "db": settings.redis_db,
+            "decode_responses": False,
+        }
+        if settings.redis_password:
+            kwargs["password"] = settings.redis_password
+
+        redis_client = redis_lib.Redis(**kwargs)
+        return RedisConversationStore(  # type: ignore[return-value]
+            redis_client=redis_client,
+            timeout_seconds=settings.session_timeout_minutes * 60,
+        )
     return InMemoryConversationStore(
         max_sessions=settings.session_max_sessions,
         timeout_minutes=settings.session_timeout_minutes,
@@ -269,11 +305,57 @@ def get_agent_conversation_service() -> AgentConversationService:
     )
 
 
+@lru_cache
+def get_event_store() -> BaseEventStore:
+    """Return event store selected by DOCUMENT_REPOSITORY_BACKEND setting."""
+    if settings.document_repository_backend == "postgres":
+        from app.infrastructure.perception.postgres_event_store import PostgresEventStore
+        return PostgresEventStore()
+    return MockEventStore()
+
+
+@lru_cache
+def get_compliance_repository() -> ComplianceRepository:
+    """Return the compliance analysis repository.
+
+    Requires document_repository_backend=postgres and valid postgres_* settings.
+    Raises NotImplementedError for any other backend value.
+    """
+    if settings.document_repository_backend != "postgres":
+        raise NotImplementedError(
+            f"ComplianceRepository requires document_repository_backend=postgres, "
+            f"got '{settings.document_repository_backend}'. "
+            "Set DOCUMENT_REPOSITORY_BACKEND=postgres in your .env file."
+        )
+    return PostgresComplianceRepository(
+        host=settings.postgres_host,
+        port=settings.postgres_port,
+        user=settings.postgres_user,
+        password=settings.postgres_password,
+        dbname=settings.postgres_db,
+    )
+
+
 @lru_cache
 def get_perception_service() -> PerceptionService:
-    """Return perception service for regulatory intelligence."""
     return PerceptionService(
-        event_store=MockEventStore(),
+        event_store=get_event_store(),
+        retrieval_service=get_retrieval_service(),
+    )
+
+
+@lru_cache
+def get_crawl_service() -> CrawlService:
+    crawlers = {
+        "CATARC": CatarcCrawler(),
+        "国标委·强制性": GuobiaoMandatoryCrawler(),
+        "国标委·推荐性": GuobiaoRecommendedCrawler(),
+        "EUR-Lex": EurlexCrawler(),
+    }
+    return CrawlService(
+        crawlers=crawlers,
+        event_store=get_event_store(),
+        llm_pipeline=LlmPipeline(),
         retrieval_service=get_retrieval_service(),
     )
 
@@ -284,6 +366,49 @@ def get_agent_session_service() -> AgentSessionService:
     return AgentSessionService(conversation_store=get_conversation_store())
 
 
+@lru_cache
+def get_agentic_conversation_service() -> AgenticConversationService:
+    """Return the Agentic RAG service (P0-1).
+
+    Uses the same retrieval, generation, and session infrastructure as the
+    standard chat service so no additional dependencies are required.
+    """
+    return AgenticConversationService(
+        retrieval_service=get_retrieval_service(),
+        answer_generator=OpenAICompatibleAnswerGenerator(),
+        conversation_store=get_conversation_store(),
+    )
+
+
+@lru_cache
+def get_celery_app():
+    """Return the shared Celery application instance.
+
+    Imported lazily so Celery is not required when running without workers
+    (e.g., tests that mock bootstrap or dev without Redis).
+    """
+    from app.infrastructure.tasks.celery_app import celery_app
+    return celery_app
+
+
+@lru_cache
+def get_jwt_handler():
+    """Return the shared JWTHandler instance for token creation and validation."""
+    from app.infrastructure.auth.jwt_handler import JWTHandler
+    return JWTHandler(
+        secret_key=settings.auth_secret_key,
+        algorithm=settings.auth_algorithm,
+        expire_minutes=settings.auth_token_expire_minutes,
+    )
+
+
+@lru_cache
+def get_user_store():
+    """Return the PostgreSQL user store (lazy-connects on first call)."""
+    from app.infrastructure.auth.user_store import PostgresUserStore
+    return PostgresUserStore()
+
+
 def preload_runtime_dependencies() -> None:
     """Warm dependencies that are safe and useful to preload during startup."""
     LLMFactory.preload_clients(["qwen", "deepseek"])

backend/app/shared/model_usage_tracker.py

@@ -0,0 +1,113 @@
+"""In-memory registry that tracks per-model call outcomes and token usage.
+
+This module lives in `app/shared` — the same cross-cutting-support tier as
+`bootstrap.py` — because it is not business logic: it exists purely so the
+System Status page can show which AI models (main LLM, HyDE LLM, embedding,
+reranker) are configured, whether their most recent call succeeded, and how
+many tokens they have consumed since this process started. Tracking here
+must never disrupt a real user-facing call: every public method swallows its
+own exceptions and logs a warning instead of raising.
+"""
+
+from __future__ import annotations
+
+import threading
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from functools import lru_cache
+
+from loguru import logger
+
+
+@dataclass
+class ModelUsageEntry:
+    """Represent accumulated usage/connection state for one provider+model pair."""
+
+    provider: str
+    model: str
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    call_count_ok: int = 0
+    call_count_error: int = 0
+    last_called_at: datetime | None = None
+    last_latency_ms: int | None = None
+    last_error: str | None = None
+
+    @property
+    def status(self) -> str:
+        """Derive never_called/ok/error from call history.
+
+        The "disabled" status (reranker only, when turned off in settings) is
+        NOT decided here: this dataclass has no access to live settings. The
+        API route layer (Task 6) applies that override on top of this value,
+        so config always wins over stale historical data.
+        """
+        if self.last_called_at is None:
+            return "never_called"
+        return "error" if self.last_error else "ok"
+
+
+class ModelUsageTracker:
+    """Thread-safe in-memory registry of per-model call/usage stats.
+
+    Keyed by "{provider}:{model}" rather than by business role (main LLM /
+    HyDE / embedding / reranker) so that any future call site is captured
+    automatically, even before anyone teaches this class about its role.
+    """
+
+    def __init__(self) -> None:
+        """Initialize an empty registry guarded by a single lock."""
+        self._entries: dict[str, ModelUsageEntry] = {}
+        # One coarse lock is enough: record() runs at most a few times per
+        # request, and snapshot() is only read by the low-traffic status page.
+        self._lock = threading.Lock()
+
+    def record(
+        self,
+        *,
+        provider: str,
+        model: str,
+        success: bool,
+        usage: dict | None = None,
+        latency_ms: int | None = None,
+        error: str | None = None,
+    ) -> None:
+        """Record the outcome of one call to provider/model.
+
+        Never raises: any internal failure is logged and swallowed so a bug
+        in observability code cannot break a real LLM/embedding/reranker call.
+        """
+        try:
+            key = f"{provider}:{model}"
+            usage = usage if isinstance(usage, dict) else {}
+            with self._lock:
+                entry = self._entries.setdefault(key, ModelUsageEntry(provider=provider, model=model))
+                entry.total_tokens += int(usage.get("total_tokens", 0) or 0)
+                entry.prompt_tokens += int(usage.get("prompt_tokens", 0) or 0)
+                entry.completion_tokens += int(usage.get("completion_tokens", 0) or 0)
+                if success:
+                    entry.call_count_ok += 1
+                    entry.last_error = None
+                else:
+                    entry.call_count_error += 1
+                    entry.last_error = error or "unknown error"
+                entry.last_called_at = datetime.now(timezone.utc)
+                entry.last_latency_ms = latency_ms
+        except Exception as exc:  # noqa: BLE001 - tracking must never break a real call
+            logger.warning("ModelUsageTracker.record failed for {}:{} - {}", provider, model, exc)
+
+    def snapshot(self) -> dict[str, ModelUsageEntry]:
+        """Return a shallow copy of all tracked entries, safe to mutate by the caller."""
+        with self._lock:
+            return dict(self._entries)
+
+    def get(self, provider: str, model: str) -> ModelUsageEntry | None:
+        """Return the entry for one provider/model pair, or None if never recorded."""
+        return self.snapshot().get(f"{provider}:{model}")
+
+
+@lru_cache
+def get_model_usage_tracker() -> ModelUsageTracker:
+    """Return the process-wide singleton tracker (mirrors get_settings()/get_llm_factory())."""
+    return ModelUsageTracker()

backend/requirements.txt

@@ -1,30 +1,48 @@
+# ── Web framework ─────────────────────────────────────────────────────────────
 fastapi>=0.110.0
 uvicorn[standard]>=0.27.0
 python-multipart>=0.0.9
 
+# ── Config & utilities ────────────────────────────────────────────────────────
 pydantic>=2.0.0
 pydantic-settings>=2.0.0
 python-dotenv>=1.0.0
 loguru>=0.7.0
-
 httpx>=0.25.0
+beautifulsoup4>=4.12.0
+lxml>=5.0.0
 tiktoken>=0.5.0
 tenacity>=8.2.0
 
+# ── Auth ──────────────────────────────────────────────────────────────────────
+python-jose[cryptography]>=3.3.0
+# passlib is incompatible with bcrypt>=4.0 (removed __about__, strict 72-byte limit).
+# Pin bcrypt to 3.x until passlib ships a fix.
+passlib[bcrypt]>=1.7.4
+bcrypt>=3.2.0,<4.0.0
+
+# ── Async task queue ──────────────────────────────────────────────────────────
+celery>=5.3.0
+redis>=4.5.0
+
+# ── Storage & databases ───────────────────────────────────────────────────────
 pymilvus>=2.4.0
 minio>=7.1.0
 psycopg2-binary>=2.9.0
 
+# ── Document parsing ─────────────────────────────────────────────────────────
 pymupdf>=1.24.0
 python-docx>=1.1.0
-
-numpy>=1.24.0
 alibabacloud-docmind-api20220711>=1.0.6
 alibabacloud-tea-openapi>=0.3.11
 alibabacloud-tea-util>=0.3.13
 
+# ── RAG / LangChain ───────────────────────────────────────────────────────────
 langchain>=0.1.0
 langchain-milvus>=0.1.0
+numpy>=1.24.0
 
+# ── Testing ───────────────────────────────────────────────────────────────────
 pytest>=7.4.0
 pytest-asyncio>=0.21.0
+fakeredis>=2.0.0

backend/tests/compliance/test_pipeline.py

@@ -0,0 +1,140 @@
+import asyncio
+
+import pytest
+from unittest.mock import MagicMock, patch
+from datetime import datetime
+
+from app.infrastructure.vectorstore.pass_through_reranker import PassThroughReranker
+from app.domain.retrieval.models import RetrievedChunk
+from app.domain.compliance.ports import AnalysisRecord, FindingRecord
+
+
+# ── helpers ──────────────────────────────────────────────────────────────────
+
+def _make_chunk(score: float) -> RetrievedChunk:
+    return RetrievedChunk(
+        chunk_id="c1",
+        doc_id="d1",
+        doc_title="Test Doc",
+        section_title="S1",
+        text="some text",
+        score=score,
+        page_start=1,
+    )
+
+
+def _make_mock_client(content: str = '{"status":"ok","title":"T","desc":"D","clause_ref":"A1"}'):
+    client = MagicMock()
+    response = MagicMock()
+    response.is_success = True
+    response.content = content
+    client.chat.return_value = response
+    return client
+
+
+def _make_mock_retrieval():
+    svc = MagicMock()
+    svc.retrieve.return_value = []
+    return svc
+
+
+# ── existing tests ────────────────────────────────────────────────────────────
+
+def test_pass_through_returns_top_k():
+    reranker = PassThroughReranker()
+    chunks = [_make_chunk(0.9), _make_chunk(0.8), _make_chunk(0.7)]
+    result = reranker.rerank(query="test", chunks=chunks, top_k=2)
+    assert len(result) == 2
+    assert result[0].score == 0.9
+
+
+def test_pass_through_returns_all_when_top_k_exceeds():
+    reranker = PassThroughReranker()
+    chunks = [_make_chunk(0.5)]
+    result = reranker.rerank(query="test", chunks=chunks, top_k=10)
+    assert len(result) == 1
+
+
+# ── new tests ─────────────────────────────────────────────────────────────────
+
+def test_process_single_clause_returns_finding():
+    from app.application.compliance.pipeline import process_single_clause
+    client = _make_mock_client()
+    svc = _make_mock_retrieval()
+    result = process_single_clause("test clause", 0, svc, client)
+    assert result["finding"] is not None
+    assert result["index"] == 0
+    assert result["chunks"] == []
+
+
+def test_run_clauses_parallel_runs_all():
+    from app.application.compliance.pipeline import run_clauses_parallel
+    client = _make_mock_client()
+    svc = _make_mock_retrieval()
+    clauses = ["clause one", "clause two", "clause three"]
+    results = asyncio.run(run_clauses_parallel(clauses, svc, client))
+    assert len(results) == 3
+    assert all(r["index"] == i for i, r in enumerate(results))
+
+
+def test_run_clauses_parallel_handles_clause_failure():
+    from app.application.compliance.pipeline import run_clauses_parallel
+    svc = _make_mock_retrieval()
+    bad_client = MagicMock()
+    bad_client.chat.side_effect = RuntimeError("LLM exploded")
+    results = asyncio.run(run_clauses_parallel(
+        ["clause one", "clause two"], svc, bad_client
+    ))
+    assert len(results) == 2
+    assert all(r["finding"] is None for r in results)
+    assert all(r["chunks"] == [] for r in results)
+
+
+# ── helpers for new tests ─────────────────────────────────────────────────────
+
+def _sample_analysis() -> AnalysisRecord:
+    return AnalysisRecord(
+        id="a1", created_at=datetime(2026, 6, 8), created_by="u",
+        doc_name="doc.pdf", standard_name="EU AI Act",
+        risk_score=72, conclusion="Gaps found.", actions=[], para_text="para",
+        highlight_terms=[], findings=[],
+    )
+
+
+def _sample_finding(status: str = "risk") -> FindingRecord:
+    return FindingRecord(
+        id="f1", analysis_id="a1", seq=0,
+        title="Missing CSMS", description="No CSMS certification.",
+        status=status, clause_ref="Art.9.1",
+    )
+
+
+# ── new tests ─────────────────────────────────────────────────────────────────
+
+def test_build_finding_context_contains_required_fields():
+    from app.application.compliance.pipeline import build_finding_context
+    ctx = build_finding_context(_sample_finding(), _sample_analysis())
+    assert "doc.pdf" in ctx
+    assert "EU AI Act" in ctx
+    assert "Missing CSMS" in ctx
+    assert "Art.9.1" in ctx
+
+
+def test_generate_suggestions_returns_three_questions():
+    from app.application.compliance.pipeline import generate_suggestions
+    client = _make_mock_client(
+        '{"questions": ["Q1?", "Q2?", "Q3?"]}'
+    )
+    questions = generate_suggestions(_sample_finding("risk"), _sample_analysis(), client)
+    assert len(questions) == 3
+    assert all(isinstance(q, str) for q in questions)
+
+
+def test_generate_suggestions_falls_back_on_error():
+    from app.application.compliance.pipeline import generate_suggestions
+    bad_client = MagicMock()
+    bad_resp = MagicMock()
+    bad_resp.is_success = False
+    bad_client.chat.return_value = bad_resp
+    questions = generate_suggestions(_sample_finding(), _sample_analysis(), bad_client)
+    assert len(questions) == 3  # fallback always returns 3

backend/tests/compliance/test_repository.py

@@ -0,0 +1,98 @@
+from unittest.mock import MagicMock, patch
+from datetime import datetime
+from app.domain.compliance.ports import (
+    AnalysisRecord,
+    FindingRecord,
+    ComplianceRepository,
+)
+
+
+def _mock_pool():
+    """Return a mock psycopg2 ThreadedConnectionPool."""
+    conn = MagicMock()
+    cursor = MagicMock()
+    cursor.__enter__ = MagicMock(return_value=cursor)
+    cursor.__exit__ = MagicMock(return_value=False)
+    conn.cursor.return_value = cursor
+    pool = MagicMock()
+    pool.getconn.return_value = conn
+    return pool, conn, cursor
+
+
+@patch("app.infrastructure.compliance.repository.psycopg2.pool.ThreadedConnectionPool")
+def test_save_analysis_returns_uuid(mock_pool_cls):
+    from app.infrastructure.compliance.repository import PostgresComplianceRepository
+    pool, conn, cursor = _mock_pool()
+    mock_pool_cls.return_value = pool
+    cursor.fetchone.return_value = {"id": "abc-123"}
+
+    repo = PostgresComplianceRepository(
+        host="localhost", port=5432, user="u", password="p", dbname="db"
+    )
+    record = AnalysisRecord(
+        id="", created_at=datetime.utcnow(), created_by="user1",
+        doc_name="doc.pdf", standard_name="EU AI Act",
+        risk_score=50, conclusion="OK", actions=[], para_text="p",
+        highlight_terms=[], findings=[],
+    )
+    result = repo.save_analysis(record)
+    assert result == "abc-123"
+
+
+def test_analysis_record_construction():
+    record = AnalysisRecord(
+        id="",
+        created_at=datetime.utcnow(),
+        created_by="user1",
+        doc_name="test.pdf",
+        standard_name="EU AI Act",
+        risk_score=72,
+        conclusion="Several gaps found.",
+        actions=[{"label": "Fix", "value": "Update docs"}],
+        para_text="The system shall...",
+        highlight_terms=["CSMS", "ISO 21434"],
+        findings=[
+            FindingRecord(
+                id="",
+                analysis_id="",
+                seq=0,
+                title="Missing CSMS",
+                description="No CSMS certification found.",
+                status="risk",
+                clause_ref="Art.9.1",
+            )
+        ],
+    )
+    assert record.doc_name == "test.pdf"
+    assert len(record.findings) == 1
+    assert record.findings[0].status == "risk"
+
+
+def test_compliance_repository_is_abstract():
+    import inspect
+    assert inspect.isabstract(ComplianceRepository)
+
+
+def test_generate_docx_returns_bytes():
+    from app.infrastructure.compliance.docx_export import generate_docx
+    record = AnalysisRecord(
+        id="test-id", created_at=datetime(2026, 6, 8), created_by="user1",
+        doc_name="test.pdf", standard_name="EU AI Act",
+        risk_score=72, conclusion="Several gaps found.",
+        actions=[{"label": "Fix", "value": "Update CSMS docs"}],
+        para_text="The system shall implement CSMS.",
+        highlight_terms=["CSMS"],
+        findings=[
+            FindingRecord(
+                id="f1", analysis_id="test-id", seq=0,
+                title="Missing CSMS", description="No CSMS cert.",
+                status="risk", clause_ref="Art.9.1",
+            )
+        ],
+    )
+    data = generate_docx(record)
+    assert isinstance(data, bytes)
+    assert len(data) > 1000  # DOCX is at minimum a ZIP with ~1 KB overhead
+    # Verify it's a valid ZIP (DOCX = ZIP container)
+    import zipfile, io
+    assert zipfile.is_zipfile(io.BytesIO(data))

backend/tests/observability/test_embedding_usage_tracking.py

@@ -0,0 +1,65 @@
+"""Verifies OpenAICompatibleEmbeddingProvider records usage into ModelUsageTracker."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+
+from app.infrastructure.embedding.openai_compatible_embedding_provider import OpenAICompatibleEmbeddingProvider
+from app.shared.model_usage_tracker import get_model_usage_tracker
+
+
+@pytest.fixture(autouse=True)
+def _reset_tracker():
+    """Clear the process-wide tracker before and after each test in this file."""
+    get_model_usage_tracker()._entries.clear()
+    yield
+    get_model_usage_tracker()._entries.clear()
+
+
+def _fake_response(usage: dict) -> MagicMock:
+    """Build a fake httpx.Response-like object for a successful embeddings call."""
+    resp = MagicMock(spec=httpx.Response)
+    resp.raise_for_status.return_value = None
+    resp.json.return_value = {
+        "data": [{"index": 0, "embedding": [0.1] * 1024}],
+        "usage": usage,
+    }
+    return resp
+
+
+def test_successful_embed_records_usage():
+    """A successful embeddings call must record token usage under 'embedding:<model>'."""
+    provider = OpenAICompatibleEmbeddingProvider()
+    provider.api_key = "test-key"
+    with patch("httpx.post", return_value=_fake_response({"prompt_tokens": 3, "total_tokens": 3})):
+        provider.embed_query("hello")
+
+    entry = get_model_usage_tracker().get("embedding", provider.model)
+    assert entry is not None
+    assert entry.total_tokens == 3
+    assert entry.status == "ok"
+
+
+def test_failed_embed_records_error():
+    """An HTTP error from the embeddings endpoint must be recorded as a failure, then re-raised."""
+    provider = OpenAICompatibleEmbeddingProvider()
+    provider.api_key = "test-key"
+    failing_response = MagicMock(spec=httpx.Response)
+    failing_response.status_code = 500
+    failing_response.text = "boom"
+    failing_response.request = MagicMock()
+    failing_response.request.url = "http://example.com/embeddings"
+    failing_response.raise_for_status.side_effect = httpx.HTTPStatusError(
+        "boom", request=failing_response.request, response=failing_response
+    )
+    with patch("httpx.post", return_value=failing_response):
+        with pytest.raises(httpx.HTTPStatusError):
+            provider.embed_query("hello")
+
+    entry = get_model_usage_tracker().get("embedding", provider.model)
+    assert entry is not None
+    assert entry.status == "error"
+    assert entry.call_count_error == 1

backend/tests/observability/test_llm_factory_tracking.py

@@ -0,0 +1,44 @@
+"""Verifies get_llm_client() returns a usage-tracked client end to end."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from app.services.llm.llm_factory import LLMFactory, get_llm_client
+from app.services.llm.tracked_client import TrackedLLMClient
+from app.shared.model_usage_tracker import get_model_usage_tracker
+
+
+@pytest.fixture(autouse=True)
+def _reset_singletons():
+    """Clear the two process-wide singletons this test touches, before and after.
+
+    LLMFactory._global_instances and get_model_usage_tracker() both persist
+    for the life of the process; without this fixture, tests would leak
+    cached clients/usage data into each other and become order-dependent.
+    """
+    LLMFactory._global_instances.clear()
+    get_model_usage_tracker().snapshot()  # no-op read, just documents intent
+    get_model_usage_tracker()._entries.clear()
+    yield
+    LLMFactory._global_instances.clear()
+    get_model_usage_tracker()._entries.clear()
+
+
+def test_get_llm_client_returns_tracked_client():
+    """get_llm_client() must return a TrackedLLMClient, not the raw provider client."""
+    with patch("app.services.llm.llm_factory.DeepSeekClient") as mock_cls:
+        mock_cls.return_value = MagicMock()
+        client = get_llm_client(provider="deepseek", model="deepseek-v4-flash", api_key="test-key")
+    assert isinstance(client, TrackedLLMClient)
+
+
+def test_get_llm_client_caches_the_tracked_instance():
+    """A second call with the same provider/model must return the same TrackedLLMClient."""
+    with patch("app.services.llm.llm_factory.DeepSeekClient") as mock_cls:
+        mock_cls.return_value = MagicMock()
+        first = get_llm_client(provider="deepseek", model="deepseek-v4-flash", api_key="test-key")
+        second = get_llm_client(provider="deepseek", model="deepseek-v4-flash", api_key="test-key")
+    assert first is second

backend/tests/observability/test_model_usage_tracker.py

@@ -0,0 +1,79 @@
+"""Unit tests for ModelUsageTracker — no mocking needed, pure in-memory state."""
+
+from __future__ import annotations
+
+from app.shared.model_usage_tracker import ModelUsageEntry, ModelUsageTracker, get_model_usage_tracker
+
+
+def test_never_called_model_has_no_entry():
+    """A tracker that has never recorded a call returns None from get()."""
+    tracker = ModelUsageTracker()
+    assert tracker.get("deepseek", "deepseek-v4-flash") is None
+
+
+def test_record_success_accumulates_tokens_and_calls():
+    """Two successful calls accumulate tokens and call_count_ok."""
+    tracker = ModelUsageTracker()
+    tracker.record(
+        provider="deepseek", model="deepseek-v4-flash", success=True,
+        usage={"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, latency_ms=100,
+    )
+    tracker.record(
+        provider="deepseek", model="deepseek-v4-flash", success=True,
+        usage={"prompt_tokens": 20, "completion_tokens": 8, "total_tokens": 28}, latency_ms=200,
+    )
+    entry = tracker.get("deepseek", "deepseek-v4-flash")
+    assert entry is not None
+    assert entry.total_tokens == 43
+    assert entry.prompt_tokens == 30
+    assert entry.completion_tokens == 13
+    assert entry.call_count_ok == 2
+    assert entry.call_count_error == 0
+    assert entry.status == "ok"
+    assert entry.last_latency_ms == 200
+
+
+def test_record_error_sets_error_status_without_losing_prior_tokens():
+    """A failed call after successful ones flips status to 'error' but keeps accumulated tokens."""
+    tracker = ModelUsageTracker()
+    tracker.record(provider="qwen", model="qwen3.5-flash", success=True, usage={"total_tokens": 50}, latency_ms=50)
+    tracker.record(provider="qwen", model="qwen3.5-flash", success=False, error="HTTP 500", latency_ms=30)
+    entry = tracker.get("qwen", "qwen3.5-flash")
+    assert entry.total_tokens == 50
+    assert entry.call_count_ok == 1
+    assert entry.call_count_error == 1
+    assert entry.status == "error"
+    assert entry.last_error == "HTTP 500"
+
+
+def test_record_success_after_error_clears_last_error():
+    """A later successful call clears last_error and status returns to 'ok'."""
+    tracker = ModelUsageTracker()
+    tracker.record(provider="qwen", model="qwen3.5-flash", success=False, error="timeout", latency_ms=30)
+    tracker.record(provider="qwen", model="qwen3.5-flash", success=True, usage={"total_tokens": 5}, latency_ms=40)
+    entry = tracker.get("qwen", "qwen3.5-flash")
+    assert entry.status == "ok"
+    assert entry.last_error is None
+
+
+def test_record_never_raises_on_bad_usage_dict():
+    """A malformed usage value (wrong type) is swallowed, not raised, and does not corrupt other entries."""
+    tracker = ModelUsageTracker()
+    tracker.record(provider="embedding", model="text-embedding-v3", success=True, usage="not-a-dict", latency_ms=10)  # type: ignore[arg-type]
+    # Must not raise, and must not have created a corrupted entry that breaks snapshot().
+    snapshot = tracker.snapshot()
+    assert isinstance(snapshot, dict)
+
+
+def test_snapshot_returns_independent_copy():
+    """snapshot() returns a dict that can be safely mutated without affecting the tracker."""
+    tracker = ModelUsageTracker()
+    tracker.record(provider="deepseek", model="deepseek-v4-flash", success=True, usage={"total_tokens": 1}, latency_ms=1)
+    snap = tracker.snapshot()
+    snap.clear()
+    assert tracker.get("deepseek", "deepseek-v4-flash") is not None
+
+
+def test_get_model_usage_tracker_returns_singleton():
+    """get_model_usage_tracker() always returns the same process-wide instance."""
+    assert get_model_usage_tracker() is get_model_usage_tracker()

backend/tests/observability/test_reranker_usage_tracking.py

@@ -0,0 +1,50 @@
+"""Verifies OpenAICompatibleReranker records call outcome (no tokens) into ModelUsageTracker."""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+
+from app.domain.retrieval import RetrievedChunk
+from app.infrastructure.vectorstore.cross_encoder_reranker import OpenAICompatibleReranker
+from app.shared.model_usage_tracker import get_model_usage_tracker
+
+
+@pytest.fixture(autouse=True)
+def _reset_tracker():
+    """Clear the process-wide tracker before and after each test in this file."""
+    get_model_usage_tracker()._entries.clear()
+    yield
+    get_model_usage_tracker()._entries.clear()
+
+
+def _chunk(chunk_id: str, text: str) -> RetrievedChunk:
+    """Build a minimal RetrievedChunk for reranker tests."""
+    return RetrievedChunk(chunk_id=chunk_id, doc_id="doc-1", doc_title="Doc", text=text, score=0.0)
+
+
+def test_successful_rerank_records_call_without_tokens():
+    """A successful rerank() call is recorded with call_count_ok but zero tokens."""
+    reranker = OpenAICompatibleReranker(base_url="http://example.test", model="bge-reranker-v2-m3")
+    with patch.object(reranker, "_call_reranker", return_value=[0.9, 0.1]):
+        result = reranker.rerank("query", [_chunk("c1", "a"), _chunk("c2", "b")], top_k=2)
+
+    assert len(result) == 2
+    entry = get_model_usage_tracker().get("reranker", "bge-reranker-v2-m3")
+    assert entry is not None
+    assert entry.call_count_ok == 1
+    assert entry.total_tokens == 0
+
+
+def test_failed_rerank_records_error_and_falls_back():
+    """A rerank() call that raises internally is recorded as an error but still returns a fallback list."""
+    reranker = OpenAICompatibleReranker(base_url="http://example.test", model="bge-reranker-v2-m3")
+    with patch.object(reranker, "_call_reranker", side_effect=RuntimeError("gateway down")):
+        result = reranker.rerank("query", [_chunk("c1", "a")], top_k=1)
+
+    assert len(result) == 1  # existing fallback behavior: original order, unscored
+    entry = get_model_usage_tracker().get("reranker", "bge-reranker-v2-m3")
+    assert entry is not None
+    assert entry.call_count_error == 1
+    assert entry.status == "error"

backend/tests/observability/test_tracked_client.py

@@ -0,0 +1,85 @@
+"""Unit tests for TrackedLLMClient — verifies transparent delegation + recording."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+from app.services.llm.base_client import LLMConfig, LLMProvider, LLMResponse
+from app.services.llm.tracked_client import TrackedLLMClient
+from app.shared.model_usage_tracker import ModelUsageTracker
+
+
+def _make_inner(model: str = "deepseek-v4-flash") -> MagicMock:
+    """Build a MagicMock standing in for a concrete BaseLLMClient subclass."""
+    # Use MagicMock to avoid requiring a real LLM provider implementation (e.g., DeepseekClient);
+    # tests focus on TrackedLLMClient's delegation and recording behavior, not provider logic.
+    inner = MagicMock()
+    inner.config = LLMConfig(
+        provider=LLMProvider.DEEPSEEK, model=model, api_key="test-key", base_url="http://example.test/v1",
+    )
+    return inner
+
+
+def test_chat_delegates_and_returns_unchanged_response():
+    """chat() must return exactly what the wrapped client returned."""
+    inner = _make_inner()
+    expected = LLMResponse(content="hello", model="deepseek-v4-flash", usage={"total_tokens": 12})
+    inner.chat.return_value = expected
+    tracker = ModelUsageTracker()
+
+    tracked = TrackedLLMClient(inner, tracker)
+    result = tracked.chat([{"role": "user", "content": "hi"}])
+
+    assert result is expected
+    inner.chat.assert_called_once_with([{"role": "user", "content": "hi"}], None, None, None)
+
+
+def test_chat_records_success_and_tokens():
+    """A successful chat() call must be recorded under 'deepseek:deepseek-v4-flash'."""
+    inner = _make_inner()
+    inner.chat.return_value = LLMResponse(content="hi", model="deepseek-v4-flash", usage={"total_tokens": 42})
+    tracker = ModelUsageTracker()
+
+    TrackedLLMClient(inner, tracker).chat([{"role": "user", "content": "hi"}])
+
+    entry = tracker.get("deepseek", "deepseek-v4-flash")
+    assert entry is not None
+    assert entry.total_tokens == 42
+    assert entry.status == "ok"
+
+
+def test_chat_records_error_from_response():
+    """A chat() call that returns an error-carrying LLMResponse is recorded as a failure."""
+    inner = _make_inner()
+    inner.chat.return_value = LLMResponse(content="", model="deepseek-v4-flash", error="API error: 500")
+    tracker = ModelUsageTracker()
+
+    TrackedLLMClient(inner, tracker).chat([{"role": "user", "content": "hi"}])
+
+    entry = tracker.get("deepseek", "deepseek-v4-flash")
+    assert entry.status == "error"
+    assert entry.last_error == "API error: 500"
+
+
+def test_getattr_forwards_to_inner_client():
+    """Attributes not defined on TrackedLLMClient must forward to the wrapped client."""
+    inner = _make_inner()
+    inner.get_available_models.return_value = ["deepseek-v4-flash"]
+    tracked = TrackedLLMClient(inner, ModelUsageTracker())
+
+    assert tracked.get_available_models() == ["deepseek-v4-flash"]
+    assert tracked.config is inner.config
+
+
+def test_stream_chat_records_call_without_token_usage():
+    """stream_chat() must record a call (latency/success) but not fabricate token counts."""
+    inner = _make_inner()
+    inner.stream_chat.return_value = iter(["chunk-1", "chunk-2"])
+    tracker = ModelUsageTracker()
+
+    chunks = list(TrackedLLMClient(inner, tracker).stream_chat([{"role": "user", "content": "hi"}]))
+
+    assert chunks == ["chunk-1", "chunk-2"]
+    entry = tracker.get("deepseek", "deepseek-v4-flash")
+    assert entry.call_count_ok == 1
+    assert entry.total_tokens == 0

backend/tests/perception/test_base_event_store.py

@@ -0,0 +1,95 @@
+"""Contract tests: any BaseEventStore implementation must pass these."""
+from app.infrastructure.perception.base_event_store import BaseEventStore
+from app.infrastructure.perception.mock_event_store import MockEventStore
+
+
+def _store() -> BaseEventStore:
+    return MockEventStore()
+
+
+def test_is_base_event_store():
+    assert isinstance(_store(), BaseEventStore)
+
+
+def test_all_returns_list():
+    result = _store().all()
+    assert isinstance(result, list)
+    assert len(result) > 0
+
+
+def test_get_known_id():
+    store = _store()
+    first = store.all()[0]
+    result = store.get(first["id"])
+    assert result is not None
+    assert result["id"] == first["id"]
+
+
+def test_get_unknown_returns_none():
+    assert _store().get("does-not-exist") is None
+
+
+def test_filter_by_impact():
+    store = _store()
+    highs = store.filter(impact_level="high", limit=100)
+    assert all(e["impact_level"] == "high" for e in highs)
+
+
+def test_filter_limit():
+    store = _store()
+    result = store.filter(limit=3)
+    assert len(result) <= 3
+
+
+def test_stats_keys():
+    stats = _store().stats()
+    for key in ("total", "high_impact", "medium_impact", "recent_90d"):
+        assert key in stats, f"missing key: {key}"
+
+
+def test_upsert_and_get():
+    store = _store()
+    event = {
+        "id": "test-upsert-001",
+        "source": "TEST",
+        "source_label": "Test Source",
+        "standard_code": "TST-001",
+        "title": "Test Event",
+        "summary": "A test event",
+        "full_text_url": "https://example.com",
+        "status": "draft",
+        "impact_level": "low",
+        "published_at": "2026-01-01",
+        "effective_at": None,
+        "category": "test",
+        "tags": ["test"],
+        "content_hash": "abc123",
+        "previous_hash": None,
+    }
+    store.upsert(event)
+    result = store.get("test-upsert-001")
+    assert result is not None
+    assert result["title"] == "Test Event"
+
+
+def test_get_by_standard_code():
+    store = _store()
+    first = store.all()[0]
+    result = store.get_by_standard_code(first["standard_code"])
+    assert result is not None
+    assert result["standard_code"] == first["standard_code"]
+
+
+def test_upsert_updates_existing():
+    store = _store()
+    first = store.all()[0]
+    original_id = first["id"]
+    store.upsert({"id": original_id, "title": "Updated Title", "impact_level": first["impact_level"],
+                  "standard_code": first.get("standard_code", ""), "source": first["source"],
+                  "source_label": first.get("source_label", ""), "summary": "Updated",
+                  "full_text_url": "", "status": first["status"], "published_at": first.get("published_at", ""),
+                  "effective_at": None, "category": first.get("category", ""), "tags": [],
+                  "content_hash": "newhash", "previous_hash": None})
+    result = store.get(original_id)
+    assert result is not None
+    assert result["title"] == "Updated Title"

backend/tests/perception/test_crawl_service.py

@@ -0,0 +1,111 @@
+"""Integration tests for CrawlService."""
+from __future__ import annotations
+from unittest.mock import MagicMock
+import hashlib
+import pytest
+
+from app.infrastructure.perception.crawlers.base import RawEvent
+from app.infrastructure.perception.mock_event_store import MockEventStore
+
+
+def _make_raw_event(code="TST-001"):
+    return RawEvent(
+        source="TEST", source_label="Test", standard_code=code,
+        title=f"Test {code}", summary="Summary", full_text_url="https://example.com",
+        status="enacted", published_at="2026-01-01", effective_at=None,
+        category="test", tags=["test"], raw_text="full text",
+    )
+
+
+def _make_service(raw_events):
+    from app.application.perception.crawl_service import CrawlService
+
+    mock_crawler = MagicMock()
+    mock_crawler.fetch.return_value = raw_events
+
+    mock_pipeline = MagicMock()
+    mock_pipeline.extract_structure.return_value = {
+        "obligations": [], "deadlines": [], "scope": "test",
+        "penalties": None, "impact_level": "low",
+    }
+    mock_pipeline.assess_impact.return_value = []
+    mock_pipeline.compute_diff.return_value = {
+        "changed_sections": [], "change_summary": "No changes.",
+    }
+
+    mock_retrieval = MagicMock()
+    store = MockEventStore()
+
+    return CrawlService(
+        crawlers={"TEST": mock_crawler},
+        event_store=store,
+        llm_pipeline=mock_pipeline,
+        retrieval_service=mock_retrieval,
+    )
+
+
+def test_crawl_yields_progress_and_done():
+    svc = _make_service([_make_raw_event("TST-001")])
+    events = list(svc.run_crawl())
+    event_types = [e.get("event") for e in events]
+    assert "done" in event_types
+
+
+def test_crawl_upserts_to_store():
+    store = MockEventStore()
+    from app.application.perception.crawl_service import CrawlService
+    mock_crawler = MagicMock()
+    mock_crawler.fetch.return_value = [_make_raw_event("NEW-001")]
+    mock_pipeline = MagicMock()
+    mock_pipeline.extract_structure.return_value = {
+        "obligations": [], "deadlines": [], "scope": "",
+        "penalties": None, "impact_level": "medium",
+    }
+    mock_pipeline.assess_impact.return_value = []
+    mock_pipeline.compute_diff.return_value = {
+        "changed_sections": [], "change_summary": "",
+    }
+    svc = CrawlService(
+        crawlers={"TEST": mock_crawler},
+        event_store=store,
+        llm_pipeline=mock_pipeline,
+        retrieval_service=MagicMock(),
+    )
+    list(svc.run_crawl())
+    result = store.get_by_standard_code("NEW-001")
+    assert result is not None
+    assert result["title"] == "Test NEW-001"
+
+
+def test_crawl_skips_unchanged_events():
+    store = MockEventStore()
+    raw = _make_raw_event("SKIP-001")
+    content_hash = hashlib.sha256(raw.raw_text.encode()).hexdigest()
+    store.upsert({
+        "id": hashlib.sha256(f"TEST-SKIP-001".encode()).hexdigest()[:12],
+        "standard_code": "SKIP-001",
+        "source": "TEST",
+        "source_label": "Test",
+        "title": "Test SKIP-001",
+        "summary": "",
+        "full_text_url": "",
+        "status": "enacted",
+        "impact_level": "low",
+        "published_at": "2026-01-01",
+        "effective_at": None,
+        "category": "test",
+        "tags": [],
+        "content_hash": content_hash,
+    })
+    mock_pipeline = MagicMock()
+    from app.application.perception.crawl_service import CrawlService
+    mock_crawler = MagicMock()
+    mock_crawler.fetch.return_value = [raw]
+    svc = CrawlService(
+        crawlers={"TEST": mock_crawler},
+        event_store=store,
+        llm_pipeline=mock_pipeline,
+        retrieval_service=MagicMock(),
+    )
+    list(svc.run_crawl())
+    mock_pipeline.extract_structure.assert_not_called()

backend/tests/perception/test_crawlers.py

@@ -0,0 +1,127 @@
+"""Unit tests for crawlers — mock httpx responses."""
+from __future__ import annotations
+from unittest.mock import MagicMock, patch
+import pytest
+
+from app.infrastructure.perception.crawlers.base import RawEvent, BaseCrawler
+
+
+def test_raw_event_fields():
+    ev = RawEvent(
+        source="TEST",
+        source_label="Test",
+        standard_code="TST-001",
+        title="Test",
+        summary="Summary",
+        full_text_url="https://example.com",
+        status="enacted",
+        published_at="2026-01-01",
+        effective_at=None,
+        category="test",
+        tags=["a"],
+        raw_text="full text here",
+    )
+    assert ev.source == "TEST"
+    assert ev.tags == ["a"]
+
+
+CATARC_HTML = """
+<html><body>
+<table>
+<tr>
+  <td><a href="/std/detail/123">GB 18384-2025</a></td>
+  <td>电动汽车安全要求</td>
+  <td>2025-11-15</td>
+  <td>现行</td>
+</tr>
+<tr>
+  <td><a href="/std/detail/456">GB/T 40429-2026</a></td>
+  <td>汽车驾驶自动化分级</td>
+  <td>2026-02-01</td>
+  <td>即将实施</td>
+</tr>
+</table>
+</body></html>
+"""
+
+
+def test_catarc_crawler_parses_html():
+    from app.infrastructure.perception.crawlers.catarc_crawler import CatarcCrawler
+
+    mock_resp = MagicMock()
+    mock_resp.status_code = 200
+    mock_resp.text = CATARC_HTML
+    mock_resp.raise_for_status = MagicMock()
+
+    with patch("httpx.get", return_value=mock_resp):
+        crawler = CatarcCrawler()
+        events = crawler.fetch(limit=10)
+
+    assert isinstance(events, list)
+    assert len(events) >= 1
+    assert all(isinstance(e, RawEvent) for e in events)
+    codes = [e.standard_code for e in events]
+    assert "GB 18384-2025" in codes
+
+
+GUOBIAO_JSON = {
+    "rows": [
+        {
+            "std_code": "GB 18384-2025",
+            "std_name": "电动汽车安全要求",
+            "release_date": "2025-11-15",
+            "implement_date": "2026-07-01",
+            "std_status": "现行",
+            "std_type": "强制性",
+        },
+    ]
+}
+
+
+def test_guobiao_crawler_parses_json():
+    from app.infrastructure.perception.crawlers.guobiao_crawler import GuobiaoMandatoryCrawler
+
+    mock_resp = MagicMock()
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = GUOBIAO_JSON
+    mock_resp.raise_for_status = MagicMock()
+
+    with patch("httpx.get", return_value=mock_resp):
+        crawler = GuobiaoMandatoryCrawler()
+        events = crawler.fetch(limit=10)
+
+    assert len(events) >= 1
+    assert events[0].source == "国标委"
+    assert events[0].standard_code == "GB 18384-2025"
+
+
+EURLEX_RSS = """<?xml version="1.0" encoding="UTF-8"?>
+<rss version="2.0">
+  <channel>
+    <title>EUR-Lex</title>
+    <item>
+      <title>Regulation (EU) 2024/1689 — AI Act</title>
+      <link>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689</link>
+      <description>The EU Artificial Intelligence Act enters into force.</description>
+      <pubDate>Fri, 12 Jul 2024 00:00:00 GMT</pubDate>
+    </item>
+  </channel>
+</rss>"""
+
+
+def test_eurlex_crawler_parses_rss():
+    from app.infrastructure.perception.crawlers.eurlex_crawler import EurlexCrawler
+
+    mock_resp = MagicMock()
+    mock_resp.status_code = 200
+    mock_resp.text = EURLEX_RSS
+    mock_resp.content = EURLEX_RSS
+    mock_resp.raise_for_status = MagicMock()
+
+    with patch("httpx.get", return_value=mock_resp):
+        crawler = EurlexCrawler()
+        events = crawler.fetch(limit=5)
+
+    assert isinstance(events, list)
+    assert len(events) >= 1
+    assert events[0].source == "EUR-Lex"

backend/tests/perception/test_llm_pipeline.py

@@ -0,0 +1,77 @@
+"""Unit tests for LlmPipeline — mock LLM client and embedding provider."""
+from __future__ import annotations
+from unittest.mock import MagicMock, patch
+import json
+import pytest
+
+
+def _make_pipeline():
+    with patch("app.infrastructure.perception.llm_pipeline.get_llm_client") as mock_llm_fn, \
+         patch("app.infrastructure.perception.llm_pipeline.OpenAICompatibleEmbeddingProvider") as mock_emb_cls:
+
+        mock_client = MagicMock()
+        mock_client.chat.return_value = MagicMock(content='{"obligations":[{"text":"test obligation","deontic":"must","subject":"OEM","object":"system","condition":""}],"deadlines":[{"date":"2026-07-01","description":"实施截止"}],"scope":"适用于M1类车辆","penalties":"罚款","impact_level":"high"}')
+        mock_llm_fn.return_value = mock_client
+
+        mock_emb = MagicMock()
+        mock_emb.embed_texts.return_value = [[0.1] * 1024, [0.9] * 1024]
+        mock_emb_cls.return_value = mock_emb
+
+        from app.infrastructure.perception.llm_pipeline import LlmPipeline
+        return LlmPipeline(), mock_client, mock_emb
+
+
+def test_extract_structure_returns_dict():
+    pipeline, mock_client, _ = _make_pipeline()
+    event = {
+        "id": "evt-001",
+        "standard_code": "GB 18384-2025",
+        "title": "电动汽车安全要求",
+        "summary": "新增 IP67 级别防护",
+        "source_label": "CATARC",
+        "tags": ["电池安全"],
+    }
+    result = pipeline.extract_structure(event)
+    assert isinstance(result, dict)
+    assert "obligations" in result
+    assert "impact_level" in result
+
+
+def test_assess_impact_returns_list():
+    pipeline, mock_client, _ = _make_pipeline()
+    mock_client.chat.return_value = MagicMock(content='[{"doc_id":"d1","doc_name":"Safety Manual","score":0.85,"key_clauses":"§4.2","recommendation":"更新第4章"}]')
+    mock_retrieval = MagicMock()
+    chunk = MagicMock()
+    chunk.doc_id = "d1"
+    chunk.doc_title = "Safety Manual"
+    chunk.score = 0.85
+    chunk.text = "relevant text"
+    chunk.section_title = "§4.2"
+    mock_retrieval.retrieve.return_value = [chunk]
+    event = {
+        "standard_code": "GB 18384-2025",
+        "title": "电动汽车安全要求",
+        "obligations": [{"text": "OEM shall comply"}],
+    }
+    result = pipeline.assess_impact(event, mock_retrieval)
+    assert isinstance(result, list)
+
+
+def test_compute_diff_no_change():
+    pipeline, _, mock_emb = _make_pipeline()
+    mock_emb.embed_texts.return_value = [[0.5] * 1024, [0.5] * 1024]
+    result = pipeline.compute_diff("paragraph one", "paragraph one")
+    assert isinstance(result, dict)
+    assert "changed_sections" in result
+    assert "change_summary" in result
+
+
+def test_compute_diff_detects_change():
+    pipeline, mock_client, mock_emb = _make_pipeline()
+    mock_emb.embed_texts.return_value = [
+        [1.0] + [0.0] * 1023,
+        [0.0] + [1.0] + [0.0] * 1022,
+    ]
+    mock_client.chat.return_value = MagicMock(content='{"change_type":"tightened","summary":"Requirement tightened"}')
+    result = pipeline.compute_diff("old paragraph text", "new tighter requirement text")
+    assert isinstance(result["changed_sections"], list)

backend/tests/perception/test_postgres_event_store.py

@@ -0,0 +1,98 @@
+"""Unit tests for PostgresEventStore using a mocked psycopg2 pool."""
+from __future__ import annotations
+import json
+from unittest.mock import MagicMock, patch
+import pytest
+
+# Patch psycopg2 before importing the module under test
+import sys
+mock_psycopg2 = MagicMock()
+mock_psycopg2.extras = MagicMock()
+sys.modules.setdefault("psycopg2", mock_psycopg2)
+sys.modules.setdefault("psycopg2.extras", mock_psycopg2.extras)
+sys.modules.setdefault("psycopg2.pool", MagicMock())
+
+from app.infrastructure.perception.base_event_store import BaseEventStore
+
+
+SAMPLE_ROW = {
+    "id": "pg-001",
+    "source": "国标委",
+    "source_label": "国家标准化管理委员会",
+    "standard_code": "GB 18384-2025",
+    "title": "电动汽车安全要求",
+    "summary": "新增要求",
+    "full_text_url": "https://openstd.samr.gov.cn",
+    "status": "enacted",
+    "impact_level": "high",
+    "published_at": "2025-11-15",
+    "effective_at": "2026-07-01",
+    "category": "电动汽车安全",
+    "tags": ["电池安全"],
+    "obligations": None,
+    "deadlines": None,
+    "scope": None,
+    "penalties": None,
+    "content_hash": "abc123",
+    "previous_hash": None,
+    "change_summary": None,
+    "changed_sections": None,
+    "affected_docs": None,
+    "crawled_at": "2026-06-05T10:00:00+00:00",
+    "processed_at": None,
+    "raw_storage_key": None,
+}
+
+
+def _make_store_with_pool(mock_pool):
+    with patch("psycopg2.pool.ThreadedConnectionPool", return_value=mock_pool):
+        with patch(
+            "app.infrastructure.perception.postgres_event_store.PostgresEventStore._ensure_schema"
+        ):
+            from app.infrastructure.perception.postgres_event_store import PostgresEventStore
+            return PostgresEventStore()
+
+
+def _cursor_returning(rows):
+    cursor = MagicMock()
+    cursor.__enter__ = lambda s: s
+    cursor.__exit__ = MagicMock(return_value=False)
+    cursor.fetchall.return_value = rows
+    cursor.fetchone.return_value = rows[0] if rows else None
+    return cursor
+
+
+def test_is_base_event_store():
+    mock_pool = MagicMock()
+    store = _make_store_with_pool(mock_pool)
+    assert isinstance(store, BaseEventStore)
+
+
+def test_filter_returns_list():
+    mock_pool = MagicMock()
+    conn = MagicMock()
+    conn.__enter__ = lambda s: s
+    conn.__exit__ = MagicMock(return_value=False)
+    cursor = _cursor_returning([SAMPLE_ROW])
+    conn.cursor.return_value = cursor
+    mock_pool.getconn.return_value = conn
+    store = _make_store_with_pool(mock_pool)
+    result = store.filter(limit=10)
+    assert isinstance(result, list)
+
+
+def test_stats_returns_correct_keys():
+    mock_pool = MagicMock()
+    conn = MagicMock()
+    conn.__enter__ = lambda s: s
+    conn.__exit__ = MagicMock(return_value=False)
+    cursor = MagicMock()
+    cursor.__enter__ = lambda s: s
+    cursor.__exit__ = MagicMock(return_value=False)
+    cursor.fetchone.return_value = {"count": 5}
+    conn.cursor.return_value = cursor
+    mock_pool.getconn.return_value = conn
+    store = _make_store_with_pool(mock_pool)
+    stats = store.stats()
+    for key in ("total", "high_impact", "medium_impact", "recent_90d"):
+        assert key in stats

dev.sh

@@ -549,7 +549,7 @@ AI+合规智能中枢统一脚本
 用法:
   ./dev.sh help
   ./dev.sh setup
-  ./dev.sh start [all|api|frontend] [--foreground] [--mode dev|static]
+  ./dev.sh start [all|api|frontend|worker|beat] [--foreground] [--mode dev|static]
   ./dev.sh stop [all|api|frontend]
   ./dev.sh restart [all|api|frontend] [--mode dev|static]
   ./dev.sh status
@@ -563,6 +563,9 @@ AI+合规智能中枢统一脚本
     进行一次性的本地初始化。
     包含 Python 版本检查、.venv 虚拟环境创建、后端依赖安装、前端 npm install、
     以及 6.86.80.8 基础服务端口连通性检查。
+    初始化完成后，首次运行前还需执行:
+      PYTHONPATH=backend .venv/bin/python scripts/seed_users.py
+    以创建 admin/legal/ehs/readonly 四个演示用户。
 
   start
     启动服务。默认行为等同于 ./dev.sh start all。
@@ -570,6 +573,8 @@ AI+合规智能中枢统一脚本
       all       同时启动 API 和前端。
       api       只启动后端 API。
       frontend  只启动前端。
+      worker    启动 Celery 文档处理 worker（前台运行，需要 Redis）。
+      beat      启动 Celery Beat 定时调度器（前台运行，需要 Redis）。
     可选参数:
       --foreground    仅对 start api 生效，前台运行并开启 --reload，便于调试。
       --mode dev      前端使用 Vite 开发服务器，默认端口 5173。
@@ -578,6 +583,7 @@ AI+合规智能中枢统一脚本
   stop
     停止服务。默认行为等同于 ./dev.sh stop all。
     会优先读取 logs/*.pid，PID 文件失效时会回退到端口探测。
+    注意: worker 和 beat 为前台进程，直接 Ctrl+C 停止。
 
   restart
     先停止再启动，支持 all/api/frontend。
@@ -601,8 +607,11 @@ AI+合规智能中枢统一脚本
 
 常用示例:
   ./dev.sh setup
+  PYTHONPATH=backend .venv/bin/python scripts/seed_users.py
   ./dev.sh start
   ./dev.sh start api --foreground
+  ./dev.sh start worker
+  ./dev.sh start beat
   ./dev.sh start frontend --mode static
   ./dev.sh restart frontend --mode dev
   ./dev.sh status
@@ -615,7 +624,7 @@ parse_target() {
     local default_target="$1"
     local candidate="${2:-}"
     case "$candidate" in
-        all|api|frontend)
+        all|api|frontend|worker|beat)
             echo "$candidate"
             ;;
         *)
@@ -646,6 +655,27 @@ main() {
                 shift || true
             fi
 
+            # worker and beat are pass-through — forward remaining args to celery directly.
+            case "$target" in
+                worker)
+                    print_header "AI+合规智能中枢 - 启动 Celery Worker"
+                    require_venv
+                    export PYTHONPATH="backend${PYTHONPATH:+:$PYTHONPATH}"
+                    "$VENV_PYTHON" -m celery -A app.infrastructure.tasks.celery_app worker \
+                        --loglevel=info \
+                        --concurrency=2 \
+                        --queues=celery \
+                        "$@"
+                    ;;
+                beat)
+                    print_header "AI+合规智能中枢 - 启动 Celery Beat"
+                    require_venv
+                    export PYTHONPATH="backend${PYTHONPATH:+:$PYTHONPATH}"
+                    "$VENV_PYTHON" -m celery -A app.infrastructure.tasks.celery_app beat \
+                        --loglevel=info \
+                        "$@"
+                    ;;
+                *)
                     while [ $# -gt 0 ]; do
                         case "$1" in
                             --foreground)
@@ -684,6 +714,8 @@ main() {
                             ;;
                     esac
                     ;;
+            esac
+            ;;
         stop)
             target="$(parse_target all "${1:-}")"
             print_header "AI+合规智能中枢 - 停止服务"

docker/docker-compose.yml

@@ -58,7 +58,8 @@ services:
       retries: 5
     restart: unless-stopped
 
-  # PostgreSQL数据库 (可选，启用 DOCUMENT_REPOSITORY_BACKEND=postgres 时使用)
+  # PostgreSQL数据库 (启用 DOCUMENT_REPOSITORY_BACKEND=postgres 时使用；
+  # 合规分析历史记录 Direction B、DOCX 报告下载及 Finding Chat 持久化 Direction C 均依赖此服务)
   postgres:
     image: postgres:15-alpine
     container_name: postgres

docs/superpowers/specs/2026-06-05-next-steps-roadmap-design.md

@@ -0,0 +1,289 @@
+# AI+合规智能中枢 — 下一步开发与优化路线图（设计文档）
+
+- 日期：2026-06-05
+- 定位：试点 MVP 走向生产
+- 范围：全景清单 + 异步任务化（设计①）+ 法规感知闭环（设计②）深入方案 + 三阶段实施路线图
+- 作者：AI Regulations Team（brainstorming 产出）
+
+---
+
+## 0. 背景与目的
+
+本文档基于对当前仓库前后端真实代码的逐文件探查，结合四份愿景文档（`AI_Regulations_Report.pptx`、`AI_Regulations_Architecture.docx`、`01_Architecture.html`、`02_Architecture_Detail.html`）与最新开源 AI 技术调研，给出**下一步可继续开发与优化的方向清单**，并对两个最高价值方向给出可落地的深入设计。
+
+本文档是**方向性设计（spec）**，不是实施计划（plan）。阶段一、阶段二的具体落地由后续 writing-plans 环节拆分为分步计划。
+
+### 0.1 现状一句话
+
+后端是一套结构清晰的 DDD 风格 FastAPI RAG 系统（上传 → 解析 → 分块 → BGE-M3 嵌入 → Milvus → 混合检索 → 流式问答 + 合规分析），**真实可用**。但愿景文档中的多个旗舰能力（知识图谱、法规感知闭环、RBAC、EHS、异步化）目前为 **mock 或缺失**。
+
+---
+
+## 1. 现状盘点（基于真实代码）
+
+### 1.1 已实现且真实可用
+
+- **文档处理主链路**：`application/documents/services.py::DocumentCommandService.upload_and_process` — 存储 → 解析（阿里云 DocMind / 本地）→ 分块 → BGE-M3 嵌入 → Milvus 入库，含 `DocumentProcessingStore` 全程状态事件记录。
+- **混合检索**：`application/knowledge/services.py::KnowledgeRetrievalService` — Dense（`DenseRetriever`）+ BM25（jieba）+ Reciprocal Rank Fusion + 可选 Cross-Encoder 重排。
+- **流式 RAG 问答**：`application/agent/services.py::AgentConversationService.stream_chat` + `api/routes/rag.py` — 真实检索 + 引文 + 会话历史 + SSE。
+- **合规分析管线**：`application/compliance/pipeline.py` — clause_split → retrieve → gap_check → conclusion，真实 LLM + 真实检索，SSE 流式（`api/routes/compliance.py::analyze_stream`）。
+- **状态/健康面板**：`api/routes/status.py` + 前端 `StatusPage.tsx` — Milvus/MinIO/BM25/Reranker/会话实时状态。
+- **存储后端**：PostgreSQL / MinIO 适配器齐全；JSON 与 Postgres 双后端可切换。
+- **前端**：React 19 + Vite + Tailwind，6 个页面（Overview/Status/Perception/Docs/Compliance/RagChat）。
+
+### 1.2 愿景已规划但代码缺失或为 mock
+
+| 能力 | 愿景出处 | 代码现状 |
+|------|---------|---------|
+| 知识图谱 / Neo4j 多跳推理 | 架构图 L4/L5、Slide 5 | 全代码 0 处 neo4j/graph |
+| 法规感知自动更新闭环 | 01_Architecture.html L157-193、Slide 11 | `PerceptionService` 喂 `MockEventStore`（20 条死数据） |
+| 认证 / RBAC / 审计日志 | Slide 12 四角色权限矩阵 | 全代码 0 处 auth/jwt/rbac；`main.py` CORS=`*` |
+| 异步任务 / Worker 集群 | 架构图"Worker 集群"、Slide 9 | `app/workers/` 空目录；处理全同步 |
+| EHS 隐患识别（SIF/四维根因） | Slide 7 | 未实现 |
+| 多渠道推送（Email/Teams/飞书） | Slide 8 | 未实现 |
+| 闭环整改跟踪、可观测性 | 架构图右栏 | 缺失 |
+
+### 1.3 关键发现
+
+- **`requirements.txt:28` 已有 `celery>=5.3.0` + `redis>=4.5.0`**，`docker-compose.yml` 已配 Redis 7，`settings.py` 已有 redis 配置 —— **异步化是"接线"，不是"从零搭建"**。
+- **`DocumentProcessingStore` 已能记录 run 状态/状态事件** —— 是天然的任务进度表。
+- **`PerceptionService.analyze_event` 的 LLM 影响分析与 RAG 关联检索是真的** —— 感知闭环缺的只是前半段（采集 → Diff → 入库）。
+- 后端正处于 legacy 迁移期：`services/*`、`workflows/*` 为兼容层（见 `docs/architecture/backend-project-architecture.md`）。
+
+---
+
+## 2. 全景机会清单
+
+类型标记：`[新能力]`=愿景缺口补齐，`[加固]`=已实现能力优化。价值 ★（1-5），工作量 S/M/L。
+
+### P0 — 生产地基（阻断"走向生产"的硬伤）
+
+| # | 机会点 | 类型 | 现状证据 | 价值 | 工作量 |
+|---|--------|------|---------|------|--------|
+| 1 | 异步任务化（Celery + 已配 Redis）：解析/嵌入/感知/推送下沉 worker | 加固 | `workers/` 空；`documents.py:34` 上传同步阻塞 | ★★★★★ | L |
+| 2 | 认证 + RBAC + 审计日志，收紧 CORS | 新能力 | 0 处 auth；`main.py` CORS=`*`；Slide 12 | ★★★★★ | M |
+| 3 | 会话 & 任务持久化（内存 → Redis/PG） | 加固 | `bootstrap.py:254` 内存会话；`compliance.py:25` 内存字典 | ★★★★ | M |
+| 4 | 基础可观测性（Prometheus + 结构化日志 + 追踪） | 加固 | 仅 loguru；架构图右栏全缺 | ★★★ | M |
+
+### P1 — 高价值能力补齐 + RAG 质量
+
+| # | 机会点 | 类型 | 现状证据 | 价值 | 工作量 |
+|---|--------|------|---------|------|--------|
+| 5 | 启用并升级 Reranker（`bge-reranker-v2.5-gemma2-lightweight`） | 加固 | `settings.py:113` 默认关；管线已写好 | ★★★★ | S |
+| 6 | Agentic 检索（查询改写/意图理解/多路召回） | 加固 | `agent/services.py` 直接 retrieve，无 rewrite/HyDE | ★★★★ | M |
+| 7 | 知识图谱 / GraphRAG（Neo4j + LightRAG v1.5） | 新能力 | 0 处 neo4j；LightRAG v1.5 原生支持 | ★★★★★ | L |
+| 8 | 法规感知自动更新闭环（真实采集 + 版本 Diff + 增量重索引） | 新能力 | `perception/services.py` 用 MockEventStore | ★★★★★ | L |
+| 9 | 引文置信度评分（Slide 5 承诺"置信度评分+页码溯源"） | 加固 | `rag.py` sources 无 confidence | ★★★ | S |
+| 10 | 检索评估 harness（recall@k / faithfulness） | 加固 | `tests/` 需真实服务，无离线 RAG 评估 | ★★★ | M |
+
+### P2 — 视野扩展（独立子项目）
+
+| # | 机会点 | 类型 | 价值 | 工作量 |
+|---|--------|------|------|--------|
+| 11 | EHS 隐患识别（SIF 评分 + 四维根因 + ISO 45001 扫描，Slide 7） | 新能力 | ★★★★ | L |
+| 12 | 多渠道推送 + 订阅规则引擎（Email/Teams/飞书，Slide 8） | 新能力 | ★★★ | M |
+| 13 | 闭环整改跟踪（任务派发 → 进度 → 验收归档） | 新能力 | ★★★ | M |
+| 14 | 企业系统集成（PLM/ERP/OA/MES Webhook） | 新能力 | ★★ | L |
+| 15 | MinerU 3.1 升级（已转 Apache 协议，VLM 解析）作本地兜底 | 加固 | ★★ | S |
+| 16 | 前端加固（清 mock 数据、补 error/loading 态、KG 可视化、登录态） | 加固 | ★★★ | M |
+| 17 | 收口 legacy 迁移（`services/*`、`workflows/*` 按架构文档归位） | 加固 | ★★ | M |
+
+---
+
+## 3. 深入设计 ① — 异步任务化
+
+### 3.1 问题
+
+`upload_document`（`api/routes/documents.py:34`）在单个 HTTP 请求内同步跑完 存储 → 解析（阿里云云端可达 900 秒，`settings.py:49`）→ 嵌入 → Milvus 入库。大体量 GB 标准必然超时；`compliance.py` 的 `/analyze` 为假异步（立即返回 mock）；perception 爬取闭环无执行载体。PPT Slide 9 已将"大文件性能"列为关键挑战，对策正是"流式处理 + 异步队列 + 实时进度"。
+
+### 3.2 关键前提：基建已就位
+
+- `requirements.txt:28` 已含 `celery>=5.3.0` + `redis>=4.5.0`
+- `docker-compose.yml:46` Redis 7 已配置；`settings.py:64` 已有 redis 连接配置
+- `PostgresDocumentProcessingStore` 已记录 run 状态/状态事件 —— 天然任务进度表
+- `app/workers/` 为空目录（唯一缺口）
+
+### 3.3 架构（遵循 AGENTS.md 的 `api → application → domain ports → infrastructure`）
+
+```
+api/routes/documents.py  POST /upload
+        │ 1. 存二进制 + 建 Document 记录（快，同步）
+        │ 2. enqueue task → 立即返回 {doc_id, status:"queued", run_id}
+        ▼
+infrastructure/tasks/            ← 新增
+   celery_app.py                 broker=redis, backend=redis
+   document_tasks.py             @task process_document(doc_id) → DocumentCommandService
+        │ 复用现有 upload_and_process 的 parse→embed→index 段
+        ▼
+application/documents/services.py（拆分：store 与 process 解耦）
+        │ 每阶段写 DocumentProcessingStore（已存在）→ 进度可查
+        ▼
+api/routes/documents.py  GET /status/{doc_id}  ← 已存在，读 run 状态即可
+```
+
+### 3.4 落地步骤（增量、不破坏现有同步路径）
+
+1. 新增 `infrastructure/tasks/celery_app.py` — Celery 实例，broker/backend 指向已配 Redis。
+2. 拆分 `upload_and_process` → `store_document`（同步快）+ `process_document`（可异步），复用现有逻辑，零重写解析/嵌入代码。
+3. 新增 `document_tasks.py` — `@celery_app.task` 包裹 `process_document`，失败用 `tenacity`（已在 deps）重试 + 死信。
+4. 改 `documents.py` 上传 — 默认入队（保留 `?sync=true` 同步回退便于演示）；`GET /status/{doc_id}` 读 `DocumentProcessingStore` 返回阶段进度。
+5. 前端 `DocsPage.tsx` — 上传后轮询/SSE 进度条（架构图 Worker"心跳/状态上报"已是既定设计）。
+6. `dev.sh`/`dev.bat` 加 worker 启动：`celery -A app.infrastructure.tasks.celery_app worker`。
+
+### 3.5 工作量与风险
+
+- **M（中），3-5 天。**
+- 最大风险：Celery worker 进程内 `PYTHONPATH=backend` 与 bootstrap `lru_cache` 单例需重新初始化 —— 可控，因 bootstrap 已是懒加载。
+- YAGNI 边界：本期仅异步化"文档处理"一条链；compliance/perception 复用同一 Celery 基建后续接入。
+
+---
+
+## 4. 深入设计 ② — 法规感知自动更新闭环
+
+### 4.1 问题
+
+感知闭环是愿景旗舰能力（`01_Architecture.html` L157-193、Slide 11）。现状：`PerceptionService` 喂 `MockEventStore`（`mock_event_store.py:7`，20 条手写死数据），`list_events`/`stats` 全静态，`source_url` 真实但从不访问。**LLM 影响分析与 RAG 关联检索是真的** —— 闭环缺的是前半段：真实采集 → 变更感知（Diff）→ 入库。
+
+### 4.2 六步现状对照
+
+| 步骤 | 愿景设计 | 现状 | 本期目标 |
+|------|---------|------|---------|
+| ① 法规源监控 | 定时爬国标网/MIIT/UN-ECE/EUR-Lex | ❌ 无 | ✅ 适配器+定时 |
+| ② 智能变更感知 | NLP 比对新旧版本 Diff | ❌ 无 | ✅ 内容指纹+LLM Diff |
+| ③ 自动解析入库 | MinerU→分块→BGE-M3→Milvus | ✅ 已有（复用设计①管线） | ✅ 接线 |
+| ④ 知识图谱更新 | Neo4j 关系同步 | ❌ 无 | ⏭️ 本期不做（归 GraphRAG 专项） |
+| ⑤ 差距分析&推送 | AI 比对+按角色推送 | 🟡 analyze_event 已有分析，无推送 | 🟡 分析复用，推送下期 |
+| ⑥ 触发整改闭环 | 整改任务跟踪 | ❌ 无 | ⏭️ 下期 |
+
+本期聚焦 ①②③，复用设计①异步管线与已有解析/嵌入/检索/分析能力。
+
+### 4.3 架构（端口与适配器）
+
+```
+domain/perception/ports.py        ← 新增
+   RegulationSource (Protocol)    fetch_latest() → list[RawRegulation]
+   EventStore (Protocol)          抽象掉 MockEventStore（现有 mock 成为一个实现）
+   ChangeDetector (Protocol)      diff(old, new) → ChangeSet
+
+infrastructure/perception/
+   sources/                       ← 新增，每法规源一个适配器
+     gb_openstd_source.py         国标网 (openstd.samr.gov.cn)
+     miit_source.py               工信部
+     base_html_source.py          通用 HTML 抓取基类（httpx 已在 deps）
+   postgres_event_store.py        ← 替换 MockEventStore（真实持久化）
+   content_fingerprint_detector.py  哈希指纹 + LLM 语义 Diff
+
+application/perception/services.py（扩展现有）
+   ingest_cycle()                 ← 新增：①抓取 → ②Diff → ③入队解析（设计①的 task）
+   （list_events/analyze_event 保持不变，已是真实逻辑）
+
+infrastructure/tasks/perception_tasks.py  ← 复用设计①的 Celery
+   @task perception_crawl_cycle()  Celery Beat 定时触发
+```
+
+### 4.4 关键设计决策
+
+1. **接口契约零改动**：`PostgresEventStore` 输出与 `MockEventStore` 完全相同的 dict 结构（mock_event_store.py 的 20 字段），故 `perception.ts` 前端契约、`PerceptionPage.tsx`、`analyze_event` 全部不改。Mock 退化为种子数据/演示回退，通过 `perception_event_store=mock|postgres` 开关切换（对齐现有 `document_repository_backend` 模式）。
+2. **变更感知分两层**：廉价层（内容哈希指纹判断"是否变了"）+ 智能层（变了才调 LLM 做"新增/修订/废止条款"结构化 Diff，复用 `get_llm_client`，prompt 风格照搬 `compliance/pipeline.py::_extract_json`）。
+3. **合规防滥用**：尊重 `robots.txt` + 限速 + `tenacity` 重试 + 抓取失败不污染已有数据；适配器隔离，单源故障不影响其它。
+4. **入库复用设计①**：抓到新法规 PDF → 丢进 `process_document` task → 自动走完解析/嵌入/索引。
+
+### 4.5 落地步骤
+
+1. 抽 `domain/perception/ports.py`，让现有 `MockEventStore` 实现 `EventStore` 协议（纯重构，行为不变）。
+2. `PostgresEventStore` + 建表（参照 `aliyun_parser/schema.sql` 风格）+ 20 条 mock 作 seed。
+3. 先做 1 个真实源适配器（建议国标网，结构最稳）跑通 ①→②→③，验证端到端。
+4. `content_fingerprint_detector` + LLM Diff。
+5. `perception_crawl_cycle` Celery Beat 定时（每日）；新事件落 PostgresEventStore + 新法规入队解析。
+6. 前端 `PerceptionPage` 加"最近同步时间/本次新增 N 条"（stats 已有结构，加 2 字段）。
+
+### 4.6 工作量与风险
+
+- **L（大），5-8 天**，依赖设计①先落地（共用 Celery）。
+- 最大风险：外部源站不可控（改版/反爬）。缓解：适配器隔离 + mock 永久保留为回退 + 先攻 1 个源验证（对齐 Slide 13"选取 2-3 个场景 POC 验证"）。
+- YAGNI 边界：④Neo4j 图谱、⑥整改闭环、多渠道推送本期不做，各自独立子项目。
+
+---
+
+## 5. 三阶段实施路线图
+
+### 5.1 核心主线
+
+项目不缺"能力点"，缺的是**让能力点从同步脚本变成可运营的系统**。主线是**异步化基建**：既是文档处理性能解药（设计①），又是感知闭环执行载体（设计②），也是未来 EHS/推送的统一底座。路线图以它为"第 0 块地基"，其余能力挂载其上。
+
+### 5.2 与 PPT 三阶段映射（Slide 10）
+
+```
+PPT 规划                  代码现状          本路线图补齐
+─────────────────────────────────────────────────────
+一阶段 知识库+基础问答   ✅ 大体已实现     → 加固 (P0/P1)
+二阶段 文档审查+API集成  🟡 审查真/API半   → 异步化+感知闭环
+三阶段 EHS+个性化+图谱   ❌ 基本缺失       → 子项目 (P2)
+```
+
+### 5.3 阶段一 · 生产地基（2-3 周）— "让它扛得住生产"
+
+| 顺序 | 事项 | 依据 | 估时 |
+|------|------|------|------|
+| 1 | 设计① 异步任务化 | celery/redis 已在 deps，workers/ 空 | M, 3-5d |
+| 2 | 认证 + RBAC + 审计 + 收紧 CORS | 0 处 auth；Slide 12 矩阵 | M, 3-5d |
+| 3 | 会话/任务持久化（内存 → Redis/PG） | InMemoryConversationStore 重启即丢 | M, 2-3d |
+| 4 | 快赢：启用 Reranker | settings 默认关，管线已写好 | S, 0.5d |
+
+### 5.4 阶段二 · 招牌能力（2-3 周）— "让它有亮点"
+
+建议**感知闭环优先于图谱**（前者复用阶段一异步基建，ROI 更高）。
+
+| 顺序 | 事项 | 依据 | 估时 |
+|------|------|------|------|
+| 5 | 设计② 法规感知闭环 ①②③ | MockEventStore → 真实采集 | L, 5-8d |
+| 6 | Agentic 检索（查询改写/意图理解） | Slide 5"意图理解"，代码是直检索 | M, 3-4d |
+| 7 | 引文置信度评分 + 基础可观测性 | Slide 5 承诺；架构图右栏全缺 | S+M, 3-4d |
+
+### 5.5 阶段三 · 视野扩展（按需，各为独立子项目）— "让它成体系"
+
+每项单独 brainstorm → spec → 实施，本期不细化：
+
+- 知识图谱 / GraphRAG（Neo4j + LightRAG v1.5，接感知闭环第④步）
+- EHS 隐患识别（SIF + 四维根因，Slide 7）
+- 多渠道推送 + 订阅规则引擎（Slide 8）→ 闭环整改跟踪（第⑤⑥步）
+- 持续加固：MinerU 3.1 升级、前端清 mock、legacy 收口
+
+### 5.6 决策建议
+
+1. 强烈建议按阶段顺序：地基 → 招牌 → 扩展。跳过地基直接做招牌，会在生产暴露超时/无鉴权/数据丢失。
+2. 阶段一第 4 项（Reranker）可立即做 —— 半天见效，与其它解耦，适合先尝甜头。
+3. 阶段二二选一先行：要 demo 冲击力选"感知闭环"；要问答质量选"Agentic 检索"。
+
+---
+
+## 6. 最新 AI 技术调研（支撑选型）
+
+| 技术 | 版本/状态（2026） | 对应机会点 |
+|------|------------------|-----------|
+| LightRAG | v1.5.0（2026-06），EMNLP 2025；KG-RAG，原生支持 Neo4j + MinerU/Docling，含 Web UI 图谱可视化 | #7 知识图谱 |
+| MinerU | v3.1.0（2026-04），协议转为 Apache 2.0 基础的开源协议，VLM 解析（MinerU2.5-Pro），109 语言 OCR | #15 本地解析兜底 |
+| BGE Reranker | `bge-reranker-v2.5-gemma2-lightweight`（token 压缩 + 分层轻量化，生产推荐） | #5 Reranker 升级 |
+| BGE-M3 | 100+ 语言，8192 上下文，dense+sparse+colbert 统一（现已在用） | 现有嵌入 |
+| RAGFlow | 2026 支持 DeepSeek v4 / MCP / 跨语言查询；agentic RAG 参考实现 | #6 Agentic 检索参考 |
+
+---
+
+## 7. 验收与边界
+
+### 7.1 本文档明确不做（YAGNI）
+
+- 阶段三所有子项目（图谱、EHS、推送、整改闭环、企业集成）仅列方向，不在本期展开。
+- 移动端适配（AGENTS.md 明确 desktop-first）。
+- 感知闭环的第④⑤⑥步（图谱同步、推送、整改）。
+
+### 7.2 架构约束（必须遵守）
+
+- 后端遵循 `api → application → domain ports → infrastructure`（`docs/architecture/backend-project-architecture.md` 为权威）。
+- 新业务逻辑不得落入 `services/*`、`workflows/*`（legacy 迁移区）。
+- `shared/bootstrap.py` 为依赖装配 composition root，新依赖在此接线。
+- 后端注释/docstring 全英文（AGENTS.md 规范）。
+
+### 7.3 下一步
+
+经用户审阅本 spec 后，对**阶段一**（异步任务化优先）调用 writing-plans 拆分为分步实施计划。

docs/superpowers/specs/2026-06-05-perception-intelligence-design.md

@@ -0,0 +1,328 @@
+# Regulatory Signals Intelligence Enhancement — Design Spec
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Replace the 20-item hardcoded MockEventStore with real regulatory data from Chinese and international sources, add LLM-driven structured extraction, impact assessment, and semantic change diff — all accessible through a manual-trigger crawl in the frontend.
+
+**Architecture:** Crawler Service (httpx + BeautifulSoup) → PostgreSQL EventStore → LLM Pipeline (extract → assess → diff) → existing PerceptionService interface. New code follows `api → application → domain ports → infrastructure` layering; no new files in `services/*` or `workflows/*`; `shared/bootstrap.py` is the composition root.
+
+**Tech Stack:** httpx, BeautifulSoup4, sentence-transformers (for diff), existing LLM factory (deepseek/qwen), existing KnowledgeRetrievalService (RAG), PostgreSQL (already available), existing SSE infrastructure.
+
+---
+
+## 1. Data Sources
+
+| Source | URL | Method | Coverage |
+|--------|-----|--------|----------|
+| CATARC 汽车标准 | `https://www.catarc.org.cn/bzzxd/qcbz/index.html` | httpx + BeautifulSoup (static pages) | 国家/行业汽车标准列表 |
+| 国标委强制性标准 | `https://openstd.samr.gov.cn/bzgk/std/std_list_type?p.p1=1&p.p2=车&p.p90=circulation_date&p.p91=desc` | httpx + JSON API parse | 强制性国家标准，按"车"过滤 |
+| 国标委推荐性标准 | `https://openstd.samr.gov.cn/bzgk/std/std_list_type?p.p1=2&p.p2=车&p.p90=circulation_date&p.p91=desc` | httpx + JSON API parse | 推荐性国家标准，按"车"过滤 |
+| EUR-Lex | RSS + CELLAR REST API | pyeurlex / httpx | EU AI Act, automotive directives |
+| UN R155/R156 | CELLAR REST API (CELEX lookup) | httpx | UN-ECE cybersecurity/OTA regulations |
+
+Crawl is **manual-trigger only** — no cron/Celery Beat. Admin clicks "刷新数据源" in the frontend UI.
+
+---
+
+## 2. Database Schema
+
+### New table: `regulation_events`
+
+```sql
+CREATE TABLE IF NOT EXISTS regulation_events (
+    id              TEXT PRIMARY KEY,          -- sha256(source + standard_code)[:12]
+    source          TEXT NOT NULL,             -- 'CATARC' | '国标委' | 'EUR-Lex' | 'UN-ECE'
+    source_label    TEXT,                      -- Human-readable source label
+    standard_code   TEXT NOT NULL,             -- e.g. "GB 18384-2025", "EU/2024/1689"
+    title           TEXT NOT NULL,
+    summary         TEXT,                      -- Crawled abstract or first paragraph
+    full_text_url   TEXT,                      -- Original page URL
+    status          TEXT,                      -- 'enacted' | 'draft' | 'consultation'
+    impact_level    TEXT,                      -- 'high' | 'medium' | 'low' (LLM-assigned)
+    published_at    DATE,
+    effective_at    DATE,
+    category        TEXT,
+    tags            TEXT[],
+    -- LLM structured extraction
+    obligations     JSONB,       -- [{text, deontic, subject, object, condition}]
+    deadlines       JSONB,       -- [{date, description}]
+    scope           TEXT,        -- Applicability scope summary
+    penalties       TEXT,        -- Penalty / consequence summary
+    -- Change tracking
+    content_hash    TEXT,        -- SHA256 of crawled full text
+    previous_hash   TEXT,        -- Hash from prior crawl (NULL on first crawl)
+    change_summary  TEXT,        -- LLM-generated description of changes
+    changed_sections JSONB,      -- [{old_text, new_text, change_type}] where cosine<0.85
+    -- Impact assessment
+    affected_docs   JSONB,       -- [{doc_id, doc_name, score, key_clauses, recommendation}]
+    -- Metadata
+    crawled_at      TIMESTAMPTZ DEFAULT now(),
+    processed_at    TIMESTAMPTZ,
+    raw_storage_key TEXT         -- MinIO path for raw HTML/PDF (optional)
+);
+
+CREATE INDEX IF NOT EXISTS regulation_events_source_date
+    ON regulation_events (source, published_at DESC);
+CREATE INDEX IF NOT EXISTS regulation_events_impact_date
+    ON regulation_events (impact_level, published_at DESC);
+CREATE INDEX IF NOT EXISTS regulation_events_tags
+    ON regulation_events USING gin(tags);
+```
+
+---
+
+## 3. Backend Architecture
+
+### 3.1 File Map
+
+**New files (infrastructure layer):**
+- `backend/app/infrastructure/perception/crawlers/catarc_crawler.py` — CATARC scraper
+- `backend/app/infrastructure/perception/crawlers/guobiao_crawler.py` — 国标委 JSON API crawler
+- `backend/app/infrastructure/perception/crawlers/eurlex_crawler.py` — EUR-Lex RSS + CELLAR
+- `backend/app/infrastructure/perception/crawlers/base.py` — Abstract base class
+- `backend/app/infrastructure/perception/postgres_event_store.py` — PostgresEventStore (replaces MockEventStore)
+- `backend/app/infrastructure/perception/llm_pipeline.py` — Extract / assess / diff pipeline
+
+**New files (application layer):**
+- `backend/app/application/perception/crawl_service.py` — Orchestrates crawlers + LLM pipeline, exposes `run_crawl(sources)` + progress generator
+
+**Modified files:**
+- `backend/app/api/routes/perception.py` — Add `POST /crawl`, `GET /crawl/status` (SSE), `POST /events/{id}/process`, `GET /events/{id}/diff`
+- `backend/app/shared/bootstrap.py` — Wire `PostgresEventStore` + `CrawlService` + `LlmPipeline` when `DOCUMENT_REPOSITORY_BACKEND=postgres`; fallback to `MockEventStore` when `json`
+- `backend/app/config/settings.py` — Add `perception_crawl_timeout_seconds`, `perception_max_events_per_source`
+
+**Unchanged files:**
+- `backend/app/application/perception/services.py` — `PerceptionService` interface unchanged; only `_store` swap
+- `backend/app/infrastructure/perception/mock_event_store.py` — Kept for `json` backend mode
+
+### 3.2 Domain Port (Abstract Interface)
+
+```python
+# backend/app/infrastructure/perception/base_event_store.py
+from abc import ABC, abstractmethod
+
+class BaseEventStore(ABC):
+    @abstractmethod
+    def all(self) -> list[dict]: ...
+    @abstractmethod
+    def get(self, event_id: str) -> dict | None: ...
+    @abstractmethod
+    def filter(self, source=None, impact_level=None, limit=50) -> list[dict]: ...
+    @abstractmethod
+    def stats(self) -> dict: ...
+    @abstractmethod
+    def upsert(self, event: dict) -> None: ...      # new — needed for crawl writes
+    @abstractmethod
+    def get_by_standard_code(self, code: str) -> dict | None: ...  # for change detection
+```
+
+`MockEventStore` and `PostgresEventStore` both implement this interface.
+
+### 3.3 Crawler Base Contract
+
+```python
+# backend/app/infrastructure/perception/crawlers/base.py
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+
+@dataclass
+class RawEvent:
+    source: str
+    source_label: str
+    standard_code: str
+    title: str
+    summary: str
+    full_text_url: str
+    status: str           # 'enacted' | 'draft' | 'consultation'
+    published_at: str     # YYYY-MM-DD string
+    effective_at: str | None
+    category: str
+    tags: list[str]
+    raw_text: str         # full crawled text for hashing + LLM
+
+class BaseCrawler(ABC):
+    @abstractmethod
+    def fetch(self, limit: int = 50) -> list[RawEvent]: ...
+```
+
+### 3.4 LLM Pipeline
+
+```python
+# backend/app/infrastructure/perception/llm_pipeline.py
+
+class LlmPipeline:
+    """Runs three sequential LLM steps on a regulation event."""
+
+    def extract_structure(self, event: dict) -> dict:
+        """Step 1: Extract obligations, deadlines, scope, penalties, impact_level.
+        
+        Returns dict with keys: obligations, deadlines, scope, penalties, impact_level.
+        Uses JSON-mode or structured prompt; model retries once on parse failure.
+        """
+
+    def assess_impact(self, event: dict, retrieval_service) -> list[dict]:
+        """Step 2: RAG-based impact on existing knowledge base documents.
+        
+        Query = standard_code + title + first obligation texts.
+        Returns list of {doc_id, doc_name, score, key_clauses, recommendation}.
+        """
+
+    def compute_diff(self, old_text: str, new_text: str) -> dict:
+        """Step 3: Semantic diff between old and new regulation text.
+        
+        Splits both texts by paragraph. Calls existing EmbeddingService (text-embedding-v3
+        via EMBEDDING_BASE_URL) to embed each paragraph, then computes cosine similarity.
+        Changed paragraphs (cosine < 0.85) sent to LLM for change_type classification:
+          'tightened' | 'relaxed' | 'added' | 'removed'
+        Returns {changed_sections: [...], change_summary: str}.
+        Only called when content_hash differs from previous_hash.
+        """
+```
+
+### 3.5 CrawlService
+
+```python
+# backend/app/application/perception/crawl_service.py
+
+class CrawlService:
+    def __init__(self, crawlers, event_store, llm_pipeline, retrieval_service): ...
+
+    def run_crawl(self, sources: list[str] | None = None) -> Generator[dict, None, None]:
+        """Manual-trigger crawl. Yields progress SSE dicts:
+          {event: 'progress', data: {source, fetched, new, updated, stage}}
+          {event: 'done', data: {total_new, total_updated, duration_ms}}
+          {event: 'error', data: {source, message}}
+        
+        For each crawler:
+          1. fetch() RawEvents
+          2. hash check vs stored event → skip if unchanged
+          3. upsert raw event to DB
+          4. run LLM pipeline (extract → assess → diff)
+          5. upsert enriched event to DB
+          6. yield progress
+        """
+```
+
+---
+
+## 4. API Endpoints
+
+### Existing (unchanged interface, new store backend)
+- `GET /api/v1/perception/stats`
+- `GET /api/v1/perception/events`
+- `GET /api/v1/perception/events/{id}`
+- `POST /api/v1/perception/events/{id}/analyze` (streaming)
+
+### New endpoints
+
+```
+POST /api/v1/perception/crawl
+  Body: { sources?: ["CATARC", "国标委", "EUR-Lex", "UN-ECE"] }
+  Response: text/event-stream (SSE)
+  Auth: requires current_user (admin/legal role)
+  Streams progress events until done or error.
+
+POST /api/v1/perception/events/{id}/process
+  Trigger LLM pipeline for a single already-crawled event.
+  Response: { status: "ok", processed_at: "..." }
+  Auth: requires current_user
+
+GET /api/v1/perception/events/{id}/diff
+  Returns: { changed_sections: [...], change_summary: str, previous_hash: str }
+  Returns 404 if no diff available (first crawl or no change detected).
+```
+
+---
+
+## 5. Frontend Changes
+
+### 5.1 New: Crawl Control Bar (top of PerceptionPage)
+
+Above the stats-bar, add a `<CrawlBar>` component:
+- "刷新数据源" button — triggers `POST /crawl` (all sources)
+- Inline progress display: shows SSE progress events as a mini status line
+  - e.g. "CATARC: 抓取中… | 国标委: 12 条新增 | EUR-Lex: 等待中"
+- On completion: shows "更新完成 — 新增 N 条，更新 M 条"
+- Disabled while crawl is in progress (prevents double-trigger)
+
+### 5.2 Signal Card Enhancement
+
+Existing cards get two new indicators:
+- **NEW badge** — shown when `crawled_at` is within last 24h (green dot)
+- **CHANGED badge** — shown when `previous_hash != content_hash` and `change_summary` exists
+
+### 5.3 Right Panel — Structured Tab
+
+Right detail panel adds a tab bar: **概览 | 义务条款 | 影响评估 | 变更对比**
+
+**义务条款 tab:**
+- Table: 义务描述 | 主体 | 对象 | 截止日期
+- Tags for deontic type: 强制 / 禁止 / 允许
+- Shows `obligations[]` + `deadlines[]` from DB
+
+**影响评估 tab:**
+- Replaces hardcoded MOCK_DOCS with real `affected_docs[]` from DB
+- Each row: document name, similarity score (%), key clause excerpt, LLM recommendation
+- "Run fresh assessment" button → triggers `POST /events/{id}/process`
+
+**变更对比 tab:**
+- Only visible when `change_summary` is non-null
+- Top: `change_summary` text (LLM prose)
+- Below: diff table with old/new paragraph pairs, change_type badge per row
+- Hidden (tab disabled) on first-crawl events with no prior version
+
+### 5.4 Existing behavior preserved
+- `analyze` streaming (AI analysis) unchanged
+- Search/filter (source, impact) unchanged — now hits real DB data
+- Stats bar — now reflects real counts from PostgreSQL
+
+---
+
+## 6. Settings Additions
+
+```python
+# backend/app/config/settings.py additions
+perception_crawl_timeout_seconds: int = Field(default=120, ...)
+perception_max_events_per_source: int = Field(default=100, ...)
+perception_diff_similarity_threshold: float = Field(default=0.85, ...)
+```
+
+```env
+# .env additions
+PERCEPTION_CRAWL_TIMEOUT_SECONDS=120
+PERCEPTION_MAX_EVENTS_PER_SOURCE=100
+PERCEPTION_DIFF_SIMILARITY_THRESHOLD=0.85
+```
+
+---
+
+## 7. Dependencies
+
+```
+# requirements.txt additions
+httpx>=0.27.0              # already likely present; confirm
+beautifulsoup4>=4.12.0     # HTML parsing for CATARC
+lxml>=5.0.0                # BeautifulSoup parser backend
+# sentence-transformers NOT added — diff uses existing text-embedding-v3 API (EMBEDDING_BASE_URL)
+```
+
+No new infrastructure required (PostgreSQL + MinIO + Milvus already available).
+
+---
+
+## 8. Backward Compatibility
+
+- `DOCUMENT_REPOSITORY_BACKEND=json` → `bootstrap.py` uses `MockEventStore` (unchanged behavior)
+- `DOCUMENT_REPOSITORY_BACKEND=postgres` → uses `PostgresEventStore`
+- Migration: run `CREATE TABLE` SQL on first startup (idempotent `CREATE TABLE IF NOT EXISTS`)
+- Existing 20 mock events are not seeded to PostgreSQL; PostgreSQL starts empty until first crawl
+
+---
+
+## 9. Out of Scope (this phase)
+
+- Automatic/scheduled crawling (Celery Beat) — manual trigger only
+- Playwright-based JS-rendered pages — all target sites work with httpx
+- Knowledge Graph (Neo4j / LightRAG) — future phase
+- Email/Slack webhook notifications — future phase
+- User-facing diff history (versioning beyond one prior snapshot) — future phase

docs/superpowers/specs/2026-06-08-compliance-enhancement-design.md

@@ -0,0 +1,459 @@
+# Compliance Analysis Enhancement Design
+
+**Date:** 2026-06-08
+**Directions:** A (Analysis Quality) + B (History & Reports) + C (Deep Chat)
+**Approach:** Three independent but coordinated feature sets sharing one DB schema (method one / structured tables).
+
+---
+
+## Goals
+
+1. **A — Analysis Quality:** Parallel clause processing (3-5× speed), fix `highlight_terms` bug (always returns empty), add LLM retry with tenacity, reserve `PassThroughReranker` for future cross-encoder work.
+2. **B — Analysis History & Reports:** Auto-save every completed analysis to PostgreSQL, history rail in UI, per-record DOCX export, delete with confirmation.
+3. **C — Deep Chat:** Per-finding persistent chat threads grounded in real retrieved text, LLM-generated suggestion questions, multi-turn memory.
+
+---
+
+## Architecture Overview
+
+### Layering Rules (must not be violated)
+
+```
+api/routes/         →  thin HTTP handlers, SSE generators only
+application/        →  orchestration logic (pipeline.py)
+domain/ports/       →  ABCs, no implementation
+infrastructure/     →  DB, docx, external calls
+shared/bootstrap.py →  composition root, wires everything
+```
+
+New business logic goes in `application/compliance/pipeline.py` and domain ports. Never in `services/*` or `workflows/*`.
+
+### Shared Database Schema (B + C)
+
+Three tables, created together so C's FK references are valid from day one:
+
+```sql
+CREATE TABLE compliance_analyses (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    created_at      TIMESTAMPTZ NOT NULL DEFAULT now(),
+    created_by      VARCHAR(255),
+    doc_name        VARCHAR(500),
+    standard_name   VARCHAR(500),
+    risk_score      INTEGER,
+    conclusion      TEXT,
+    actions         JSONB,
+    para_text       TEXT,
+    highlight_terms JSONB
+);
+
+CREATE TABLE compliance_findings (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+    seq         INTEGER NOT NULL,
+    title       VARCHAR(500),
+    description TEXT,
+    status      VARCHAR(50),
+    clause_ref  VARCHAR(200)
+);
+
+CREATE TABLE finding_chat_messages (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+    finding_id  UUID NOT NULL REFERENCES compliance_findings(id) ON DELETE CASCADE,
+    role        VARCHAR(20) NOT NULL,  -- 'user' | 'assistant'
+    content     TEXT NOT NULL,
+    created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+```
+
+---
+
+## Direction A — Analysis Quality
+
+### A1: Parallel Clause Processing
+
+**Current:** Route handler has a sequential `for i, clause in enumerate(clauses)` loop. Each iteration calls `retrieve_for_clause()` then `check_clause_compliance()` synchronously via `asyncio.to_thread`.
+
+**Change:** Extract a `process_single_clause(clause, idx, ...) -> dict` function in `pipeline.py`, then replace the loop with `asyncio.gather`:
+
+```python
+async def run_clauses_parallel(clauses, retrieval_svc, llm_client, standard_name, para_text):
+    tasks = [
+        asyncio.to_thread(process_single_clause, clause, i, retrieval_svc, llm_client, standard_name, para_text)
+        for i, clause in enumerate(clauses)
+    ]
+    return await asyncio.gather(*tasks, return_exceptions=True)
+```
+
+Results are yielded to the SSE stream in original order. Exceptions from individual clauses are caught and emitted as `{type: "error", clause_index: i}` events rather than crashing the whole stream.
+
+### A2: Fix highlight_terms
+
+**Root cause:** `synthesize_conclusion()` passes the LLM response through `json.loads()` but the LLM often wraps output in markdown fences (` ```json ... ``` `), causing a parse failure and silent fallback to `[]`.
+
+**Fix in `pipeline.py`:**
+
+```python
+import re
+
+def _extract_json(text: str) -> dict:
+    """Strip markdown fences then parse JSON. Raises ValueError on failure."""
+    cleaned = re.sub(r"^```(?:json)?\s*|\s*```$", "", text.strip(), flags=re.MULTILINE)
+    return json.loads(cleaned)
+```
+
+Apply `_extract_json` in `synthesize_conclusion()` instead of bare `json.loads`. Wrap with `@retry` (see A3) so transient parse failures get a second attempt.
+
+### A3: LLM Retry with tenacity
+
+`tenacity` is already in `requirements.txt` but unused. Add to all LLM calls in `pipeline.py`:
+
+```python
+from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
+
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=1, max=4),
+    retry=retry_if_exception_type((httpx.HTTPError, ValueError)),
+    reraise=True,
+)
+def _call_llm_with_retry(client, prompt: str) -> str:
+    """Call LLM and return raw text. Retries on HTTP errors and JSON parse failures."""
+    ...
+```
+
+On final failure, the calling function catches and emits `{type: "error", text: "LLM call failed after 3 attempts"}` to the SSE stream.
+
+### A4: PassThroughReranker (future-ready stub)
+
+`domain/retrieval/ports.py` already defines a `Reranker` ABC. Add the no-op implementation:
+
+**New file:** `backend/app/infrastructure/retrieval/reranker.py`
+
+```python
+from app.domain.retrieval.ports import Reranker, RetrievedChunk
+
+class PassThroughReranker(Reranker):
+    """No-op reranker. Replace with CrossEncoderReranker when a local model is available."""
+
+    def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
+        return chunks[:top_k]
+```
+
+Register in `shared/bootstrap.py` as the default `Reranker` implementation.
+
+### A — Files Changed
+
+| File | Action |
+|------|--------|
+| `backend/app/application/compliance/pipeline.py` | Add `process_single_clause`, `run_clauses_parallel`, `_extract_json`, `_call_llm_with_retry` |
+| `backend/app/api/routes/compliance.py` | Replace sequential loop with `await run_clauses_parallel(...)` |
+| `backend/app/infrastructure/retrieval/reranker.py` | New — `PassThroughReranker` |
+| `backend/app/shared/bootstrap.py` | Register `PassThroughReranker` |
+
+---
+
+## Direction B — History & Reports
+
+### B1: Domain Port
+
+**New file:** `backend/app/domain/compliance/ports.py`
+
+```python
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional
+
+@dataclass
+class FindingRecord:
+    id: str
+    analysis_id: str
+    seq: int
+    title: str
+    description: str
+    status: str
+    clause_ref: Optional[str] = None
+
+@dataclass
+class AnalysisRecord:
+    id: str
+    created_at: datetime
+    created_by: Optional[str]
+    doc_name: str
+    standard_name: str
+    risk_score: int
+    conclusion: str
+    actions: list
+    para_text: str
+    highlight_terms: list
+    findings: list[FindingRecord] = field(default_factory=list)
+
+class ComplianceRepository(ABC):
+    @abstractmethod
+    def save_analysis(self, record: AnalysisRecord) -> str: ...
+    @abstractmethod
+    def list_analyses(self, limit: int = 50, offset: int = 0) -> list[AnalysisRecord]: ...
+    @abstractmethod
+    def get_analysis(self, analysis_id: str) -> Optional[AnalysisRecord]: ...
+    @abstractmethod
+    def delete_analysis(self, analysis_id: str) -> None: ...
+    @abstractmethod
+    def save_message(self, analysis_id: str, finding_id: str, role: str, content: str) -> str: ...
+    @abstractmethod
+    def get_messages(self, finding_id: str) -> list[dict]: ...
+```
+
+### B2: PostgresComplianceRepository
+
+**New file:** `backend/app/infrastructure/compliance/repository.py`
+
+Implements `ComplianceRepository` using `psycopg2` (already in requirements). Connection string from `settings.DATABASE_URL`. Key methods:
+
+- `save_analysis`: INSERT into `compliance_analyses`, then bulk INSERT findings into `compliance_findings`, return `analysis_id` (UUID string).
+- `list_analyses`: SELECT with JOIN on findings count, ORDER BY `created_at DESC`, supports limit/offset.
+- `get_analysis`: SELECT analysis + all findings by `analysis_id`.
+- `delete_analysis`: DELETE cascades to findings and chat messages via FK.
+- `save_message` / `get_messages`: INSERT/SELECT on `finding_chat_messages`.
+
+Uses a connection pool (simple `psycopg2.pool.ThreadedConnectionPool`, min=1, max=5).
+
+### B3: Auto-save Hook
+
+In the SSE generator in `compliance.py`, after the `done` event is assembled:
+
+```python
+# After yielding the done event
+if repo is not None:
+    record = AnalysisRecord(
+        id="",  # will be assigned by DB
+        created_at=datetime.utcnow(),
+        created_by=current_user,
+        doc_name=doc_name,
+        standard_name=standard_name,
+        risk_score=done_payload["risk_score"],
+        conclusion=done_payload["conclusion"],
+        actions=done_payload["actions"],
+        para_text=done_payload["para_text"],
+        highlight_terms=done_payload["highlight_terms"],
+        findings=[FindingRecord(...) for f in accumulated_findings],
+    )
+    analysis_id = await asyncio.to_thread(repo.save_analysis, record)
+    # Emit an extra SSE event so frontend receives the analysis_id
+    yield f"data: {json.dumps({'type': 'saved', 'analysis_id': analysis_id})}\n\n"
+```
+
+### B4: New API Endpoints
+
+Added to `backend/app/api/routes/compliance.py`:
+
+```
+GET    /api/v1/compliance/history
+       Query params: limit=20&offset=0
+       Response: [{id, created_at, doc_name, standard_name, risk_score, finding_count}]
+
+GET    /api/v1/compliance/history/{analysis_id}
+       Response: full AnalysisRecord including findings list
+
+DELETE /api/v1/compliance/history/{analysis_id}
+       Response: 204 No Content
+
+GET    /api/v1/compliance/history/{analysis_id}/download
+       Response: DOCX file (application/vnd.openxmlformats-officedocument.wordprocessingml.document)
+```
+
+### B5: DOCX Export
+
+**New file:** `backend/app/infrastructure/compliance/docx_export.py`
+
+Uses `python-docx` (already in requirements). Generates a structured report:
+
+- Cover: document name, standard, date, risk score badge
+- Executive summary: conclusion paragraph
+- Findings table: seq / title / status / clause_ref / description
+- Action items: numbered list
+- Footer: generated by AI Regulation Analysis System
+
+```python
+def generate_docx(record: AnalysisRecord) -> bytes:
+    """Generate a DOCX compliance report and return as bytes."""
+    doc = Document()
+    # ... build document ...
+    buf = BytesIO()
+    doc.save(buf)
+    return buf.getvalue()
+```
+
+### B6: Frontend — History Rail
+
+`CompliancePage.tsx` gains a left rail (same layout pattern as RagChat's `history-pane`):
+
+```
+┌──────────────┬─────────────────────────────────┐
+│ History      │  Main Analysis Area              │
+│ ──────────   │                                  │
+│ 2026-06-08   │  (current analysis or loaded     │
+│ doc.pdf      │   read-only historical record)   │
+│ ⚠ 72  [↓][×]│                                  │
+│ ──────────   │                                  │
+│ 2026-06-07   │                                  │
+│ csms.pdf     │                                  │
+│ ✓ 15  [↓][×]│                                  │
+└──────────────┴─────────────────────────────────┘
+```
+
+- `[↓]` triggers `GET /history/{id}/download` and saves the DOCX file
+- `[×]` shows a confirmation dialog, then calls `DELETE /history/{id}`
+- Clicking a row loads that analysis into the main area in read-only mode
+- `PageStateContext.ComplianceState` gains `analysisId: string | null` and `isReadOnly: boolean`
+
+On mount, the rail calls `GET /history?limit=20` to populate the list. The list re-fetches after delete or after a new analysis completes (triggered by the `saved` SSE event).
+
+### B — Files Changed
+
+| File | Action |
+|------|--------|
+| `backend/app/domain/compliance/ports.py` | New — `ComplianceRepository` ABC + data classes |
+| `backend/app/infrastructure/compliance/repository.py` | New — `PostgresComplianceRepository` |
+| `backend/app/infrastructure/compliance/docx_export.py` | New — `generate_docx()` |
+| `backend/app/api/routes/compliance.py` | Add history endpoints + auto-save hook |
+| `backend/app/shared/bootstrap.py` | Register `PostgresComplianceRepository` |
+| `frontend/src/pages/Compliance/CompliancePage.tsx` | Add History Rail |
+| `frontend/src/contexts/PageStateContext.tsx` | Add `analysisId`, `isReadOnly` to `ComplianceState` |
+
+---
+
+## Direction C — Deep Chat
+
+### C1: New Chat Endpoints
+
+Replace the existing `/compliance/chat/{segment_id}` (kept for backward compatibility but deprecated) with finding-scoped endpoints:
+
+```
+POST /api/v1/compliance/analyses/{analysis_id}/findings/{finding_id}/chat
+     Body: {query: string}
+     Response: SSE stream — chunk / done / error events
+
+GET  /api/v1/compliance/analyses/{analysis_id}/findings/{finding_id}/chat
+     Response: [{id, role, content, created_at}]
+
+POST /api/v1/compliance/analyses/{analysis_id}/findings/{finding_id}/suggestions
+     Response: {questions: [string, string, string]}
+```
+
+### C2: Grounded Context Construction
+
+New function in `pipeline.py`:
+
+```python
+def build_finding_context(finding: FindingRecord, analysis: AnalysisRecord) -> str:
+    """
+    Build a grounded system context string for a finding chat thread.
+    Combines finding details with analysis metadata for LLM grounding.
+    """
+    return (
+        f"Document: {analysis.doc_name}\n"
+        f"Standard: {analysis.standard_name}\n"
+        f"Finding [{finding.seq}]: {finding.title}\n"
+        f"Status: {finding.status}\n"
+        f"Clause reference: {finding.clause_ref or 'N/A'}\n"
+        f"Description: {finding.description}\n"
+        f"Overall conclusion: {analysis.conclusion}\n"
+    )
+```
+
+This string is prepended to the system prompt for every chat call — replacing the fragile `segment_context` approach.
+
+### C3: Multi-turn Context
+
+Chat handler fetches existing messages from `finding_chat_messages` via `repo.get_messages(finding_id)` and prepends them to the LLM call as `[{"role": "user"/"assistant", "content": "..."}]` message history. Max history: 10 most recent messages (5 turns) to avoid token overflow.
+
+After each LLM response, both the user message and assistant message are saved via `repo.save_message()`.
+
+### C4: Suggestion Generation
+
+New function in `pipeline.py`:
+
+```python
+SUGGESTION_PROMPTS = {
+    "non_compliant": "Generate 3 questions focused on remediation steps and timeline.",
+    "partial": "Generate 3 questions focused on identifying the compliance gap.",
+    "compliant": "Generate 3 questions focused on maintaining and evidencing compliance.",
+}
+
+def generate_suggestions(finding: FindingRecord, analysis: AnalysisRecord, llm_client) -> list[str]:
+    """
+    Generate 3 context-aware follow-up questions for a finding chat thread.
+    Returns a list of 3 question strings. Falls back to generic questions on error.
+    """
+    focus = SUGGESTION_PROMPTS.get(finding.status, SUGGESTION_PROMPTS["partial"])
+    context = build_finding_context(finding, analysis)
+    prompt = f"{context}\n\n{focus}\nReturn JSON: {{\"questions\": [\"...\", \"...\", \"...\"]}}"
+    # ... call LLM, parse JSON, return list ...
+    # Fallback on error:
+    return ["What are the specific requirements?", "What is the remediation timeline?", "Which regulation clause applies?"]
+```
+
+### C5: Frontend — Finding Chat Drawer
+
+New component: `frontend/src/pages/Compliance/FindingChatDrawer.tsx`
+
+Drawer slides in from the right (CSS: `position: fixed; right: 0; width: 420px`), reusing existing CSS variables (`--surface`, `--border`, `--accent`).
+
+Structure:
+- Header: finding title + close button
+- Suggestions section: 3 chip buttons (only shown before first user message; hidden after)
+- Message list: scrollable, same bubble style as RagChat
+- Composer: textarea + send button, same pattern as RagChat composer
+
+State managed in `PageStateContext.ComplianceState`:
+- `activeFindingId: string | null` — which finding's drawer is open
+- Drawer open/close controlled by `activeFindingId !== null`
+
+On open:
+1. `GET /analyses/{id}/findings/{fid}/chat` → restore history
+2. If history is empty: `POST /findings/{fid}/suggestions` → show chips
+
+Each finding card in `CompliancePage.tsx` gains a `💬 Chat` button that sets `activeFindingId`.
+
+### C — Files Changed
+
+| File | Action |
+|------|--------|
+| `backend/app/api/routes/compliance.py` | Add 3 new finding-chat endpoints |
+| `backend/app/application/compliance/pipeline.py` | Add `build_finding_context`, `generate_suggestions` |
+| `backend/app/infrastructure/compliance/repository.py` | Add `save_message`, `get_messages` (already in port) |
+| `frontend/src/pages/Compliance/FindingChatDrawer.tsx` | New component |
+| `frontend/src/pages/Compliance/CompliancePage.tsx` | Add Chat button to finding cards, render drawer |
+| `frontend/src/contexts/PageStateContext.tsx` | Add `activeFindingId` to `ComplianceState` |
+
+---
+
+## Implementation Order
+
+Direction A must be completed first (parallel processing changes the route handler that B's auto-save hook attaches to). B must be completed before C (C's FK references require B's tables and repository).
+
+```
+A (parallel + bug fixes + reranker stub)
+  └→ B (schema migration + history + DOCX)
+       └→ C (finding chat + suggestions)
+```
+
+---
+
+## Non-Goals
+
+- PDF export (DOCX only; users convert via Word/WPS)
+- Cross-encoder reranking (stub reserved, not implemented)
+- Scheduled/automatic crawling
+- User-level history isolation (all users share history — global visibility)
+- Prompt version management or A/B testing
+
+---
+
+## Constraints
+
+- Backend comments and docstrings: English only
+- No new top-level libraries beyond those already in `requirements.txt` (`tenacity`, `python-docx`, `psycopg2-binary` are all present)
+- `DOCUMENT_REPOSITORY_BACKEND=postgres` → `PostgresComplianceRepository`; any other value → raise `NotImplementedError` with a clear message (no mock fallback for compliance history)
+- Git commits are made by the user, never automated

docs/superpowers/specs/2026-06-08-i18n-design.md

@@ -0,0 +1,421 @@
+# Internationalisation (i18n) Design — Frontend Chinese/English Toggle
+
+**Date:** 2026-06-08
+**Scope:** UI framework strings only (nav labels, button labels, status messages, placeholders). Mock data, API-returned content, and domain regulation text are explicitly excluded.
+
+---
+
+## Goals
+
+Add a language toggle button (EN ↔ 中) in the Sidebar footer, immediately left of the existing theme-toggle button, so users can switch the UI between English and Simplified Chinese. Default language is English on every page load; preference is not persisted across sessions.
+
+---
+
+## Architecture
+
+### Approach
+
+Custom `LanguageContext` following the same pattern as the existing `ThemeContext`. No external library dependencies. Translation strings live in two TypeScript modules (`locales/en.ts` and `locales/zh.ts`) that export identical-shape objects.
+
+### Layering
+
+```
+src/
+├── contexts/
+│   └── LanguageContext.tsx    # type Lang, LanguageProvider, useLanguage()
+└── locales/
+    ├── en.ts                  # English translations (default)
+    └── zh.ts                  # Simplified Chinese translations
+```
+
+`LanguageProvider` wraps the entire app in `App.tsx` — outermost provider so every component can consume it.
+
+### Context interface
+
+```ts
+type Lang = 'en' | 'zh';
+
+interface LanguageContextValue {
+  lang: Lang;
+  t: Translations;       // typed translation object
+  toggleLang: () => void;
+}
+```
+
+`useState<Lang>('en')` — hardcoded default, no localStorage read on mount.
+
+### Translation object shape (both files export `Translations`)
+
+```ts
+export interface Translations {
+  nav: {
+    groupMain: string;
+    groupWorkbench: string;
+    groupChat: string;
+    overview: string;
+    signals: string;
+    status: string;
+    documents: string;
+    compliance: string;
+    chat: string;
+  };
+  sidebar: {
+    toggleTheme: string;
+    toggleLang: string;
+    signOut: string;
+  };
+  overview: {
+    eyebrow: string;
+    heroTitle: string;
+    heroDesc: string;
+    openDashboard: string;
+    jumpToChat: string;
+    sectionHowItWorks: string;
+    sectionScreens: string;
+    stepUpload: string; stepUploadDesc: string;
+    stepProcess: string; stepProcessDesc: string;
+    stepMonitor: string; stepMonitorDesc: string;
+    stepAnalyze: string; stepAnalyzeDesc: string;
+    stepReview: string; stepReviewDesc: string;
+    stepChat: string; stepChatDesc: string;
+    statScreens: string;
+    statFlows: string;
+    statReviewPosture: string;
+    navLiveHealth: string;
+    navRegulatoryChanges: string;
+    navUploadDocs: string;
+    navComplianceWorkspace: string;
+    navChatCited: string;
+    navKPIs: string;
+  };
+  signals: {
+    topbarTitle: string;
+    topbarSub: string;
+    searchPlaceholder: string;
+    refreshBtn: string;
+    crawlingBtn: string;
+    statTotal: string;
+    statHigh: string;
+    statMedium: string;
+    statLast90: string;
+    badgeFinal: string;
+    badgeDraft: string;
+    badgeUrgent: string;
+    badgePublished: string;
+    emptySelectSignal: string;
+    runAnalysis: string;
+    stopBtn: string;
+    sourceLink: string;
+    tabOverview: string;
+    tabObligations: string;
+    tabImpact: string;
+    tabChanges: string;
+    cardScopeHeader: string;
+    cardObligationsHeader: string;
+    obligationsEmpty: string;
+    colObligationDesc: string;
+    colSubject: string;
+    colType: string;
+    colDeadline: string;
+    deadlinePending: string;
+    cardAffectedDocs: string;
+    noAffectedDocs: string;
+    cardAIImpact: string;
+    footerText: string;
+    statusConnecting: string;
+    statusNoStream: string;
+    statusCrawling: string;
+    statusProcessing: string;
+    statusComplete: string;
+    statusUpdateComplete: string;
+    statusError: string;
+    statusConnFailed: string;
+  };
+  status: {
+    topbarTitle: string;
+    searchPlaceholder: string;
+    exportBtn: string;
+    refreshBtn: string;
+    newUploadBtn: string;
+    statTotal: string;
+    statIndexed: string;
+    statFailed: string;
+    statChunks: string;
+    statCoverage: string;
+    cardHealth: string;
+    badgeOnline: string;
+    badgeError: string;
+    badgeDegraded: string;
+    badgeUnknown: string;
+    healthEndpointError: string;
+    serviceEnabled: string;
+    serviceDisabled: string;
+    serviceNotLoaded: string;
+    cardConfig: string;
+    labelLLMProvider: string;
+    labelLLMModel: string;
+    labelEmbeddingModel: string;
+    labelEmbeddingDim: string;
+    labelMilvusCollection: string;
+    labelParserBackend: string;
+    labelChunkBackend: string;
+    labelParserFailureMode: string;
+    configLoadError: string;
+    cardBreakdown: string;
+    breakdownIndexed: string;
+    breakdownProcessing: string;
+    breakdownFailed: string;
+    cardRuntime: string;
+    labelActiveSessions: string;
+    labelSessionCapacity: string;
+    labelReranker: string;
+    labelBM25: string;
+    statusActive: string;
+    statusUnavailable: string;
+    footerAllOk: string;
+    footerDegraded: string;
+    footerChecking: string;
+  };
+  docs: {
+    topbarTitle: string;
+    searchPlaceholder: string;
+    refreshBtn: string;
+    uploadBtn: string;
+    confirmDeleteTitle: string;
+    cancelBtn: string;
+    deleteBtn: string;
+    filterAll: string;
+    filterReady: string;
+    filterProcessing: string;
+    filterFailed: string;
+    filterPending: string;
+    filterAllTypes: string;
+    selectedCount: string;   // '{n} document(s) selected' — use {n} placeholder
+    deleteSelected: string;
+    colName: string;
+    colStatus: string;
+    colUploaded: string;
+    colChunks: string;
+    colSize: string;
+    colType: string;
+    colActions: string;
+    loading: string;
+    emptyNoDocuments: string;
+    emptyNoMatch: string;
+    footerCount: string;     // '{n} of {m} document(s)'
+    titleDownload: string;
+    titleRetry: string;
+    titleDelete: string;
+    confirmSingle: string;   // '{name}' placeholder
+    confirmBatch: string;    // '{n}' placeholder
+  };
+  compliance: {
+    topbarTitle: string;
+    searchPlaceholder: string;
+    clearBtn: string;
+    exportBtn: string;
+    exportJSON: string;
+    exportText: string;
+    newAnalysisBtn: string;
+    statusAnalyzing: string;
+    statusComplete: string;
+    statusError: string;
+    emptyTitle: string;
+    emptyDesc: string;
+    colRetrieved: string;    // 'Retrieved Regulations {count}'
+    retrievingMsg: string;
+    defaultRegulation: string;
+    matchSuffix: string;
+    colParagraph: string;
+    extractingMsg: string;
+    noTextExtracted: string;
+    stagesHeader: string;
+    stageExtraction: string;
+    stageClauseSplit: string;
+    stageRetrieval: string;
+    stageSynthesis: string;
+    colFindings: string;     // 'Findings {count}'
+    gapInProgress: string;
+    askAIBtn: string;
+    chatBtn: string;
+    conclusionHeader: string;
+    riskScoreTooltip: string;
+    statusCovered: string;
+    statusGap: string;
+    statusCritical: string;
+    statusInfo: string;
+    sourceTypePasted: string;
+    sourceTypeIndexed: string;
+    sourceTypeUploaded: string;
+    chatSidebarHeader: string;
+    chatThinking: string;
+    quickQ1: string;
+    quickQ2: string;
+    quickQ3: string;
+    chatPlaceholder: string;
+    sendBtn: string;
+    analysisFailed: string;
+    exportReportHeader: string;
+    exportSectionParagraph: string;
+    exportSectionFindings: string;
+    exportSectionConclusion: string;
+    exportSectionActions: string;
+    historyHeader: string;
+    downloadReport: string;
+    historyEmpty: string;
+    historyDeleteConfirm: string;
+    drawerClose: string;
+    drawerChatEmpty: string;
+    drawerSuggestionsHeader: string;
+  };
+  ragchat: {
+    topbarTitle: string;
+    exportBtn: string;
+    quickPromptsHeader: string;
+    inputPlaceholder: string;
+    citationsHeader: string;    // 'Sources {count}'
+    citationsEmpty: string;
+    jumpToSource: string;       // 'Jump to source [N]'
+    apiError: string;
+    quickPrompt1: string;
+    quickPrompt2: string;
+    quickPrompt3: string;
+    quickPrompt4: string;
+  };
+}
+```
+
+---
+
+## Language Toggle Button
+
+Location: `Sidebar.tsx` footer `<div style={{ display: 'flex', gap: 4 }}>`.
+
+Inserted **left of** the existing theme button:
+
+```tsx
+<button className="theme-btn" onClick={toggleLang} title={t.sidebar.toggleLang}>
+  {lang === 'en' ? 'EN' : '中'}
+</button>
+```
+
+- Reuses existing `theme-btn` CSS class — no new styles needed.
+- Displays two-character label: `EN` or `中`.
+- `title` attribute (tooltip) translates with the rest of the UI.
+
+---
+
+## Translation Files (complete values)
+
+### `locales/en.ts` (English — default)
+
+Key values (representative; full file contains all keys above):
+
+```ts
+nav: { groupMain: 'Main', groupWorkbench: 'Workbench', groupChat: 'Chat',
+       overview: 'Overview', signals: 'Regulatory Signals', status: 'System Status',
+       documents: 'Documents', compliance: 'Compliance Analysis', chat: 'Regulation Q&A' },
+sidebar: { toggleTheme: 'Toggle theme', toggleLang: 'Switch language', signOut: 'Sign out' },
+signals: { refreshBtn: 'Refresh Sources', crawlingBtn: 'Crawling...', ... },
+docs: { uploadBtn: 'Upload document', deleteBtn: 'Delete', cancelBtn: 'Cancel', ... },
+compliance: { newAnalysisBtn: 'New analysis', analyzeBtn: 'Analyze', sendBtn: 'Send', ... },
+ragchat: { exportBtn: 'Export chat', inputPlaceholder: 'Ask about your regulations…', ... },
+```
+
+### `locales/zh.ts` (Simplified Chinese)
+
+Key values:
+
+```ts
+nav: { groupMain: '主菜单', groupWorkbench: '工作台', groupChat: '对话',
+       overview: '概览', signals: '法规信号', status: '系统状态',
+       documents: '文档管理', compliance: '合规分析', chat: '法规问答' },
+sidebar: { toggleTheme: '切换主题', toggleLang: '切换语言', signOut: '退出' },
+signals: { refreshBtn: '刷新数据源', crawlingBtn: '抓取中...', ... },
+docs: { uploadBtn: '上传文档', deleteBtn: '删除', cancelBtn: '取消', ... },
+compliance: { newAnalysisBtn: '新建分析', analyzeBtn: '开始分析', sendBtn: '发送', ... },
+ragchat: { exportBtn: '导出对话', inputPlaceholder: '请输入关于法规的问题…', ... },
+```
+
+---
+
+## App.tsx Provider Wrapping
+
+```tsx
+// Before
+<ThemeProvider>
+  <AuthProvider>
+    <PageStateProvider>
+      <AppRouter />
+    </PageStateProvider>
+  </AuthProvider>
+</ThemeProvider>
+
+// After
+<LanguageProvider>
+  <ThemeProvider>
+    <AuthProvider>
+      <PageStateProvider>
+        <AppRouter />
+      </PageStateProvider>
+    </AuthProvider>
+  </ThemeProvider>
+</LanguageProvider>
+```
+
+`LanguageProvider` is outermost so it is available to all components including the theme toggle itself.
+
+---
+
+## Usage in Components
+
+```tsx
+import { useLanguage } from '../../contexts/LanguageContext';
+
+function MyComponent() {
+  const { t } = useLanguage();
+  return <button>{t.docs.uploadBtn}</button>;
+}
+```
+
+No wrapping needed — `t` is always the correct object for the current language.
+
+---
+
+## Files Changed
+
+| File | Action |
+|------|--------|
+| `src/contexts/LanguageContext.tsx` | New — `LanguageProvider`, `useLanguage()`, `Lang` type |
+| `src/locales/en.ts` | New — complete English `Translations` object |
+| `src/locales/zh.ts` | New — complete Chinese `Translations` object |
+| `src/App.tsx` | Add `<LanguageProvider>` wrapper |
+| `src/components/layout/Sidebar.tsx` | Add language toggle button; replace nav group titles and labels with `t.nav.*` |
+| `src/pages/Overview/OverviewPage.tsx` | Replace all UI strings with `t.overview.*` |
+| `src/pages/Perception/PerceptionPage.tsx` | Replace all UI strings with `t.signals.*` |
+| `src/pages/Status/StatusPage.tsx` | Replace all UI strings with `t.status.*` |
+| `src/pages/Docs/DocsPage.tsx` | Replace all UI strings with `t.docs.*` |
+| `src/pages/Compliance/CompliancePage.tsx` | Replace all UI strings with `t.compliance.*` |
+| `src/pages/RagChat/RagChatPage.tsx` | Replace all UI strings with `t.ragchat.*` |
+| `src/pages/Compliance/HistoryRail.tsx` | Replace UI strings with `t.compliance.*` |
+| `src/pages/Compliance/FindingChatDrawer.tsx` | Replace UI strings with `t.compliance.*` |
+
+---
+
+## Non-Goals
+
+- Persistence across sessions (no localStorage for language preference)
+- More than two languages
+- RTL layout support
+- Pluralisation helpers (simple string substitution with `{n}` placeholders is sufficient — callers replace via `t.docs.selectedCount.replace('{n}', String(count))`)
+- Translation of API-returned content, mock data, regulation names, or document file names
+- Date/number formatting localisation
+
+---
+
+## Constraints
+
+- Zero new npm dependencies
+- Follow existing `ThemeContext` pattern exactly
+- Backend comments/docstrings: English only (no backend changes in this feature)
+- Git commits made by the user, never automated

docs/superpowers/specs/2026-07-02-status-llm-model-usage-design.md

@@ -0,0 +1,273 @@
+# System Status — Connected AI Models & Token Usage Design
+
+**Date:** 2026-07-02
+**Scope:** Extend the existing System Status module with a new "AI Models" panel showing which LLM/Embedding/Reranker models are configured, their connection status, and cumulative token consumption.
+**Relationship to existing roadmap:** This is a lightweight, self-contained first slice of the "P0-A observability" priority already identified in `AI_Agent_优化分析报告_2026-06-18.md` (full Langfuse tracing + Ragas evaluation remains a separate, larger future effort — see Out of Scope).
+
+---
+
+## Goals
+
+1. Show all "connected" AI models in one place: main answer-generation LLM, the dedicated HyDE query-expansion LLM, the embedding model, and the reranker (even when disabled).
+2. Show connection status per model, derived passively from real traffic (no extra cost), plus an optional manual "test connection" action for an on-demand active check.
+3. Show cumulative token consumption per model since process start (in-memory; resets on restart — no new database table).
+4. Guarantee accuracy by instrumenting the single shared LLM client factory, so intermediate Agentic RAG steps, HyDE, regulation-perception analysis, compliance review, and document summarization are all captured — not just the final chat answer.
+
+## Non-Goals (see "Out of Scope" at the end)
+
+- Persistent/historical token usage (DB-backed, survives restart) — deferred.
+- Cost/spend estimation in currency — deferred (no reliable public pricing for the internal gateway).
+- Per-session or per-user token breakdown — deferred.
+- Accurate token counting for **streaming** chat responses — deferred (see Known Limitations).
+- Full distributed tracing / LLM-as-judge faithfulness scoring (Langfuse + Ragas, `P0-A` in the existing roadmap) — this feature is a lightweight precursor, not a replacement.
+
+---
+
+## Architecture Overview
+
+### Layering (must not be violated — per `docs/architecture/backend-project-architecture.md`)
+
+```
+api/routes/status.py        →  thin handlers, reads tracker + settings, no business logic
+shared/model_usage_tracker.py →  cross-cutting support (same tier as shared/bootstrap.py)
+services/llm/llm_factory.py  →  wraps clients with TrackedLLMClient at creation time
+infrastructure/embedding/…   →  direct instrumentation (single implementation)
+infrastructure/vectorstore/cross_encoder_reranker.py → direct instrumentation (single implementation)
+```
+
+No new business orchestration is added to `services/*` or `workflows/*`. The tracker is passive, cross-cutting infrastructure support, consistent with how `shared/bootstrap.py` and `shared/errors.py` are described in the backend README as "composition root 与横切支撑".
+
+### Data Model
+
+`ModelUsageTracker` keys its internal state by **`f"{provider}:{model}"`**, not by business role. This is more robust than keying by role: if a future Agentic sub-step uses a different provider/model, it is still captured under its own key rather than being silently dropped because no role mapping exists for it. "Role" (`main_llm` / `hyde_llm` / `embedding` / `reranker`) is purely a **presentation-layer label**, resolved at read time in the `/status/models` handler by looking up the current `settings` (`llm_provider`/`llm_model`, `hyde_llm_provider`/`hyde_llm_model` with its existing "empty means reuse main" fallback, `embedding_model`, `reranker_model`).
+
+```python
+# backend/app/shared/model_usage_tracker.py
+@dataclass
+class ModelUsageEntry:
+    """Represent accumulated usage/connection state for one provider+model pair."""
+    provider: str
+    model: str
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    call_count_ok: int = 0
+    call_count_error: int = 0
+    last_called_at: datetime | None = None
+    last_latency_ms: int | None = None
+    last_error: str | None = None
+
+    @property
+    def status(self) -> str:
+        """Derive display status from call history: never_called | ok | error.
+
+        Note: this only reflects the tracker's own history. The route handler
+        (not this class) overrides the value to "disabled" for the reranker role
+        when settings.reranker_enabled is False — config always wins over any
+        stale historical data, e.g. if the reranker was enabled in the past and
+        later turned off in .env.
+        """
+        if self.last_called_at is None:
+            return "never_called"
+        return "error" if self.last_error else "ok"
+
+
+class ModelUsageTracker:
+    """Thread-safe in-memory registry of per-model call/usage stats.
+
+    Never raises: a bug here must not break a real user-facing LLM call.
+    """
+
+    def __init__(self) -> None:
+        self._entries: dict[str, ModelUsageEntry] = {}
+        self._lock = threading.Lock()
+
+    def record(
+        self,
+        *,
+        provider: str,
+        model: str,
+        success: bool,
+        usage: dict | None = None,
+        latency_ms: int | None = None,
+        error: str | None = None,
+    ) -> None:
+        """Record the outcome of one call to provider/model. Safe to call from any thread."""
+        ...
+
+    def snapshot(self) -> dict[str, ModelUsageEntry]:
+        """Return a shallow copy of all tracked entries, safe to iterate without the lock."""
+        ...
+
+
+@lru_cache
+def get_model_usage_tracker() -> ModelUsageTracker:
+    """Return the process-wide singleton tracker (mirrors get_settings()/get_llm_factory() pattern)."""
+    return ModelUsageTracker()
+```
+
+All `record()` bodies are wrapped in `try/except Exception: logger.warning(...)` internally — tracking failures are logged and swallowed, never propagated.
+
+### LLM Instrumentation — `TrackedLLMClient` Wrapper
+
+Every LLM call in the codebase goes through `get_llm_client()` in `backend/app/services/llm/llm_factory.py` (confirmed call sites: `agentic_service.py`, `hyde_expander.py`, `perception/services.py`, `perception/llm_pipeline.py`, `api/routes/compliance.py` ×2, `infrastructure/llm/openai_compatible_answer_generator.py` ×2, `services/llm/document_summarizer.py`). `LLMFactory.create()` wraps the concrete client (`DeepSeekClient`/`QwenClient`/`QwenVLClient`) in `TrackedLLMClient` before caching it, so every current and future call site is covered automatically with **one** change point.
+
+```python
+# backend/app/services/llm/tracked_client.py
+class TrackedLLMClient:
+    """Transparent decorator that records usage/latency into ModelUsageTracker.
+
+    Deliberately does NOT subclass BaseLLMClient: that ABC declares abstract
+    methods (_init_client, get_available_models) which would have to be stubbed
+    out, defeating the point of __getattr__ delegation and instantiation would
+    fail with "Can't instantiate abstract class" before __getattr__ ever runs.
+    Plain composition + __getattr__ forwarding is sufficient since callers only
+    ever use duck-typed access (.chat(), .stream_chat(), .get_available_models(), .close()).
+    """
+
+    def __init__(self, inner: BaseLLMClient, tracker: ModelUsageTracker) -> None:
+        self._inner = inner
+        self._tracker = tracker
+
+    def chat(self, messages, max_tokens=None, temperature=None, tools=None, **kwargs) -> LLMResponse:
+        """Delegate to the wrapped client's chat(), then record usage/latency/outcome."""
+        start = time.time()
+        response = self._inner.chat(messages, max_tokens, temperature, tools, **kwargs)
+        self._tracker.record(
+            provider=self._inner.config.provider.value,
+            model=response.model or self._inner.config.model,
+            success=response.is_success,
+            usage=response.usage,
+            latency_ms=int((time.time() - start) * 1000),
+            error=response.error,
+        )
+        return response
+
+    def stream_chat(self, messages, *args, **kwargs):
+        """Delegate to stream_chat(); records call success/latency only (no token usage — see Known Limitations)."""
+        ...
+
+    def __getattr__(self, name):
+        """Forward any other attribute/method access to the wrapped client."""
+        return getattr(self._inner, name)
+```
+
+### Embedding & Reranker Instrumentation
+
+Both have a single concrete implementation today, so they are instrumented directly (no wrapper needed):
+
+- `OpenAICompatibleEmbeddingProvider._request()` — additionally reads `data.get("usage", {})` from the OpenAI-compatible embeddings response and calls `get_model_usage_tracker().record(provider="embedding", model=self.model, ...)`.
+- `OpenAICompatibleReranker._call_reranker()` / `rerank()` — records call success/failure + latency only. TEI/Cohere-style rerank responses do not include token usage, so `total_tokens` for the reranker role will always show as unavailable (`—`), which is factually correct, not a bug to fix later.
+
+---
+
+## API
+
+Both endpoints are added to the existing `backend/app/api/routes/status.py` (no new router file), returning plain dicts — matching the existing convention in this file and in `perception.py` (no Pydantic response models for these "reporting" endpoints).
+
+### `GET /status/models`
+
+Passive read: no outbound network calls, just tracker snapshot + settings resolution.
+
+```json
+{
+  "models": [
+    {
+      "role": "main_llm",
+      "role_label": "主问答 LLM",
+      "provider": "deepseek",
+      "model": "deepseek-v4-flash",
+      "enabled": true,
+      "status": "ok",
+      "total_tokens": 12345,
+      "call_count_ok": 42,
+      "call_count_error": 1,
+      "last_called_at": "2026-07-02T10:00:00+08:00",
+      "last_latency_ms": 350,
+      "last_error": null,
+      "shares_usage_with": null
+    }
+  ]
+}
+```
+
+Always returns exactly 4 entries in a fixed order: `main_llm`, `hyde_llm`, `embedding`, `reranker` — even if a model has never been called (`status: "never_called"`, all counters zero) or is disabled (`reranker.enabled: false` when `settings.reranker_enabled` is `False`). When `hyde_llm_provider`/`hyde_llm_model` are empty (config falls back to the main LLM), `hyde_llm.shares_usage_with` is set to `"main_llm"` and both rows naturally show identical numbers because they resolve to the same tracker key.
+
+`status` precedence (resolved by the route handler, not by `ModelUsageEntry` itself): if the role is disabled by config (`reranker` only, when `reranker_enabled=False`) the handler always reports `"disabled"`, regardless of any historical call data the tracker may still hold from when it was previously enabled. Otherwise it passes through the tracker's own `ok` / `error` / `never_called`.
+
+### `POST /status/models/ping`
+
+Active check, run only for `enabled` models, in parallel (`asyncio.gather` over `run_in_threadpool`, since the underlying clients are synchronous `httpx`):
+
+- `main_llm` / `hyde_llm`: `chat([{"role": "user", "content": "ping"}], max_tokens=1)`
+- `embedding`: `embed_query("ping")`
+- `reranker`: `rerank("ping", [one placeholder chunk], top_k=1)` — only when `reranker_enabled=True`
+
+Each ping is wrapped independently so one timeout doesn't block the others. Ping calls go through the same instrumented code paths, so they naturally (and honestly) add a small amount to the token counters — this is not hidden or special-cased. Response shape is identical to `GET /status/models`, reflecting the fresh post-ping state.
+
+---
+
+## Frontend
+
+### New Card: "AI Models" in `frontend/src/pages/Status/StatusPage.tsx`
+
+Placed in `panel-left`, directly after the existing "System Health" card (conceptually related — both are live connectivity views).
+
+- Card header: title + a "Test Connection" button (`POST /status/models/ping`, disabled + spinner while in flight).
+- Body: 4 rows reusing the existing `StatusIcon` + `service-row` styling, extended with a right-aligned token count column (monospace, `toLocaleString()`, matching `ConfigRow`'s number formatting) and a small last-called relative-time hint.
+- `never_called` and `disabled` map to the existing muted/info badge styles already used elsewhere on this page (no new visual language needed).
+- **Cleanup**: the existing "Runtime" card (`panel-right`) currently shows a single Reranker enabled/model line — this is removed from that card since the new "AI Models" card now shows it with richer detail (status + tokens), avoiding duplicate information on the page.
+
+### Data & Types
+
+- `frontend/src/api/status.ts`: add `getModelUsage()` (`GET /status/models`) and `pingModelConnections()` (`POST /status/models/ping`).
+- `frontend/src/api/index.ts`: add `ModelUsageEntry` / `ModelUsageResponse` types alongside the existing `SystemStats`/`SystemConfig`/`SystemHealth`.
+- `StatusPage.tsx`: extend the existing `Promise.allSettled([...])` fetch-on-mount/refresh with a 4th parallel call for model usage, following the same "partial failure doesn't crash the page" pattern already used for stats/health/config.
+- i18n: add new keys under the existing `t.status.*` namespace in both `frontend/src/locales/en.ts` and `zh.ts` (card title, role labels, status labels, button label, "shares usage with main LLM" note).
+- Desktop-first, no responsive/mobile work, per `AGENTS.md`.
+
+### Files Changed
+
+| File | Action |
+|---|---|
+| `backend/app/shared/model_usage_tracker.py` | New — `ModelUsageEntry`, `ModelUsageTracker`, `get_model_usage_tracker()` |
+| `backend/app/services/llm/tracked_client.py` | New — `TrackedLLMClient` wrapper |
+| `backend/app/services/llm/llm_factory.py` | Wrap client with `TrackedLLMClient` in `LLMFactory.create()` before caching |
+| `backend/app/infrastructure/embedding/openai_compatible_embedding_provider.py` | Capture `usage` from embeddings response, record to tracker |
+| `backend/app/infrastructure/vectorstore/cross_encoder_reranker.py` | Record call success/failure + latency to tracker |
+| `backend/app/api/routes/status.py` | Add `GET /status/models`, `POST /status/models/ping` |
+| `frontend/src/api/status.ts` | Add `getModelUsage()`, `pingModelConnections()` |
+| `frontend/src/api/index.ts` | Add `ModelUsageEntry`/`ModelUsageResponse` types |
+| `frontend/src/pages/Status/StatusPage.tsx` | Add "AI Models" card; remove duplicate reranker line from "Runtime" card |
+| `frontend/src/locales/en.ts`, `zh.ts` | Add new `status.*` keys |
+
+---
+
+## Error Handling
+
+- Tracker `record()` never raises — internal `try/except Exception: logger.warning(...)`, so a bug in observability code cannot break a real RAG answer, HyDE expansion, or compliance review call.
+- `GET /status/models` mirrors the existing per-service try/except pattern already used in `/status/health` — a failure resolving one role's config falls back to a safe "unknown" entry rather than a 500 for the whole endpoint.
+- `POST /status/models/ping`: each per-model ping is wrapped individually (`asyncio.gather(..., return_exceptions=True)` or equivalent per-task try/except); one model's timeout/error does not prevent the other three from completing and being reported.
+- Frontend: ping failures surface as inline text on that row (existing `service-row` already supports a muted "detail" slot); page-level fetch failures already degrade gracefully via the existing `Promise.allSettled` fallback pattern.
+
+## Testing
+
+Backend (existing `pytest` setup, `backend/tests/`):
+- `backend/tests/shared/test_model_usage_tracker.py` (new) — accumulation across multiple `record()` calls, status transitions (`never_called` → `ok` → `error`), basic concurrent-write safety.
+- Test for `TrackedLLMClient` — verifies it delegates `chat()` faithfully (return value unchanged) while recording usage, and that a wrapped-client exception still propagates correctly.
+- `backend/tests/api/test_status_models_routes.py` (new) — `GET /status/models` returns exactly 4 roles with correct defaults when nothing has been called yet (including `reranker.enabled == settings.reranker_enabled`); `POST /status/models/ping` with mocked clients (no real network calls in tests), verifying partial-failure handling.
+
+Frontend: no test framework exists in this repo today (`frontend/package.json` has no test script, no vitest/jest config) — per project convention, this feature does not introduce one. Verification is `npm --prefix frontend run lint` + `npm --prefix frontend run build`, plus manual visual check of the new card.
+
+## Known Limitations
+
+- **Streaming token gap**: `stream_chat()` implementations in `DeepSeekClient`/`QwenClient` currently only yield content deltas and do not parse a trailing `usage` chunk (would require requesting `stream_options: {include_usage: true}` from the gateway and handling the final SSE chunk). This means token counts from streamed chat (the main RAG chat UI's default interaction mode) are **not** captured in this iteration — only call count/latency/success are recorded for streaming calls. Non-streaming calls (HyDE, agentic intent/plan/grounding steps, compliance review, document summarization, perception analysis) are fully captured. This gap is called out explicitly rather than silently under-counting without explanation, and is a natural follow-up.
+- In-memory only: counters reset on every backend restart/redeploy; acceptable per explicit product decision in this design (no new DB table).
+
+## Out of Scope (deferred to future iterations)
+
+- Persistent historical token usage (Postgres-backed, time-windowed charts).
+- Cost/spend estimation in currency.
+- Per-session/per-user attribution.
+- Parsing streaming `usage` chunks for exact streaming token counts.
+- Full Langfuse distributed tracing + Ragas/LLM-as-Judge faithfulness scoring (existing roadmap `P0-A` remains the larger follow-on effort; this feature's tracker data model is intentionally simple and would need to coexist with, not replace, a future Langfuse integration).

docs_dump.txt

@@ -0,0 +1,108 @@
+===== PPTX =====
+--- Slide 1 ---: AI + 法律法规 | 合规智能中枢 | 面向车企与工厂的 AI 驱动合规解决方案 | 2026年4月  |  EMS & EHS Compliance Intelligence Hub | AI Compliance
+Intelligence Hub | Internal  |  AI 合规智能中枢  |  2026.04
+--- Slide 2 ---: 背景与挑战 | 车企和工厂面临的合规困境 | 法规来源复杂 | 国标GB · MIIT · UN-ECE
+IATF 16949 · ISO 45001
+多轨并行，难以统管 | 更新频率高 | 新能源 · 数据安全 · 碳排放
+PIPL · NEV积分 · CCER
+政策持续迭代 | 跨语言需求 | 中英文法规混存
+跨国工厂多语言
+合规场景并存 | 文档高度分散 | 分散于 Confluence
+SharePoint · ERP · PLM
+无法联通查询 | 隐患识别被动 | EHS 安全依赖人工
+隐患发现滞后
+缺乏预防性机制 | 覆盖核心法规域 | 🚗 车辆安全  GB 7258 · GB 18384 · UN-ECE R155/156 | 🔒 数据安全  PIPL · DSL · GB/T 35273 | 🏭 工厂EHS  GB 6441 · AQ/T系列 · ISO 45001 | ♻️ 碳排放    NEV积分 · CCER · 欧盟碳边境税 | ✅ 质量管理  IATF 16949 · GB/T 19001 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 2 / 13
+--- Slide 3 ---: 产品定位与整体架构 | AI 驱动的全链路合规智能平台 | AI 合规智能中枢 | 📚  知识库构建 | 内外部法规 · 历史案例
+统一知识图谱 · 自动更新 | 💬  智能问答 | 混合检索 · 语义+关键词
+中英双语 · 引文溯源 | 📄  合规审查 | PDF/Word上传
+自动比对法规 · 风险标注 | 🔌  API集成 | 对接PLM · ERP · OA · MES | 🎯  个性化推荐 | 角色画像 · 上下文感知 | 📢  定制推送 | Email · Teams
+飞书 · 钉钉
+法规变更
+实时通知 | 🦺  EHS 隐患识别 & 管理体系审计（C-SG专项） | 事故报告 NLP | SIF潜力识别 | 四维根因分析 | ISO 45001 要素扫描 | 自动生成审计报告 | 趋势分析仪表板 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 3 / 13
+--- Slide 4 ---: 功能一：合规知识库构建与动态更新 | 统一接入内外部法规，构建可检索的结构化知识库 | 📥  数据来源 | 内部文档 | Confluence · SharePoint
+飞书 · 历史合规报告 · 审计记录 | ↓ | 外部法规 | 国标全文库 · 工标网
+MIIT政策 · UN-ECE · EUR-Lex | ↓ | 历史案例 | 处罚案例库 · 整改记录
+行业事故通报 | ⚙️  处理流程 | 1 | ① 文档解析 | 版面感知OCR，扫描件 · PDF表格 · 多栏 · Word/Excel | ↓ | 2 | ② 智能分块 | 章节级 / 条款级双粒度切割，保留语义完整性 | ↓ | 3 | ③ 向量化存储 | 多语言嵌入（中英双语），向量库 + 关键词索引双轨 | ↓ | 4 | ④ 知识图谱 | 法规实体 → 条款 → 义务 → 适用范围关系图谱 | ↓ | 5 | ⑤ 自动更新 | 定时监控法规变更，触发增量重索引 + 版本管理 | ✨  核心价值 | 数据不出厂 | 私有化本地部署
+满足PIPL/DSL数据主权 | 权限分级管理 | 研发/生产/采购/法务
+差异化访问控制 | 实时保鲜 | 法规修订自动触发重索引
+确保知识时效性 | 多格式支持 | 扫描件 · PDF · Word
+Excel · 标准文件全覆盖 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 4 / 13
+--- Slide 5 ---: 功能二：混合检索智能问答引擎 | 语义检索 + 关键词检索 + 知识图谱，生成可溯源的合规决策建议 | 用户提问 | 中 / 英 / 混合
+自然语言输入 | ▶ | 意图理解 | 识别法规实体
+适用场景 · 地域 | ▶ | 混合检索 | BM25关键词
++ 语义向量
+本地+网络双路 | ▶ | 重排序 | Cross-Encoder
+精排召回结果 | ▶ | 生成回答 | 引文锚定输出
+置信度评分
+页码溯源 | 典型问答场景 | 法规解读 | "我们的纯电SUV需满足哪些GB强制认证要求？" | 政策查询 | "2025年NEV积分核算方式有哪些最新变化？" | 合规判断 | "供应商A的REACH声明是否满足我司采购合规要求？" | 多跳推理 | "ISO 45001变更管理要求，对应哪些内部流程需更新？" | 对比分析 | "GB 18384与欧盟ECE R100在电池安全上有哪些差异？" | 📎 引文溯源 | 答案标注原文出处
+页码精确定位 | 🌐 多语言支持 | 中英混合检索
+无需切换语言 | ⚖️ 决策辅助 | 结合内部制度
+输出综合建议 | 🔄 图谱增强 | 关联上下游条款
+多跳推理支持 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 5 / 13
+--- Slide 6 ---: 功能三：智能文档合规审查 | 上传 PDF/Word，自动比对法规库，标注风险并给出整改建议 | ⚙️  审查流程 | 1 | ① 文件上传 | PDF · Word · Excel · 扫描件，支持批量 | ↓ | 2 | ② 文档解析 | 版面感知OCR，段落/条款级分块 | ↓ | 3 | ③ 法规域匹配 | 根据文档类型+内容自动识别适用法规域 | ↓ | 4 | ④ 合规比对 | 条款级语义对比，缺项检测 · 风险评分 | ↓ | 5 | ⑤ 报告输出 | 非合规位置标注，整改建议 · 风险等级 | 📋  报告输出内容 | 📍 | 非合规位置标注 | 页码 + 段落高亮，一键跳转原文 | ⚠️ | 风险等级分级 | 红（高危）/ 橙（中）/ 黄（低）三级 | 📖 | 法规条款引用 | 精确关联对应法规原文条款编号 | 🔧 | 整改建议 | 基于历史合规案例，给出可执行方案 | 📂  适用文档类型 | 供应商合规声明 | REACH/RoHS · 碳足迹申报 | 新产品EHS评估 | GB安全标准覆盖完整性核查 | 工厂安全作业规程 | AQ/T符合性 · 许可条款 | 劳动合同/协议 | 劳动法 · 工时 · 竞业条款 | 数据处理协议 | PIPL/GDPR 数据主体权利 | 供应链碳申报 | CCER/CBAM 核算方法验证 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 6 / 13
+--- Slide 7 ---: EHS 隐患识别 & 管理体系审计（C-SG专项） | AI驱动的主动安全预防：从被动响应到预测性干预 | 📥  数据输入 | 📝  事故/事件报告文本 · 巡检记录 · 安全观察卡 | 📊  设备运行数据 · 工伤统计 · 隐患整改台账 | 📷  现场照片（目标检测）· 视频（行为分析，可选） | 🤖  AI隐患识别引擎 | NLP文本分析 | 从叙述性文本中提取隐患实体
+触发因素 · 伤害类型 · 位置信息 | SIF风险评分 | 高严重性事件潜力预测
+优先处置最高风险隐患 | 四维根因分析 | 人因/设备/管理/环境
+系统性根因挖掘 | 法规自动关联 | 与GB 6441/AQ系列/ISO 45001
+自动映射对应条款 | 📋  体系审计功能 | ✓  ISO 45001要素覆盖度扫描（PDCA完整性） | ✓  历史案例相似度匹配与经验复用 | ✓  整改优先级排序（风险×紧迫×可行性） | ✓  审计报告自动生成（条款级评分） | ⚠️  典型隐患场景 | ▸  高处坠落 | AQ/T 3049 | ▸  有限空间 | AQ 3028 | ▸  化学品管理 | GB 13690 | ▸  设备点检 | IATF §8.5 | ▸  应急演练 | ISO 45001 §8.2 | 📤  输出成果 | 隐患清单 | 位置 · 类型
+风险等级
+法规依据
+整改建议 | 体系审计
+报告 | 条款级符合
+性评分
+整改优先级 | 趋势分析
+仪表板 | 隐患热图
+月度趋势
+部门对比 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 7 / 13
+--- Slide 8 ---: 系统集成 · 个性化推荐 · 定制推送 | 合规能力 API 化，主动触达用户，融入业务流程 | 🔌  合规审查 API 化 | POST | /compliance/check | 大文本分片合规检查 | POST | /compliance/upload | PDF/Word文件上传审查 | GET | /compliance/query | 法规知识库问答 | POST | /compliance/subscribe | 法规变更Webhook订阅 | 🔗  企业系统集成 | PLM | 新产品立项/BOM变更 | →  自动触发法规适用性检查 | ERP | 供应商准入/合同签署 | →  供应商自动合规评分 | OA | 合同/协议提交审批 | →  高风险自动抄送法务 | MES | 生产工艺变更 | →  触发EHS合规影响评估 | 🎯  个性化推荐 | 👤  角色画像：EHS · 法务 · 采购 · 研发 | 💡  上下文感知：对话主题 → 关联法规推荐 | 🔔  到期提醒：认证到期 · 法规更新预警 | 📈  行为学习：历史查询 → 智能问题推荐 | 📢  定制化法规推送 | 📧  Email | HTML富文本，含变更对比 | 💬  Teams | 企业Bot，实时推送 | 📱  飞书/钉钉 | 企业机器人，移动端 | 🔔  站内消息 | 系统内通知中心 | ⚙️  推送规则引擎 | ▸  订阅维度： | 按法规域 / 业务场景 / 地域灵活订阅 | ▸  优先级： | 🔴 强制  🟠 推荐  🔵 参考  三级分类 | ▸  免打扰： | 工作时间推送 · 摘要合并 · 频率上限 | ▸  内容生成： | LLM自动生成变更摘要 + 影响分析 + 行动项 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 8 / 13
+--- Slide 9 ---: 关键挑战与应对策略 | 确保合规建议的准确性、时效性与数据安全 | LLM幻觉风险 | 问题 | 合规建议失真
+可能导致法律责任 | 应对 | 引文锚定 + 输出验证
+高风险强制人工审核 | 数据主权 | 问题 | 敏感文件不能
+上传公有云 | 应对 | 全链路私有化部署
+数据不出厂 | 法规时效性 | 问题 | 知识库滞后
+导致错误建议 | 应对 | 自动更新机制
+时间戳标注 + 提醒 | 跨语言质量 | 问题 | 中英混合场景
+检索精度下降 | 应对 | 多语言嵌入模型
+语言标签过滤策略 | 大文件性能 | 问题 | GB标准数百页
+处理超时风险 | 应对 | 流式处理 + 分层索引
+异步队列实时进度 | 权限管控 | 问题 | 不同角色需
+不同密级访问 | 应对 | RBAC权限体系
+知识库分区 + 审计日志 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 9 / 13
+--- Slide 10 ---: 分阶段实施路线 | 从核心知识库到全链路合规智能，稳步落地 | 第一阶段 | 0 - 3 个月 | 知识库 + 基础问答 | 1 | 部署合规知识库平台，接入内部文档 | 2 | 接入 GB 标准 · AQ 系列 · IATF 16949 | 3 | 上线中英双语混合检索问答界面 | 4 | 完成权限分级与数据安全配置 | 第二阶段 | 3 - 6 个月 | 文档审查 + API 集成 | 1 | 构建文档合规审查引擎（PDF/Word） | 2 | 完成合规 API 封装，对接PLM/ERP/OA | 3 | 上线法规变更监控与推送服务 | 4 | 接入 Teams / 飞书 Bot 推送渠道 | 第三阶段 | 6 - 12 个月 | EHS隐患识别 + 个性化 | 1 | 构建 EHS 隐患识别与体系审计模块 | 2 | 引入知识图谱，支持多跳推理 | 3 | 上线个性化推荐引擎（角色画像） | 4 | 全链路合规智能体系正式上线 | ▶ | ▶ | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 10 / 13
+--- Slide 11 ---: 三类合规闭环场景 | 从「发现问题」到「关闭归档」的完整业务闭环 | 📡  法规变更合规闭环 | 1 | ①  | 法规监控 |  ( | 扩展功能 | ) | 国内外法规数据库实时监控
+自动检测条款变更与新法发布 | ↓ | 2 | ② 知识库更新 | 变更内容自动解析入库
+版本管理 + 影响范围标注 | ↓ | 3 | ③ 精准推送 | 按角色/业务域推送变更摘要
+Email · Teams · 飞书多渠道 | ↓ | 4 | ④ 差距分析 | AI对比新旧法规差异
+识别企业现行制度缺口 | ↓ | 5 | ⑤  | 整改执行 |  ( | 扩展功能 | ) | 生成整改任务清单
+关联责任人与完成时限 | ↓ | 6 | ⑥  | 闭环归档 |  ( | 扩展功能 | ) | 整改完成后验收确认 | 
+ | 合规证据归档留存 | 归档的文档放在 | share point  | 同步更新知识库 | ↺  持续监控 → 知识库保鲜 → 合规常态化 | 📄  文档审查合规闭环 | 1 | ① 文件上传 | PDF · Word · Excel · 扫描件
+支持批量上传与拖拽 | ↓ | 2 | ② AI解析 | 版面感知OCR，条款级分块
+自动识别文档类型与法规域 | ↓ | 3 | ③ 合规比对 | 条款级语义对比法规库
+缺项检测 · 风险评分 | ↓ | 4 | ④ 风险标注 | 页码+段落精确定位
+红/橙/黄三级风险可视化 | ↓ | 5 | ⑤ 整改建议 | AI生成具体整改方案
+关联历史合规最佳实践 | ↓ | 6 | ⑥ 复审归档 | 整改后重新提交复核 | 
+ | 通过后合规证明自动归档 | 归档的文档放在 | share point  | 同步更新知识库 | ↺  上传即审查 → 整改即跟踪 → 归档即留证 | 🦺   | EHS安全管理闭环 |  ( | 扩展功能 | ) | 1 | ① 隐患发现 | NLP解析巡检/事故报告文本
+图像识别 · 传感器数据接入 | ↓ | 2 | ② 风险评级 | SIF潜力评分 + 四维根因分析
+高/中/低三级优先级排序 | ↓ | 3 | ③ 任务派发 | 自动生成整改工单
+关联责任人 · 截止时间 · 法规依据 | ↓ | 4 | ④ 过程跟踪 | 整改进度实时可视化
+超期自动升级提醒 | ↓ | 5 | ⑤ 验收关闭 | 整改完成后现场复查
+AI辅助验收确认 | ↓ | 6 | ⑥ 体系优化 | 根因数据回流知识库
+优化隐患模型与预防策略 | ↺  发现即评级 → 整改即跟踪 → 关闭即优化 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 11 / 13
+--- Slide 12 ---: 组织架构与 |  RBAC  | 权限体系 | 按角色分级授权，确保数据安全与合规责任落实到人 | 🏢  组织架构层级 | 集团 / 总部 | 合规委员会 · 法务部 · EHS总监 | ▼ | 事业部 / 工厂 | EHS部门 · 质量部 · 采购部 · 研发部 | ▼ | 业务线 / 车间 | 安全员 · 质检员 · 工艺工程师 | ▼ | 外部协作方 | 供应商 · 第三方审计 · 监管机构 | 🔐  角色权限矩阵（RBAC） | 知识库
+查询 | 文档
+审查 | EHS
+审计 | 法规
+推送 | 系统
+管理 | 合规管理员 | ● | ● | ● | ★ | ★ | 法务专员 | ● | ● | ◑ | ◑ | ○ | EHS工程师 | ● | ◑ | ● | ◑ | ○ | 采购专员 | ◑ | ● | ○ | ◑ | ○ | 研发工程师 | ◑ | ◑ | ○ | ◑ | ○ | 工厂安全员 | ◑ | ○ | ● | ◑ | ○ | 供应商（外部） | ○ | ◑ | ○ | ○ | ○ | ● 完全权限 | ◑ 只读/有限 | ○ 无权限 | ★ 管理权限 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 12 / 13
+--- Slide 13 ---: 总结与下一步行动 | 构建面向车企与工厂的 AI 驱动全链路合规智能体系 | 📚 | 知识统一 | 内外部法规 + 历史案例
+一库统管，自动更新 | 💬 | 智能问答 | 混合检索 + 知识图谱
+可溯源的决策建议 | 📄 | 合规审查 | AI自动比对标注
+风险等级 + 整改建议 | 🦺 | EHS防控 | SIF预测 + 体系审计
+被动响应到主动预防 | 🔌 | 无缝集成 | API化能力嵌入
+PLM · ERP · OA · MES | 建议下一步行动 | 01 | 需求确认 | 与EHS · 法务 · 采购
+核心用户开展访谈 | 02 | POC验证 | 选取2-3个场景快速
+搭建原型验证可行性 | 03 | 数据准备 | 梳理内部文档，确认
+数据分级与权限策略 | 04 | 架构评审 | 与IT安全团队确认
+私有化部署与集成规范 | AI 合规智能中枢  |  面向车企与工厂  |  2026.04 | 13 / 13
+
+===== DOCX =====

frontend/src/App.tsx

@@ -1,12 +1,18 @@
 import './styles/globals.css';
-import { ThemeProvider } from './contexts';
+import { ThemeProvider, AuthProvider, PageStateProvider, LanguageProvider } from './contexts';
 import { AppRouter } from './router/AppRouter';
 
 function App() {
   return (
+    <LanguageProvider>
       <ThemeProvider>
+        <AuthProvider>
+          <PageStateProvider>
             <AppRouter />
+          </PageStateProvider>
+        </AuthProvider>
       </ThemeProvider>
+    </LanguageProvider>
   );
 }