update for mcp

feat: surface MCP server status in System Status page
Add per-tool in-memory call counters to the MCP module and a GET /api/v1/status/mcp endpoint that joins them with the live tool registry and endpoint config, then render it as a new card on the System Status page with a one-click client-config copy button. - app/mcp/stats.py: lock-guarded MCPStatsTracker (the mcp SDK runs sync tool bodies via anyio.to_thread.run_sync, so this is genuinely multi-threaded, unlike the async REST routes) - app/mcp/server.py: instrument search_regulations, add get_mcp_status() - app/config/settings.py: optional MCP_PUBLIC_URL override, required because the Vite proxy and reverse proxies rewrite the Host header - StatusPage.tsx: MCP Server card, joins the existing parallel fetch Counters are process-local by design; token usage is already persisted by ModelUsageTracker since MCP calls route through ask(). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-08-06 11:08:46 +08:00 · 2026-08-03 11:37:22 +08:00 · 2026-08-03 10:56:56 +08:00 · 2026-07-29 17:11:54 +08:00 · 2026-07-29 13:00:52 +08:00 · 2026-07-29 11:03:28 +08:00
167 changed files with 29320 additions and 1156 deletions
@@ -48,8 +48,23 @@ 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
-DOCUMENT_REPOSITORY_BACKEND=json
+# 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=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 =====
+# 单次 HTTP 请求超时（秒），含正文抓取（fetch_full_text）。
+PERCEPTION_CRAWL_TIMEOUT_SECONDS=120
+# 每个数据源单次爬取的最大条目数。
+PERCEPTION_MAX_EVENTS_PER_SOURCE=100
+# 变更判定的次要闸门：段落改动字符占比达到该阈值才送 LLM 分类。
+# 数字变化（如 30米->20米）或情态词变化（应当/宜/不得等）无视此阈值，始终判定为显著变更。
+PERCEPTION_DIFF_MIN_CHANGE_RATIO=0.02
+# 定时全量爬取的执行间隔（秒），默认 21600 = 6 小时。
+# 仅当 Celery Beat 进程在运行时才生效（./dev.sh start beat），Beat 未启动则完全不会自动爬取。
+PERCEPTION_CRAWL_INTERVAL_SECONDS=21600

 # ===== API配置 =====
 API_HOST=0.0.0.0
@@ -92,3 +107,43 @@ 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=qwen3.6-flash
+
+
+# ===== MCP 服务配置 =====
+# MCP SDK 在传输层绑定回环地址时会自动启用 DNS 重绑定防护：Host 头不在下表内的
+# 请求一律返回 HTTP 421，且发生在进入工具逻辑之前。部署在 6.86.80.9 必须显式列出
+# 该地址，否则所有远程 MCP 客户端（Claude Desktop / IDE 等）100% 连不上。
+# 语法：`:*` 后缀匹配任意端口；填 `*` 表示彻底关闭该防护（不推荐）。
+MCP_ALLOWED_HOSTS=6.86.80.9:*,127.0.0.1:*,localhost:*,[::1]:*
+
+# 系统状态页 MCP 卡片展示、以及"复制接入配置"按钮写入的对外访问地址。
+# 留空则由后端从请求 Host 头推导；但前端经 Vite 代理（changeOrigin: true）转发后
+# Host 会被改写成 API_HOST:API_PORT，推导结果是 0.0.0.0/127.0.0.1，客户端无法使用，
+# 因此远程部署必须显式指定。结尾的斜杠不能省略。
+MCP_PUBLIC_URL=http://6.86.80.9:8000/mcp/
@@ -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
@@ -50,7 +50,26 @@ 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 =====
+# 单次 HTTP 请求超时（秒），含正文抓取（fetch_full_text）。
+PERCEPTION_CRAWL_TIMEOUT_SECONDS=120
+# 每个数据源单次爬取的最大条目数。
+PERCEPTION_MAX_EVENTS_PER_SOURCE=100
+# 变更判定的次要闸门：段落改动字符占比达到该阈值才送 LLM 分类。
+# 数字变化（如 30米->20米）或情态词变化（应当/宜/不得等）无视此阈值，始终判定为显著变更。
+PERCEPTION_DIFF_MIN_CHANGE_RATIO=0.02
+# 定时全量爬取的执行间隔（秒），默认 21600 = 6 小时。
+# 仅当 Celery Beat 进程在运行时才生效（./dev.sh start beat），Beat 未启动则完全不会自动爬取。
+PERCEPTION_CRAWL_INTERVAL_SECONDS=21600

 # ===== 阿里云文档解析 =====
 ALIBABA_ACCESS_KEY_ID=your_aliyun_access_key_id
@@ -96,11 +115,15 @@ 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）=====
-# 设置 RERANKER_ENABLED=true 并配置 RERANKER_BASE_URL 以启用精排
-RERANKER_ENABLED=false
-RERANKER_BASE_URL=
+# ── Reranker (Cross-Encoder) ──────────────────────────────────────────────────
+# Set RERANKER_ENABLED=true and point to a TEI or Cohere-compatible rerank API.
+# Recommended model: BAAI/bge-reranker-v2.5-gemma2-lightweight (lighter) or
+#                    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 +131,61 @@ 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=qwen3.6-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
+
+# ===== MCP (Model Context Protocol) =====
+# MCP 端点（/mcp/）的 Host 头白名单，逗号分隔。MCP SDK 默认开启 DNS rebinding
+# 防护，任何不在此列表中的 Host 都会被直接返回 HTTP 421，请求根本到不了鉴权和
+# 工具逻辑。因此**远程部署必须把真实访问地址写进来**，否则所有外部 MCP 客户端
+# （Claude Desktop / IDE 等）100% 连不上。
+# 语法：`:*` 后缀表示匹配任意端口；填 `*` 表示彻底关闭该防护（不推荐）。
+# 例如部署在 6.86.80.9:8000 时：
+# MCP_ALLOWED_HOSTS=6.86.80.9:*,127.0.0.1:*,localhost:*
+MCP_ALLOWED_HOSTS=127.0.0.1:*,localhost:*,[::1]:*
+
+# 系统状态页展示、以及"复制接入配置"按钮所使用的 MCP 外部访问地址。
+# 留空则由后端从请求的 Host 头推导；当前端经 Vite 代理（changeOrigin: true）
+# 或反向代理改写了 Host 时，推导结果会是 127.0.0.1，此时必须显式指定。
+# MCP_PUBLIC_URL=http://6.86.80.9:8000/mcp/
+MCP_PUBLIC_URL=
+
@@ -62,3 +62,6 @@ logs/

 # codex
 .agents
+
+# personal local records (never commit)
+local/
@@ -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>
@@ -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}
@@ -0,0 +1 @@
+{"reason":"idle timeout","timestamp":1780894411095}
@@ -0,0 +1 @@
+1055
@@ -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 |
-| 多轮对话 | `/api/v1/agent/chat` | POST | 会话对话 |
+| 单次问答 | `/api/v1/agent/ask` | POST | 标准单轮问答 |
+| 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 |
-| 可用模型 | `/api/v1/agent/models` | GET | LLM模型列表 |
+| 会话历史 | `/api/v1/agent/session/{id}/history` | GET | 获取历史记录 |
+| 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）

 ---

@@ -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.
+"""
@@ -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
@@ -1,6 +1,6 @@
 """FastAPI application entrypoint."""

-from contextlib import asynccontextmanager
+from contextlib import AsyncExitStack, asynccontextmanager

 from fastapi import FastAPI, Request
 from fastapi.encoders import jsonable_encoder
@@ -8,10 +8,12 @@ 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
 from app.config.settings import settings
+from app.mcp.server import build_mcp_asgi_app
 from app.shared.bootstrap import cleanup_runtime_dependencies, preload_runtime_dependencies
 from app.shared.errors import VectorStoreSchemaError
 # Keep module behavior explicit so the backend flow stays easy to audit.
@@ -19,10 +21,23 @@ from app.shared.errors import VectorStoreSchemaError

 setup_logging(level="INFO" if not settings.debug else "DEBUG")

+# Built once at module scope so both lifespan() and app.mount() below reference
+# the same instance — mounting a second, separately-built instance would start
+# a second, unrelated MCP session manager.
+mcp_app = build_mcp_asgi_app()
+

@asynccontextmanager
 async def lifespan(app: FastAPI):
    """Application lifecycle hooks."""
+    # FastMCP-style servers own a session manager that only starts via its own
+    # lifespan context. app.mount() does NOT propagate nested ASGI lifespans
+    # automatically (confirmed Starlette/ASGI limitation) — without this,
+    # every search_regulations call would fail because the MCP session
+    # manager was never started.
+    async with AsyncExitStack() as stack:
+        await stack.enter_async_context(mcp_app.router.lifespan_context(mcp_app))
+
        logger.info(f"启动 {settings.app_name} v{settings.app_version}")
        logger.info(f"调试模式: {settings.debug}")
        logger.info("预加载LLM客户端...")
@@ -46,15 +61,25 @@ 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")
+app.mount("/mcp", mcp_app)


@app.exception_handler(VectorStoreSchemaError)
@@ -0,0 +1 @@
+"""HTTP middleware for cross-cutting concerns: audit logging."""
@@ -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
@@ -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):
@@ -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",
@@ -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",
+        },
+    )
@@ -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,
+    }
@@ -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(
-                    retrieve_for_clause, clause, retrieval_service, 5, domains or None
-                )
+            done_count = 0
+            # 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"},
+    )
@@ -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:
@@ -4,10 +4,17 @@ 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_notification_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 +72,100 @@ 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"),
+    }
+
+
+@router.get("/notifications")
+async def list_notifications(
+    limit: int = Query(default=20, ge=1, le=100),
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return the newest in-app notifications plus this user's unread count.
+
+    Every logged-in user sees the same broadcast feed — there is no per-role
+    or per-topic subscription. "read" per item and the aggregate unread_count
+    both reflect only the calling user's own read receipts.
+    """
+    store = get_notification_store()
+    items = store.list_for_user(current_user.user_id, limit=limit)
+    return {"items": items, "unread_count": store.unread_count(current_user.user_id)}
+
+
+@router.post("/notifications/read")
+async def mark_notifications_read(current_user: UserClaims = Depends(get_current_user)):
+    """Mark every currently-unread notification read for the calling user."""
+    marked = get_notification_store().mark_all_read(current_user.user_id)
+    return {"marked": marked}
@@ -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):
-    """Stream RAG Q&A using the real agent service."""
+async def rag_chat(
+    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]:
@@ -1,18 +1,25 @@
 """Define API routes for status."""

+import asyncio
 import time
 from typing import Any

-from fastapi import APIRouter
+from fastapi import APIRouter, Request

 from app.config.settings import settings
+from app.domain.retrieval import RetrievedChunk
+from app.mcp.server import get_mcp_status
+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 +30,16 @@ _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 +128,170 @@ 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]}
+
+
+@router.get("/mcp")
+async def get_mcp_server_status(request: Request):
+    """Return MCP endpoint config, advertised tools, and per-tool call counters.
+
+    This route is a thin HTTP adapter: everything MCP-specific is assembled by
+    app.mcp.server.get_mcp_status(). The only thing decided here is the public
+    URL, because only the HTTP layer knows how the client reached us.
+    """
+    # request.base_url already carries scheme/host/port and a trailing slash;
+    # strip it before appending so the result is ".../mcp/", not ".../mcp//".
+    public_url = settings.mcp_public_url or f"{str(request.base_url).rstrip('/')}/mcp/"
+    return await get_mcp_status(public_url)
@@ -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",
+]
@@ -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()
+
@@ -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. qwen3.6-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
@@ -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)
+
@@ -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)
-    doc_chunks = [c for c in chunks if c.doc_id == doc_id]
+    # Use doc_name as a broad query, filter strictly by doc_id so we only get
+    # 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]
-    return "\n\n".join(c.text for c in doc_chunks[:15])
+        # Fallback: use top results even without doc_id match (e.g., legacy store)
+        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")
-        )[:4000]
+            b.get("text", "") for b in parsed.semantic_blocks if b.get("text")
+        )
    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 "
-        "semantically complete compliance clauses. Each clause should be an independent "
-        "compliance requirement or technical statement.\n"
+            "You are a compliance analysis expert. Split the following text into "
+            "3-4 semantically complete compliance clauses. Each clause must be an "
+            "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:
-                    return clauses[:8]
+                    all_clauses.extend(clauses[:4])
            except (ValueError, TypeError):
-            logger.warning("Clause split JSON parse failed, using fallback")
-    sentences = re.split(r"[.?!;\n]+", text)
-    return [s.strip() for s in sentences if len(s.strip()) > 20][:6]
+                logger.warning("Clause split JSON parse failed for window, using sentence fallback")
+                sentences = re.split(r"[.?!;\n]+", window)
+                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:
-        return None
+    """Check whether a business clause complies with the retrieved regulations.
+
+    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 []
@@ -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",
            )
-
-            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,
+            # Delegate parse → embed → index to the shared processing method.
+            # This same method is invoked by the Celery worker for async processing.
+            return self._process_document(
                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,
+                file_name=file_name,
+                final_doc_name=final_doc_name,
+                content=content,
                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()
-            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,
+                generate_summary=generate_summary,
                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]:
-        """Handle download for the Document Query Service instance."""
+    def download(self, doc_id: str) -> tuple["Document", bytes]:
+        """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)
@@ -0,0 +1,255 @@
+"""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.config.settings import settings
+from app.domain.documents import ParsedDocument
+from app.infrastructure.perception.base_event_store import BaseEventStore
+from app.infrastructure.perception.base_notification_store import BaseNotificationStore
+from app.infrastructure.perception.crawlers.base import BaseCrawler, RawEvent
+from app.infrastructure.perception.llm_pipeline import LlmPipeline
+from app.infrastructure.parser.local_chunk_builder import LocalRegulationChunkBuilder
+
+
+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 _is_significant(changed_sections: list[dict]) -> bool:
+    """Report whether any changed section is worth notifying every user about.
+
+    changed_sections legitimately includes cosmetic edits — the differ
+    (subproject 1) still reports a fixed typo or a dropped trailing period as
+    a change, it just doesn't send those to the LLM. Broadcasting a
+    notification for every cosmetic edit would train people to ignore it, so
+    this reuses the same significance test the differ's own LLM gate applies:
+    a numeric or deontic change, or a whole paragraph added or removed.
+    """
+    return any(
+        section.get("numeric_changed")
+        or section.get("deontic_changed")
+        or section.get("change_type") in ("added", "removed")
+        for section in changed_sections
+    )
+
+
+def _index_in_knowledge_base(event: dict, *, embedding_provider: Any, vector_index: Any) -> None:
+    """Chunk, embed, and upsert a regulation's text into the shared knowledge base.
+
+    Always uses the local markdown chunker, never get_chunk_builder() — that
+    bootstrap function resolves to AliyunVectorChunkBuilder when
+    settings.chunk_backend == "aliyun" (the deployed value), which consumes
+    Aliyun DocMind's structured parse output. Crawled text has no such parse
+    output; it is already plain text (trafilatura, subproject 1), which is
+    exactly what LocalRegulationChunkBuilder chunks directly.
+
+    delete_by_document runs unconditionally before upsert — a no-op for a
+    brand-new event, and the only way to keep a changed regulation from
+    leaving its superseded text retrievable alongside the new version.
+    """
+    vector_index.delete_by_document(event["id"])
+
+    parsed = ParsedDocument(
+        doc_id=event["id"],
+        doc_name=event.get("title", ""),
+        structure_nodes=[],
+        semantic_blocks=[],
+        vector_chunks=[],
+        parser_name="perception_crawl",
+        raw_text=event.get("raw_text") or "",
+    )
+    builder = LocalRegulationChunkBuilder(
+        chunk_size=settings.chunk_size, chunk_overlap=settings.chunk_overlap,
+    )
+    chunks = builder.build(
+        parsed_document=parsed,
+        # regulation_type/version fill the same slots a manually uploaded
+        # document's form fields would, so the two intake paths are
+        # indistinguishable to retrieval and compliance analysis.
+        regulation_type=event.get("category", ""),
+        version=event.get("standard_code", ""),
+    )
+    if not chunks:
+        return
+
+    vectors = embedding_provider.embed_texts([c.embedding_text for c in chunks])
+    vector_index.upsert(chunks, vectors)
+
+
+def _raw_to_dict(raw: RawEvent, event_id: str, content_hash: str, raw_text: 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,
+        # Persisted so the next crawl has a baseline to diff against. Without
+        # this the change detector has nothing to compare and every update
+        # looks like a first sighting.
+        "raw_text": raw_text,
+        "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,
+        notification_store: BaseNotificationStore,
+        embedding_provider: Any,
+        vector_index: Any,
+    ) -> None:
+        self._crawlers = crawlers
+        self._store = event_store
+        self._pipeline = llm_pipeline
+        self._retrieval = retrieval_service
+        self._notifications = notification_store
+        self._embedding_provider = embedding_provider
+        self._vector_index = vector_index
+
+    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=settings.perception_max_events_per_source)
+            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)
+                # List pages carry only a code and a title, which is not enough
+                # to detect a change in the regulation itself. Fetch the body,
+                # degrading to whatever the list page gave us if that fails.
+                body_text = crawler.fetch_full_text(raw.full_text_url) or raw.raw_text or raw.title
+                new_hash = _content_hash(body_text)
+                existing = self._store.get(eid)
+
+                if existing and existing.get("content_hash") == new_hash:
+                    continue
+
+                is_update = existing is not None
+                old_body = existing.get("raw_text") or "" if is_update else ""
+                previous_hash = existing.get("content_hash") if is_update else None
+
+                event_dict = _raw_to_dict(raw, eid, new_hash, body_text)
+                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)
+
+                # Events stored before raw_text was persisted have no baseline,
+                # so they are treated as a first sighting and establish one now.
+                if is_update and old_body and body_text:
+                    try:
+                        diff = self._pipeline.compute_diff(old_body, body_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)
+
+                should_index = not is_update or _is_significant(event_dict.get("changed_sections") or [])
+
+                try:
+                    if not is_update:
+                        self._notifications.create(
+                            event_id=eid, kind="new", title=raw.title,
+                            impact_level=event_dict.get("impact_level"), summary=None,
+                        )
+                    elif should_index:  # significant change, already computed above
+                        self._notifications.create(
+                            event_id=eid, kind="changed", title=raw.title,
+                            impact_level=event_dict.get("impact_level"),
+                            summary=event_dict.get("change_summary"),
+                        )
+                except Exception as exc:
+                    logger.warning("Notification create failed id={} err={}", eid, exc)
+
+                if should_index:
+                    try:
+                        _index_in_knowledge_base(
+                            event_dict,
+                            embedding_provider=self._embedding_provider,
+                            vector_index=self._vector_index,
+                        )
+                    except Exception as exc:
+                        logger.warning("Knowledge base indexing failed id={} err={}", eid, exc)
+
+                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},
+        }
@@ -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
@@ -82,6 +82,34 @@ 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_min_change_ratio: float = Field(
+        default=0.02,
+        description=(
+            "Fraction of characters that must differ before an otherwise "
+            "unremarkable paragraph edit is worth an LLM classification call. "
+            "Numeric and deontic changes bypass this gate entirely."
+        ),
+    )
+    perception_crawl_interval_seconds: int = Field(
+        default=21600,
+        description=(
+            "How often Celery Beat runs the scheduled crawl-all-sources task, "
+            "in seconds. Default 21600 = 6 hours. Only takes effect when a "
+            "Beat process is running (./dev.sh start beat)."
+        ),
+    )

    # 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")
@@ -101,7 +129,7 @@ class Settings(BaseSettings):
    # Keep configuration setup explicit so runtime behavior is easy to reason about.
    qwen_api_key: str = Field(default="", description="Qwen API密钥")
    qwen_base_url: str = Field(default="http://6.86.80.4:30080/v1", description="Qwen API地址")
-    qwen_model: str = Field(default="qwen3.5-flash", description="Qwen文本模型")
+    qwen_model: str = Field(default="qwen3.6-flash", description="Qwen文本模型")
    qwen_vl_model: str = Field(default="qwen3-vl-plus", description="Qwen视觉模型")

    # Keep configuration setup explicit so runtime behavior is easy to reason about.
@@ -109,6 +137,7 @@ 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 +145,42 @@ 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 +189,52 @@ 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.",
+    )
+
+    # ── MCP ───────────────────────────────────────────────────────────────────
+    # The MCP SDK enables DNS-rebinding protection whenever the transport is
+    # bound to a loopback host, which rejects any Host header not in this list
+    # with HTTP 421. Deployments reachable by a real hostname/IP must list it
+    # here or every remote MCP client is refused before the handler runs.
+    mcp_allowed_hosts: str = Field(
+        default="127.0.0.1:*,localhost:*,[::1]:*",
+        description=(
+            "Comma-separated Host header values accepted by the MCP endpoint. "
+            "A ':*' suffix matches any port. Set to '*' to disable DNS-rebinding "
+            "protection entirely (not recommended)."
+        ),
+    )
+
+    # Optional override for the URL shown on the System Status page and copied
+    # into client configs. Needed because request.base_url reflects the Host
+    # header, which the Vite dev proxy (changeOrigin: true) and reverse proxies
+    # that do not forward the original Host both rewrite.
+    mcp_public_url: str = Field(
+        default="",
+        description=(
+            "Externally reachable MCP endpoint URL, e.g. http://6.86.80.9:8000/mcp/. "
+            "Leave empty to derive it from the incoming request."
+        ),
+    )

@lru_cache
 def get_settings() -> Settings:
@@ -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"]
@@ -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
@@ -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).
+        """
@@ -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.
+"""
@@ -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)
@@ -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)
@@ -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()
@@ -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=[],
+        )
@@ -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]]:
@@ -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."""
-        system_prompt = PROMPT_TEMPLATES.get(prompt_template or "compliance_qa", PROMPT_TEMPLATES["default"])
+        """Build the message list to send to the LLM.
+
+        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(
-            {
-                "role": "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。",
-            }
+
+        # Craft the user turn differently when a document is attached
+        if context_text and context_text.strip():
+            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}
@@ -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."""
@@ -0,0 +1,47 @@
+"""Abstract base class for in-app regulatory-signal notifications.
+
+A notification is created once per triggering event (a brand-new regulation,
+or a significant change to an existing one) and broadcast to every logged-in
+user. There is no per-user subscription targeting — see the design doc for why.
+Per-user "read" state is tracked separately from the notification itself, so
+one notification row serves every user rather than being fanned out on create.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+
+class BaseNotificationStore(ABC):
+    """Port interface for perception notification persistence."""
+
+    @abstractmethod
+    def create(
+        self,
+        *,
+        event_id: str,
+        kind: str,
+        title: str,
+        impact_level: str | None,
+        summary: str | None,
+    ) -> None:
+        """Record a new notification. kind is 'new' or 'changed'."""
+
+    @abstractmethod
+    def list_for_user(self, user_id: str, limit: int = 20) -> list[dict]:
+        """Return the most recent notifications, newest first.
+
+        Each item includes a "read" boolean reflecting whether `user_id` has
+        marked it read.
+        """
+
+    @abstractmethod
+    def unread_count(self, user_id: str) -> int:
+        """Return how many notifications `user_id` has not yet read."""
+
+    @abstractmethod
+    def mark_all_read(self, user_id: str) -> int:
+        """Mark every currently-unread notification read for `user_id`.
+
+        Returns the number of notifications newly marked.
+        """
@@ -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]
@@ -0,0 +1,75 @@
+"""Shared contracts for regulatory source crawlers."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+
+import httpx
+import trafilatura
+from loguru import logger
+
+from app.config.settings import settings
+
+
+@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)
+    # Whatever text the list page yields. CrawlService upgrades this by calling
+    # fetch_full_text(full_text_url); this value is the fallback when that
+    # fails. Used for change hashing and for the version diff.
+    raw_text: str = ""
+
+
+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."""
+
+    def fetch_full_text(self, url: str) -> str:
+        """Download a regulation detail page and extract its body text.
+
+        Change detection is only as good as the text it compares, and list
+        pages carry nothing but a standard code and a title. This default
+        implementation serves all current sources; a source that needs PDF
+        extraction or authentication overrides this one method.
+
+        Returns an empty string on any failure rather than raising, so one
+        unreachable page cannot abort a whole crawl run. The caller decides how
+        to degrade.
+        """
+        if not url:
+            return ""
+        try:
+            response = httpx.get(
+                url,
+                timeout=settings.perception_crawl_timeout_seconds,
+                follow_redirects=True,
+            )
+            response.raise_for_status()
+        except Exception as exc:  # noqa: BLE001 - any transport error degrades the same way
+            logger.warning("Full-text fetch failed url={} err={}", url, exc)
+            return ""
+
+        # trafilatura scores 0.92 F1 on government pages against 0.78 for
+        # readability-lxml, and handles CJK content; include_tables matters
+        # because regulatory limits are frequently tabulated.
+        extracted = trafilatura.extract(response.text, include_tables=True)
+        if not extracted:
+            logger.warning("Full-text extraction returned nothing url={}", url)
+            return ""
+        return extracted.strip()
@@ -0,0 +1,88 @@
+"""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.config.settings import settings
+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=settings.perception_crawl_timeout_seconds,
+                    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]
+
+
@@ -0,0 +1,122 @@
+"""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.config.settings import settings
+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=settings.perception_crawl_timeout_seconds,
+                    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]
@@ -0,0 +1,98 @@
+"""Crawlers for the 国标委 (SAMR) standard information platform."""
+
+from __future__ import annotations
+
+import httpx
+from loguru import logger
+
+from app.config.settings import settings
+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=settings.perception_crawl_timeout_seconds,
+        )
+        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]
@@ -0,0 +1,245 @@
+"""LLM-driven pipeline for regulatory event enrichment."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from loguru import logger
+
+from app.config.settings import settings
+from app.infrastructure.perception.regulation_differ import (
+    ParagraphChange,
+    RegulationDiffer,
+)
+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. You are given the OLD and NEW version of "
+    "one regulation paragraph, with the exact edits marked <DEL>removed</DEL> and "
+    "<INS>added</INS>. Classify the legal effect of the change. "
+    "Return JSON only: {\"change_type\": \"tightened|relaxed|numeric|clarified|scope\", "
+    "\"legal_effect\": \"one sentence on what this means for compliance\"}"
+)
+
+
+def _marked_diff(change: ParagraphChange) -> str:
+    """Render a paragraph change with the exact edits marked for the model.
+
+    The model is shown where the edit is rather than being asked to find it,
+    and is never asked to reproduce the changed text — the differ already
+    computed those spans exactly, so there is nothing for the model to
+    hallucinate.
+    """
+    marked = "".join(
+        text if op == 0 else (f"<DEL>{text}</DEL>" if op < 0 else f"<INS>{text}</INS>")
+        for op, text in change.diff_spans
+    )
+    return (
+        f"OLD: {change.old_text[:500]}\n"
+        f"NEW: {change.new_text[:500]}\n"
+        f"MARKED: {marked[:800]}"
+    )
+
+
+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,
+        )
+        # Change detection is deterministic; the differ needs no model and no
+        # network, so the pipeline no longer constructs an embedding provider.
+        self._differ = RegulationDiffer()
+
+    # ------------------------------------------------------------------
+    # 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: Deterministic diff with gated LLM classification
+    # ------------------------------------------------------------------
+
+    def compute_diff(self, old_text: str, new_text: str) -> dict:
+        """Compare old and new regulation text; return changed sections and summary.
+
+        Detection is deterministic — see regulation_differ for why embedding
+        similarity was removed. The LLM is called only for paragraphs the
+        differ marked significant, and only to explain the legal effect of a
+        change that has already been located exactly.
+        """
+        changes = self._differ.diff(old_text, new_text)
+        if not changes:
+            return {
+                "changed_sections": [],
+                "change_summary": "No substantive changes detected between versions.",
+            }
+
+        changed_sections = [self._describe(change) for change in changes]
+
+        types = sorted({section["change_type"] for section in changed_sections})
+        gated = sum(1 for change in changes if change.needs_llm)
+        change_summary = (
+            f"{len(changed_sections)} paragraph(s) changed ({', '.join(types)}); "
+            f"{gated} significant. "
+            + (changed_sections[0].get("summary") or "")
+        ).strip()
+
+        return {"changed_sections": changed_sections, "change_summary": change_summary}
+
+    def _describe(self, change: ParagraphChange) -> dict:
+        """Turn one detected change into the API payload, classifying if warranted."""
+        section = {
+            "old_text": change.old_text[:300],
+            "new_text": change.new_text[:300],
+            "change_type": change.change_type,
+            "change_ratio": round(change.change_ratio, 3),
+            "numeric_changed": change.numeric_changed,
+            "deontic_changed": change.deontic_changed,
+            "summary": "",
+        }
+
+        if not change.needs_llm:
+            return section
+
+        classification = _llm_json(
+            self._client,
+            [
+                {"role": "system", "content": _DIFF_SYSTEM},
+                {"role": "user", "content": _marked_diff(change)},
+            ],
+        )
+        if isinstance(classification, dict):
+            section["change_type"] = classification.get("change_type") or change.change_type
+            section["summary"] = classification.get("legal_effect") or ""
+        # A failed or malformed model response must not discard a change that
+        # deterministic analysis already proved real; the section keeps its
+        # spans, flags, and alignment-derived type with an empty summary.
+
+        if change.numeric_changed:
+            # Models routinely label a changed threshold as "clarified". The
+            # deterministic pass already knows a number moved, so it wins.
+            section["change_type"] = "numeric"
+
+        return section
@@ -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
-_EVENT_INDEX: dict[str, dict] = {e["id"]: e for e in MOCK_EVENTS}
-
-
-class MockEventStore:
+class MockEventStore(BaseEventStore):
    """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", ""))
@@ -0,0 +1,66 @@
+"""In-memory notification store used when Postgres is not configured.
+
+Mirrors MockEventStore's role for BaseEventStore: keeps the feature usable in
+local dev and in tests without a live database, and matches
+DOCUMENT_REPOSITORY_BACKEND's existing Mock/Postgres split.
+"""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+from app.infrastructure.perception.base_notification_store import BaseNotificationStore
+
+
+class MockNotificationStore(BaseNotificationStore):
+    """Dict-backed notification store. Data does not survive a process restart."""
+
+    def __init__(self) -> None:
+        """Start with an empty feed and no read receipts."""
+        self._notifications: list[dict] = []
+        self._next_id = 1
+        # (notification_id, user_id) pairs — presence means read.
+        self._reads: set[tuple[int, str]] = set()
+
+    def create(
+        self,
+        *,
+        event_id: str,
+        kind: str,
+        title: str,
+        impact_level: str | None,
+        summary: str | None,
+    ) -> None:
+        """Append a notification with an auto-incrementing id."""
+        self._notifications.append({
+            "id": self._next_id,
+            "event_id": event_id,
+            "kind": kind,
+            "title": title,
+            "impact_level": impact_level,
+            "summary": summary,
+            "created_at": datetime.now(UTC).isoformat(),
+        })
+        self._next_id += 1
+
+    def list_for_user(self, user_id: str, limit: int = 20) -> list[dict]:
+        """Return the newest `limit` notifications with this user's read state."""
+        ordered = sorted(self._notifications, key=lambda n: n["id"], reverse=True)
+        return [
+            {**n, "read": (n["id"], user_id) in self._reads}
+            for n in ordered[:limit]
+        ]
+
+    def unread_count(self, user_id: str) -> int:
+        """Count notifications this user has not yet read."""
+        return sum(1 for n in self._notifications if (n["id"], user_id) not in self._reads)
+
+    def mark_all_read(self, user_id: str) -> int:
+        """Add a read receipt for every currently-unread notification."""
+        marked = 0
+        for n in self._notifications:
+            key = (n["id"], user_id)
+            if key not in self._reads:
+                self._reads.add(key)
+                marked += 1
+        return marked
@@ -0,0 +1,234 @@
+"""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,
+    raw_text         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);
+"""
+
+_ADD_COLUMNS = """
+ALTER TABLE regulation_events ADD COLUMN IF NOT EXISTS raw_text TEXT;
+"""
+
+_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", "raw_text",
+)
+
+
+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)
+                    # CREATE TABLE IF NOT EXISTS is a no-op on deployments that
+                    # already have this table, so new columns must be added
+                    # explicitly or existing installations silently lack them.
+                    cur.execute(_ADD_COLUMNS)
+                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
@@ -0,0 +1,157 @@
+"""PostgreSQL-backed notification store.
+
+One row per triggering event, shared by every user; a separate read-receipt
+table tracks per-user read state so broadcasting to everyone needs no fan-out
+insert per user. See base_notification_store.py for the port contract and the
+design doc for why this shape was chosen over per-user subscriptions.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+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_notification_store import BaseNotificationStore
+
+_CREATE_TABLES = """
+CREATE TABLE IF NOT EXISTS perception_notifications (
+    id           SERIAL PRIMARY KEY,
+    event_id     TEXT NOT NULL REFERENCES regulation_events(id) ON DELETE CASCADE,
+    kind         TEXT NOT NULL,
+    title        TEXT NOT NULL,
+    impact_level TEXT,
+    summary      TEXT,
+    created_at   TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+CREATE TABLE IF NOT EXISTS perception_notification_reads (
+    notification_id INTEGER NOT NULL REFERENCES perception_notifications(id) ON DELETE CASCADE,
+    user_id          TEXT NOT NULL,
+    read_at          TIMESTAMPTZ NOT NULL DEFAULT now(),
+    PRIMARY KEY (notification_id, user_id)
+);
+CREATE INDEX IF NOT EXISTS perception_notif_created
+    ON perception_notifications (created_at DESC);
+"""
+
+
+def _row_to_dict(row: dict[str, Any]) -> dict:
+    """Convert a psycopg2 RealDictRow to a plain dict with an ISO timestamp."""
+    d = dict(row)
+    if d.get("created_at") is not None:
+        d["created_at"] = d["created_at"].isoformat()
+    return d
+
+
+class PostgresNotificationStore(BaseNotificationStore):
+    """Notification store backed by PostgreSQL."""
+
+    def __init__(self) -> None:
+        """Open a connection pool and ensure both tables exist."""
+        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_TABLES)
+                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 create(
+        self,
+        *,
+        event_id: str,
+        kind: str,
+        title: str,
+        impact_level: str | None,
+        summary: str | None,
+    ) -> None:
+        """Insert one notification row for the triggering event."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "INSERT INTO perception_notifications "
+                    "(event_id, kind, title, impact_level, summary) "
+                    "VALUES (%s, %s, %s, %s, %s)",
+                    (event_id, kind, title, impact_level, summary),
+                )
+            conn.commit()
+
+    def list_for_user(self, user_id: str, limit: int = 20) -> list[dict]:
+        """Return the newest notifications with this user's read state joined in."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    SELECT n.*, (r.user_id IS NOT NULL) AS read
+                    FROM perception_notifications n
+                    LEFT JOIN perception_notification_reads r
+                        ON r.notification_id = n.id AND r.user_id = %s
+                    ORDER BY n.created_at DESC
+                    LIMIT %s
+                    """,
+                    (user_id, limit),
+                )
+                return [_row_to_dict(r) for r in cur.fetchall()]
+
+    def unread_count(self, user_id: str) -> int:
+        """Count notifications with no read receipt for this user."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    """
+                    SELECT COUNT(*) FROM perception_notifications n
+                    WHERE NOT EXISTS (
+                        SELECT 1 FROM perception_notification_reads r
+                        WHERE r.notification_id = n.id AND r.user_id = %s
+                    )
+                    """,
+                    (user_id,),
+                )
+                return cur.fetchone()[0]
+
+    def mark_all_read(self, user_id: str) -> int:
+        """Insert a read receipt for every notification this user hasn't read."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    """
+                    INSERT INTO perception_notification_reads (notification_id, user_id)
+                    SELECT n.id, %s FROM perception_notifications n
+                    WHERE NOT EXISTS (
+                        SELECT 1 FROM perception_notification_reads r
+                        WHERE r.notification_id = n.id AND r.user_id = %s
+                    )
+                    ON CONFLICT (notification_id, user_id) DO NOTHING
+                    """,
+                    (user_id, user_id),
+                )
+                marked = cur.rowcount
+            conn.commit()
+        return marked
@@ -0,0 +1,224 @@
+"""Deterministic change detection between two versions of a regulation.
+
+This module deliberately contains no LLM call, no network access, and no
+embedding lookup. It exists because the previous implementation decided whether
+a paragraph had changed by comparing embedding cosine similarity against a 0.85
+threshold, which is blind to exactly the edits that matter in regulation.
+Measured against the deployed text-embedding-v3 gateway, tightening a braking
+limit from 30米 to 20米 scores 0.9153 and relaxing 应当 to 宜 scores 0.9162 —
+both far above the threshold, both undetected — while an entirely unrelated
+clause scores 0.6862 and is the only thing that fires. Cosine is scale
+invariant, so it cannot represent a change in magnitude or certainty
+(arXiv:2403.05440, ACM Web Conference 2024); no threshold recovers the signal.
+
+The replacement is the production consensus for legal text: align paragraphs
+with a longest-common-subsequence matcher, run a literal character diff on the
+aligned pairs, and let cheap deterministic rules decide whether a change is
+significant enough to spend an LLM call classifying.
+"""
+
+from __future__ import annotations
+
+import re
+import unicodedata
+from dataclasses import dataclass, field
+
+from diff_match_patch import diff_match_patch
+from difflib import SequenceMatcher
+
+from app.config.settings import settings
+
+# Chinese regulatory drafting uses a small, near-unambiguous set of deontic
+# markers, so a regex pre-pass identifies legally significant edits without an
+# LLM. Adding or removing any of these changes what the provision compels.
+_DEONTIC_PATTERN = re.compile(r"应当|须|禁止|不得|可以|允许|宜")
+
+# Matches digit runs including decimals, so "30" -> "20" and "0.85" -> "0.9"
+# are both treated as numeric changes.
+_NUMBER_PATTERN = re.compile(r"\d+(?:\.\d+)?")
+
+# diff_match_patch operation codes.
+_DMP_DELETE = -1
+_DMP_INSERT = 1
+_DMP_EQUAL = 0
+
+
+@dataclass(frozen=True)
+class ParagraphChange:
+    """One detected difference between the old and new version of a regulation.
+
+    `needs_llm` is the gate: it records whether this change is worth the cost of
+    an LLM classification call. The deterministic flags that drive it are kept
+    on the record so downstream code can act on them even when the LLM call
+    fails or is skipped.
+    """
+
+    change_type: str
+    old_text: str
+    new_text: str
+    numeric_changed: bool
+    deontic_changed: bool
+    change_ratio: float
+    needs_llm: bool
+    # (op, text) pairs from diff_match_patch, for rendering a redline view.
+    diff_spans: list[tuple[int, str]] = field(default_factory=list)
+
+
+def _split_paragraphs(text: str) -> list[str]:
+    """Split regulation text into comparable units, dropping blank lines.
+
+    ponytail: newline splitting, not clause parsing. Upgrade to 第X条 / X.X.X
+    segmentation only if paragraph granularity proves too coarse in practice.
+    """
+    return [line.strip() for line in (text or "").split("\n") if line.strip()]
+
+
+def _numbers_differ(old: str, new: str) -> bool:
+    """Report whether the two spans contain a different sequence of numbers."""
+    return _NUMBER_PATTERN.findall(old) != _NUMBER_PATTERN.findall(new)
+
+
+def _deontic_differs(old: str, new: str) -> bool:
+    """Report whether obligation markers were added, removed, or swapped."""
+    return sorted(_DEONTIC_PATTERN.findall(old)) != sorted(_DEONTIC_PATTERN.findall(new))
+
+
+def _is_cosmetic(spans: list[tuple[int, str]]) -> bool:
+    """Report whether the edit touched nothing but punctuation and whitespace.
+
+    A change ratio alone cannot answer this for Chinese regulation text. Clauses
+    run 20-60 characters, so deleting a single 。 is a 4% change and clears any
+    threshold low enough to still catch real edits in longer paragraphs. Testing
+    what actually changed is both cheaper and exact.
+    """
+    changed = "".join(text for op, text in spans if op != _DMP_EQUAL)
+    # Unicode categories P (punctuation), Z (separator) and C (control) cover
+    # Chinese and ASCII punctuation plus every flavour of whitespace.
+    return all(unicodedata.category(char)[0] in {"P", "Z", "C"} for char in changed)
+
+
+class RegulationDiffer:
+    """Align two regulation versions and classify what changed, without an LLM."""
+
+    def __init__(self, min_change_ratio: float | None = None) -> None:
+        """Store the gate threshold, defaulting to the configured value.
+
+        The explicit argument exists so tests never depend on the deployed .env.
+        """
+        self._min_change_ratio = (
+            settings.perception_diff_min_change_ratio
+            if min_change_ratio is None
+            else min_change_ratio
+        )
+        self._dmp = diff_match_patch()
+
+    def diff(self, old_text: str, new_text: str) -> list[ParagraphChange]:
+        """Return every changed paragraph between two versions.
+
+        Unchanged paragraphs are not returned. An empty old version means there
+        is no baseline to compare against — the caller's first crawl — so no
+        changes are reported rather than the whole document being called new.
+        """
+        old_paras = _split_paragraphs(old_text)
+        new_paras = _split_paragraphs(new_text)
+
+        if not old_paras or not new_paras:
+            return []
+
+        # autojunk=False is load-bearing: the default treats any element
+        # appearing in over 1% of a sequence of 200+ items as junk, and
+        # regulations repeat boilerplate paragraphs that alignment depends on
+        # as anchors.
+        matcher = SequenceMatcher(None, old_paras, new_paras, autojunk=False)
+
+        changes: list[ParagraphChange] = []
+        for tag, i1, i2, j1, j2 in matcher.get_opcodes():
+            if tag == "equal":
+                continue
+            if tag == "insert":
+                changes.extend(self._added(p) for p in new_paras[j1:j2])
+            elif tag == "delete":
+                changes.extend(self._removed(p) for p in old_paras[i1:i2])
+            elif tag == "replace":
+                changes.extend(self._replaced(old_paras[i1:i2], new_paras[j1:j2]))
+
+        return changes
+
+    def _added(self, paragraph: str) -> ParagraphChange:
+        """Build a record for a provision present only in the new version."""
+        return ParagraphChange(
+            change_type="added",
+            old_text="",
+            new_text=paragraph,
+            numeric_changed=False,
+            deontic_changed=bool(_DEONTIC_PATTERN.search(paragraph)),
+            change_ratio=1.0,
+            # A new provision always carries new obligations, so it is always
+            # worth classifying.
+            needs_llm=True,
+            diff_spans=[(_DMP_INSERT, paragraph)],
+        )
+
+    def _removed(self, paragraph: str) -> ParagraphChange:
+        """Build a record for a provision dropped from the new version."""
+        return ParagraphChange(
+            change_type="removed",
+            old_text=paragraph,
+            new_text="",
+            numeric_changed=False,
+            deontic_changed=bool(_DEONTIC_PATTERN.search(paragraph)),
+            change_ratio=1.0,
+            needs_llm=True,
+            diff_spans=[(_DMP_DELETE, paragraph)],
+        )
+
+    def _replaced(self, old_block: list[str], new_block: list[str]) -> list[ParagraphChange]:
+        """Compare a run of rewritten paragraphs pairwise, reporting the remainder.
+
+        SequenceMatcher emits `replace` for a whole run at once, and the two
+        sides may differ in length. Pairing by position within the run is safe
+        here because alignment has already established that this run as a whole
+        corresponds; any surplus on either side is a genuine insertion or
+        deletion.
+        """
+        results: list[ParagraphChange] = []
+        for index in range(max(len(old_block), len(new_block))):
+            if index >= len(old_block):
+                results.append(self._added(new_block[index]))
+            elif index >= len(new_block):
+                results.append(self._removed(old_block[index]))
+            else:
+                results.append(self._modified(old_block[index], new_block[index]))
+        return results
+
+    def _modified(self, old: str, new: str) -> ParagraphChange:
+        """Character-diff an aligned pair and decide whether it warrants an LLM call."""
+        spans = self._dmp.diff_main(old, new)
+        # Merges single-character edits into human-meaningful chunks so the
+        # redline view and the change ratio both reflect real edits.
+        self._dmp.diff_cleanupSemantic(spans)
+
+        changed_chars = sum(len(text) for op, text in spans if op != _DMP_EQUAL)
+        denominator = max(len(old), len(new), 1)
+        change_ratio = changed_chars / denominator
+
+        numeric_changed = _numbers_differ(old, new)
+        deontic_changed = _deontic_differs(old, new)
+
+        # A changed limit or obligation marker is always significant no matter
+        # how few characters moved. Everything else must be substantive and
+        # clear the ratio gate to be worth a model call.
+        significant = numeric_changed or deontic_changed or (
+            not _is_cosmetic(spans) and change_ratio >= self._min_change_ratio
+        )
+
+        return ParagraphChange(
+            change_type="modified",
+            old_text=old,
+            new_text=new,
+            numeric_changed=numeric_changed,
+            deontic_changed=deontic_changed,
+            change_ratio=change_ratio,
+            needs_llm=significant,
+            diff_spans=[(op, text) for op, text in spans],
+        )
@@ -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
@@ -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):
@@ -0,0 +1,142 @@
+"""Postgres-backed persistence for cumulative AI model usage counters.
+
+Keeps ModelUsageTracker (an in-memory, process-lifetime-only registry defined
+in app/shared/model_usage_tracker.py) from losing its counters on every
+backend restart. This store only ever persists the *current cumulative
+snapshot* per provider+model — not a historical time-series log — matching
+the "durable counters" scope decided in
+docs/superpowers/specs/2026-07-23-status-model-usage-hardening-design.md.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+
+import psycopg2
+import psycopg2.extras
+from psycopg2.pool import ThreadedConnectionPool
+
+from app.config.settings import settings
+from app.shared.model_usage_tracker import ModelUsageEntry
+
+# Table creation follows the same CREATE TABLE IF NOT EXISTS idiom used by
+# every other Postgres store in this codebase — no migration framework.
+_CREATE_TABLE = """
+CREATE TABLE IF NOT EXISTS model_usage_stats (
+    provider          VARCHAR(64)  NOT NULL,
+    model             VARCHAR(128) NOT NULL,
+    total_tokens      BIGINT       NOT NULL DEFAULT 0,
+    prompt_tokens     BIGINT       NOT NULL DEFAULT 0,
+    completion_tokens BIGINT       NOT NULL DEFAULT 0,
+    call_count_ok     BIGINT       NOT NULL DEFAULT 0,
+    call_count_error  BIGINT       NOT NULL DEFAULT 0,
+    last_called_at    TIMESTAMPTZ,
+    last_latency_ms   INTEGER,
+    last_error        TEXT,
+    updated_at        TIMESTAMPTZ  NOT NULL DEFAULT NOW(),
+    PRIMARY KEY (provider, model)
+);
+"""
+
+_UPSERT = """
+INSERT INTO model_usage_stats
+    (provider, model, total_tokens, prompt_tokens, completion_tokens,
+     call_count_ok, call_count_error, last_called_at, last_latency_ms, last_error, updated_at)
+VALUES
+    (%(provider)s, %(model)s, %(total_tokens)s, %(prompt_tokens)s, %(completion_tokens)s,
+     %(call_count_ok)s, %(call_count_error)s, %(last_called_at)s, %(last_latency_ms)s, %(last_error)s, NOW())
+ON CONFLICT (provider, model) DO UPDATE SET
+    total_tokens = EXCLUDED.total_tokens,
+    prompt_tokens = EXCLUDED.prompt_tokens,
+    completion_tokens = EXCLUDED.completion_tokens,
+    call_count_ok = EXCLUDED.call_count_ok,
+    call_count_error = EXCLUDED.call_count_error,
+    last_called_at = EXCLUDED.last_called_at,
+    last_latency_ms = EXCLUDED.last_latency_ms,
+    last_error = EXCLUDED.last_error,
+    updated_at = NOW();
+"""
+
+
+class PostgresModelUsageStore:
+    """Load and flush ModelUsageTracker snapshots to/from a Postgres table."""
+
+    def __init__(self) -> None:
+        """Open a small connection pool and ensure the table exists."""
+        self._pool = ThreadedConnectionPool(
+            minconn=1,
+            maxconn=3,
+            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:
+        """Create the model_usage_stats table if it does not already exist."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(_CREATE_TABLE)
+            conn.commit()
+
+    @contextmanager
+    def _conn(self):
+        """Borrow a pooled connection and always return it, even on error."""
+        conn = self._pool.getconn()
+        try:
+            yield conn
+        finally:
+            self._pool.putconn(conn)
+
+    def load_all(self) -> dict[str, ModelUsageEntry]:
+        """Return every persisted row as {"provider:model": ModelUsageEntry}."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute("SELECT * FROM model_usage_stats")
+                rows = cur.fetchall()
+        entries: dict[str, ModelUsageEntry] = {}
+        for row in rows:
+            entry = ModelUsageEntry(
+                provider=row["provider"],
+                model=row["model"],
+                total_tokens=row["total_tokens"],
+                prompt_tokens=row["prompt_tokens"],
+                completion_tokens=row["completion_tokens"],
+                call_count_ok=row["call_count_ok"],
+                call_count_error=row["call_count_error"],
+                last_called_at=row["last_called_at"],
+                last_latency_ms=row["last_latency_ms"],
+                last_error=row["last_error"],
+            )
+            entries[f"{entry.provider}:{entry.model}"] = entry
+        return entries
+
+    def flush(self, entries: dict[str, ModelUsageEntry]) -> None:
+        """Upsert the current cumulative snapshot of every tracked entry.
+
+        A no-op for an empty snapshot — avoids opening a connection for nothing
+        (e.g. before any LLM/embedding/reranker call has happened yet).
+        """
+        if not entries:
+            return
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                for entry in entries.values():
+                    cur.execute(
+                        _UPSERT,
+                        {
+                            "provider": entry.provider,
+                            "model": entry.model,
+                            "total_tokens": entry.total_tokens,
+                            "prompt_tokens": entry.prompt_tokens,
+                            "completion_tokens": entry.completion_tokens,
+                            "call_count_ok": entry.call_count_ok,
+                            "call_count_error": entry.call_count_error,
+                            "last_called_at": entry.last_called_at,
+                            "last_latency_ms": entry.last_latency_ms,
+                            "last_error": entry.last_error,
+                        },
+                    )
+            conn.commit()
@@ -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.
+"""
@@ -0,0 +1,56 @@
+"""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",
+        "app.infrastructure.tasks.perception_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,
+    # Scheduled counterpart to the Perception page's manual "Refresh" button.
+    # Only takes effect while a Beat process is running (./dev.sh start beat).
+    beat_schedule={
+        "crawl-regulations-periodic": {
+            "task": "app.infrastructure.tasks.perception_tasks.crawl_regulations_task",
+            "schedule": settings.perception_crawl_interval_seconds,
+        },
+    },
+)
@@ -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)
@@ -0,0 +1,63 @@
+"""Celery task for scheduled regulatory source crawling.
+
+This is the scheduled counterpart to the Perception page's manual "Refresh"
+button (POST /perception/crawl). Every architecture reference document
+describes source monitoring as continuous ("定时爬取"), not operator-triggered,
+so this task is what Celery Beat runs on a fixed interval once an operator
+starts a Beat process.
+"""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from app.infrastructure.tasks.celery_app import celery_app
+
+
+@celery_app.task(
+    name="app.infrastructure.tasks.perception_tasks.crawl_regulations_task",
+    bind=True,
+)
+def crawl_regulations_task(self) -> dict:
+    """Crawl every registered regulatory source and enrich new/changed events.
+
+    Drains CrawlService.run_crawl(), which already isolates each source's
+    fetch and each event's enrichment behind its own try/except — a source
+    outage or a single bad event yields an "error" progress item and the
+    generator continues. Re-catching those here would only hide problems the
+    service has already handled, so this task's job is limited to counting
+    them and logging a summary.
+
+    No automatic retry is configured. An exception escaping run_crawl itself
+    means something broke in a way the service's own error handling did not
+    anticipate; the next scheduled tick already provides a retry within
+    settings.perception_crawl_interval_seconds, so an immediate retry against
+    the same failure is not worth the added complexity.
+
+    ponytail: relies on a single worker process to serialize scheduled runs
+    (Celery's default concurrency processes one task at a time, so a run that
+    outlasts the interval delays the next tick rather than overlapping it).
+    Add a Redis-based lock (e.g. SETNX on a per-task key) if this queue is
+    ever served by more than one worker.
+    """
+    from app.shared.bootstrap import get_crawl_service
+
+    error_count = 0
+    new_count = 0
+    updated_count = 0
+
+    for item in get_crawl_service().run_crawl():
+        event = item.get("event")
+        if event == "error":
+            error_count += 1
+            logger.warning("Scheduled crawl source error: {}", item.get("data"))
+        elif event == "done":
+            data = item.get("data") or {}
+            new_count = data.get("total_new", 0)
+            updated_count = data.get("total_updated", 0)
+
+    logger.info(
+        "Scheduled crawl finished: new={} updated={} source_errors={}",
+        new_count, updated_count, error_count,
+    )
+    return {"new": new_count, "updated": updated_count, "source_errors": error_count}
@@ -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
-        payload = {"query": query, "texts": texts, "raw_scores": False, "return_text": False}
+        # TEI format: POST /rerank — include model name (required by gateway proxies)
+        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:
-            # Fall back to Cohere / OpenAI-style: POST /v1/rerank
+        if resp.status_code in (404, 400):
+            # 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()

@@ -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]
@@ -0,0 +1,8 @@
+"""MCP (Model Context Protocol) server module.
+
+Exposes selected read-only platform capabilities — currently only regulation
+search — as MCP tools so external MCP clients (Claude Desktop, GitHub Copilot,
+Cursor, etc.) can query this platform's compliance knowledge base directly.
+"""
+# Kept deliberately empty beyond this docstring — see server.py for the
+# actual FastMCP instance and tool/middleware definitions.
@@ -0,0 +1,201 @@
+"""MCPServer instance exposing the compliance knowledge base as an MCP tool.
+
+This module is a pure protocol adapter: search_regulations() below calls the
+existing AgentConversationService.ask() (the same application service backing
+the /api/v1/agent/ask REST endpoint) and reshapes its result into a plain
+dict. No new retrieval, ranking, or LLM orchestration logic lives here.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Annotated
+
+from mcp.server import MCPServer
+from mcp.server.transport_security import TransportSecuritySettings
+from pydantic import Field
+from starlette.responses import PlainTextResponse
+from starlette.types import ASGIApp, Receive, Scope, Send
+
+from app.config.settings import settings
+from app.mcp.stats import get_mcp_stats_tracker
+from app.shared.bootstrap import get_agent_conversation_service, get_jwt_handler
+
+logger = logging.getLogger(__name__)
+
+# Single shared MCPServer instance — analogous to the single shared FastAPI
+# `app` instance in app/api/main.py. Tools registered via @mcp.tool() below.
+# Note: the installed mcp SDK (2.0.0) renamed the older "FastMCP" class to
+# "MCPServer" (mcp.server.mcpserver.MCPServer); the .tool()/.streamable_http_app()
+# API surface used here is unchanged across that rename.
+mcp = MCPServer("ai-regulations")
+
+
+@mcp.tool()
+def search_regulations(
+    query: Annotated[str, Field(min_length=1, max_length=2000)],
+    top_k: Annotated[int, Field(ge=1, le=20)] = 5,
+) -> dict:
+    """Search the compliance knowledge base and return a grounded answer.
+
+    query: Natural-language search question, e.g. "国六排放标准最新要求".
+    top_k: Maximum number of cited sources to return (1-20, default 5).
+    """
+    # Bounds mirror AskRequest in app/api/models/agent.py so the MCP path cannot
+    # be used to bypass the REST endpoint's limits. They matter more here than
+    # there: KnowledgeRetrievalService amplifies top_k (candidate_k = top_k * 4)
+    # when reranking, and an LLM client can easily hallucinate a huge value.
+    # Declaring them via Annotated puts them in the advertised JSON schema too,
+    # so well-behaved clients never send an out-of-range value in the first place.
+    #
+    # No session_id is passed: this keeps each call stateless (no
+    # ConversationStore reads/writes), matching "search" semantics rather
+    # than multi-turn chat semantics.
+    started = time.perf_counter()
+    try:
+        _, result = get_agent_conversation_service().ask(query=query, top_k=top_k)
+    except Exception:
+        # Record the failure, then re-raise unchanged so the MCP SDK still
+        # converts it into a protocol-level error for the client. Swallowing
+        # it here would report success to the caller.
+        get_mcp_stats_tracker().record(
+            tool="search_regulations",
+            duration_ms=(time.perf_counter() - started) * 1000,
+            success=False,
+        )
+        raise
+    get_mcp_stats_tracker().record(
+        tool="search_regulations",
+        duration_ms=(time.perf_counter() - started) * 1000,
+        success=True,
+    )
+    return {
+        "answer": result.answer,
+        "sources": [source.__dict__ for source in result.sources],
+    }
+
+
+class MCPAuthMiddleware:
+    """Reject unauthenticated requests before they reach the MCP protocol handler.
+
+    Mirrors the existing get_current_user dependency's behavior (auth.py) but
+    implemented as raw ASGI middleware, since the mounted MCP app is a plain
+    ASGI app, not a FastAPI/APIRouter instance that supports Depends().
+    """
+
+    def __init__(self, app: ASGIApp) -> None:
+        """Store the wrapped ASGI app to delegate to once auth passes."""
+        self.app = app
+
+    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
+        """Validate the bearer token for HTTP requests; pass non-HTTP scopes through."""
+        # Only HTTP requests carry an Authorization header to check; lifespan
+        # and other scope types must always pass through untouched.
+        if scope["type"] != "http" or not settings.auth_enabled:
+            await self.app(scope, receive, send)
+            return
+
+        headers = dict(scope["headers"])
+        # ASGI header values are raw bytes specified as latin-1, not UTF-8;
+        # decoding strictly as UTF-8 would raise on a malformed byte and turn a
+        # bad request into an unhandled 500.
+        auth_header = headers.get(b"authorization", b"").decode("latin-1")
+        token = auth_header.removeprefix("Bearer ").strip()
+        try:
+            get_jwt_handler().decode_token(token)
+        except ValueError as exc:
+            # Reject before the MCP session/protocol layer ever sees the request.
+            # WWW-Authenticate matches the get_current_user dependency (auth.py)
+            # and is required by RFC 7235 so clients can tell "needs credentials"
+            # apart from a generic failure.
+            response = PlainTextResponse(
+                str(exc), status_code=401, headers={"WWW-Authenticate": "Bearer"}
+            )
+            await response(scope, receive, send)
+            return
+
+        await self.app(scope, receive, send)
+
+
+def _parse_allowed_hosts() -> list[str]:
+    """Split the configured MCP host allow-list into individual entries."""
+    # Shared by the transport-security builder and the status endpoint so the
+    # panel can never display an allow-list different from the enforced one.
+    return [h.strip() for h in settings.mcp_allowed_hosts.split(",") if h.strip()]
+
+
+def _build_transport_security() -> TransportSecuritySettings:
+    """Translate the configured MCP host allow-list into SDK transport settings.
+
+    Without this the SDK infers its own allow-list from the bind host, which
+    defaults to 127.0.0.1 and therefore rejects every remote client with HTTP
+    421 — fatal for a remotely deployed backend.
+    """
+    allowed = _parse_allowed_hosts()
+    if "*" in allowed:
+        # Explicit, logged opt-out. Kept as an escape hatch for environments
+        # behind a proxy that rewrites Host unpredictably, but never the default.
+        logger.warning(
+            "MCP DNS-rebinding protection is disabled (mcp_allowed_hosts='*'). "
+            "Set MCP_ALLOWED_HOSTS to the real deployment host(s) instead."
+        )
+        return TransportSecuritySettings(enable_dns_rebinding_protection=False)
+    return TransportSecuritySettings(
+        enable_dns_rebinding_protection=True,
+        allowed_hosts=allowed,
+        # Browser clients send Origin; reuse the already-maintained CORS list so
+        # there is one place to declare trusted web origins. Non-browser MCP
+        # clients send no Origin at all, which the SDK treats as allowed.
+        allowed_origins=[o.strip() for o in settings.cors_allow_origins.split(",") if o.strip()],
+    )
+
+
+def build_mcp_asgi_app() -> ASGIApp:
+    """Return the Streamable HTTP ASGI app for the MCP server, auth-guarded.
+
+    streamable_http_path="/" is required here: MCPServer.streamable_http_app()
+    registers its own internal route at "/mcp" by default, and this app is
+    itself mounted at "/mcp" in api/main.py — without overriding the internal
+    path to "/", the effective external path would be the confusing "/mcp/mcp"
+    instead of "/mcp".
+    """
+    asgi_app = mcp.streamable_http_app(
+        streamable_http_path="/",
+        transport_security=_build_transport_security(),
+    )
+    asgi_app.add_middleware(MCPAuthMiddleware)
+    return asgi_app
+
+
+async def get_mcp_status(public_url: str) -> dict:
+    """Assemble the MCP status payload shown on the System Status page.
+
+    Owned by this module rather than the status route so that MCP internals
+    (the tool registry, the allow-list format, the stats tracker) stay behind
+    one boundary; the route only supplies public_url, which is the one value
+    only the HTTP layer can know.
+    """
+    stats = get_mcp_stats_tracker().snapshot()
+    # list_tools() reads the in-memory registry populated by @mcp.tool() at
+    # import time, so the panel always reflects what is actually advertised
+    # rather than a hand-maintained duplicate list.
+    tools = await mcp.list_tools()
+    return {
+        "endpoint_url": public_url,
+        "auth_required": settings.auth_enabled,
+        "allowed_hosts": _parse_allowed_hosts(),
+        "tools": [
+            {
+                "name": tool.name,
+                "description": (tool.description or "").strip().split("\n")[0],
+                "calls": entry.calls if entry else 0,
+                "errors": entry.errors if entry else 0,
+                "avg_duration_ms": entry.avg_duration_ms if entry else None,
+                "last_called_at": (
+                    entry.last_called_at.isoformat() if entry and entry.last_called_at else None
+                ),
+            }
+            for tool, entry in ((tool, stats.get(tool.name)) for tool in tools)
+        ],
+    }
@@ -0,0 +1,91 @@
+"""In-memory per-tool call counters for the MCP server.
+
+Lives in `app/mcp/` rather than `app/shared/` because these counters are
+meaningful only for the MCP transport: they answer "is anything actually
+calling our MCP endpoint, and does it work?" for the System Status page.
+Token consumption is deliberately not tracked here — MCP tool calls route
+through AgentConversationService.ask() like every other caller, so the
+existing ModelUsageTracker already accounts for it.
+
+Counters are process-local and reset on restart. That is an accepted
+tradeoff, recorded in the design spec: nothing billable depends on them.
+"""
+
+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 MCPToolStats:
+    """Accumulated call outcomes for a single MCP tool."""
+
+    calls: int = 0
+    errors: int = 0
+    total_duration_ms: float = 0.0
+    last_called_at: datetime | None = None
+
+    @property
+    def avg_duration_ms(self) -> float | None:
+        """Mean call duration, or None when the tool has never been called.
+
+        Returning None rather than 0.0 keeps "never called" distinguishable
+        from "called, but instantaneous" in the status UI.
+        """
+        if self.calls == 0:
+            return None
+        return self.total_duration_ms / self.calls
+
+
+class MCPStatsTracker:
+    """Thread-safe registry of per-tool MCP call statistics.
+
+    The lock is load-bearing, not defensive habit: the mcp SDK dispatches
+    synchronous tool functions through anyio.to_thread.run_sync, so tool
+    bodies genuinely run on multiple worker threads at once — unlike the
+    async REST routes, which are serialized by the event loop.
+    """
+
+    def __init__(self) -> None:
+        """Initialize an empty registry guarded by a single lock."""
+        self._tools: dict[str, MCPToolStats] = {}
+        # One coarse lock is enough: record() runs once per MCP tool call and
+        # snapshot() is only read by the low-traffic status endpoint.
+        self._lock = threading.Lock()
+
+    def record(self, *, tool: str, duration_ms: float, success: bool) -> None:
+        """Record the outcome of one MCP tool invocation.
+
+        Never raises: a defect in observability code must not turn a working
+        tool call into a protocol error for the client.
+        """
+        try:
+            # Coerce outside the lock so a bad argument cannot abort mid-update
+            # and leave calls incremented but duration unaccounted for.
+            duration = float(duration_ms)
+            now = datetime.now(timezone.utc)
+            with self._lock:
+                stats = self._tools.setdefault(tool, MCPToolStats())
+                stats.calls += 1
+                if not success:
+                    stats.errors += 1
+                stats.total_duration_ms += duration
+                stats.last_called_at = now
+        except Exception as exc:  # noqa: BLE001 - tracking must never break a real call
+            logger.warning("MCPStatsTracker.record failed for tool {} - {}", tool, exc)
+
+    def snapshot(self) -> dict[str, MCPToolStats]:
+        """Return a shallow copy of all tracked tools, safe to read outside the lock."""
+        with self._lock:
+            return dict(self._tools)
+
+
+@lru_cache
+def get_mcp_stats_tracker() -> MCPStatsTracker:
+    """Return the process-wide singleton tracker (mirrors get_model_usage_tracker())."""
+    return MCPStatsTracker()
@@ -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):
@@ -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(
@@ -1,11 +1,16 @@
-"""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
+from typing import List, Dict, Optional, Generator
 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:
@@ -101,8 +130,14 @@ class DeepSeekClient(BaseLLMClient):
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        **kwargs
-    ):
-        """Stream chat for the Deep Seek Client instance."""
+    ) -> Generator[str, None, Optional[Dict[str, int]]]:
+        """Stream chat for the Deep Seek Client instance.
+
+        Returns the trailing token-usage dict as the generator's return value
+        (read via StopIteration.value when manually driven with next()) when
+        the gateway sends one via stream_options.include_usage, else None.
+        """
+        usage: Optional[Dict[str, int]] = None
        try:
            payload = {
                "model": self.config.model,
@@ -110,7 +145,8 @@ class DeepSeekClient(BaseLLMClient):
                "max_tokens": max_tokens or self.config.max_tokens,
                "temperature": temperature or self.config.temperature,
                "top_p": kwargs.get("top_p", self.config.top_p),
-                "stream": True
+                "stream": True,
+                "stream_options": {"include_usage": True}
            }

            with self._client.stream("POST", "/chat/completions", json=payload) as response:
@@ -139,6 +175,9 @@ class DeepSeekClient(BaseLLMClient):
                            content = delta.get("content", "")
                            if content:
                                yield content
+                        elif data.get("usage"):
+                            # Trailing usage-only chunk — no content to yield, just capture it.
+                            usage = data["usage"]
                    except json.JSONDecodeError:
                        continue

@@ -149,6 +188,8 @@ class DeepSeekClient(BaseLLMClient):
            logger.error(f"DeepSeek Stream调用失败: {e}")
            yield ""

+        return usage
+
    def get_available_models(self) -> List[str]:
        """Return available models for the Deep Seek Client instance."""
        return self.SUPPORTED_MODELS
@@ -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.


@@ -14,7 +16,7 @@ from .qwen_client import QwenClient, QwenVLClient
 # Keep provider-specific behavior explicit so debugging stays straightforward.
 DEFAULT_MODELS = {
    LLMProvider.DEEPSEEK: "deepseek-v4-flash",
-    LLMProvider.QWEN: "qwen3.5-flash",
+    LLMProvider.QWEN: "qwen3.6-flash",
    LLMProvider.QWEN_VL: "qwen3-vl-plus"
 }

@@ -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."""
@@ -94,6 +101,8 @@ class LLMFactory:
            "qwen-max": LLMProvider.QWEN,
            "qwen3.5-flash": LLMProvider.QWEN,
            "qwen3.5-plus": LLMProvider.QWEN,
+            "qwen3.6-flash": LLMProvider.QWEN,
+            "qwen3.6-plus": LLMProvider.QWEN,
            "qwen_vl": LLMProvider.QWEN_VL,
            "qwen-vl": LLMProvider.QWEN_VL,
            "qwen-vl-plus": LLMProvider.QWEN_VL,
@@ -137,7 +146,7 @@ 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 +209,7 @@ def get_llm_client(
    provider: str = "qwen",
    model: Optional[str] = None,
    **kwargs
-) -> BaseLLMClient:
+) -> "BaseLLMClient | TrackedLLMClient":
    """Return llm client."""
    factory = get_llm_factory()

@@ -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.


@@ -22,6 +27,8 @@ class QwenClient(BaseLLMClient):
        "qwen-long",
        "qwen3.5-flash",
        "qwen3.5-plus",
+        "qwen3.6-flash",
+        "qwen3.6-plus",
        "qwen3-plus",
        "qwen2.5-72b-instruct",
        "qwen2.5-32b-instruct",
@@ -54,14 +61,20 @@ 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 +83,11 @@ 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 +100,24 @@ 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:
@@ -112,8 +142,14 @@ class QwenClient(BaseLLMClient):
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        **kwargs
-    ) -> Generator[str, None, None]:
-        """Stream chat for the Qwen Client instance."""
+    ) -> Generator[str, None, Optional[Dict[str, int]]]:
+        """Stream chat for the Qwen Client instance.
+
+        Returns the trailing token-usage dict as the generator's return value
+        (read via StopIteration.value when manually driven with next()) when
+        the gateway sends one via stream_options.include_usage, else None.
+        """
+        usage: Optional[Dict[str, int]] = None
        try:
            # Keep provider-specific behavior explicit so debugging stays straightforward.
            payload = {
@@ -122,7 +158,8 @@ class QwenClient(BaseLLMClient):
                "max_tokens": max_tokens or self.config.max_tokens,
                "temperature": temperature or self.config.temperature,
                "top_p": kwargs.get("top_p", self.config.top_p),
-                "stream": True  # Keep provider-specific behavior explicit so debugging stays straightforward.
+                "stream": True,  # Keep provider-specific behavior explicit so debugging stays straightforward.
+                "stream_options": {"include_usage": True}
            }

            # Keep provider-specific behavior explicit so debugging stays straightforward.
@@ -139,6 +176,9 @@ class QwenClient(BaseLLMClient):
                                data = json.loads(data_str)
                                choices = data.get("choices", [])
                                if not choices:
+                                    if data.get("usage"):
+                                        # Trailing usage-only chunk — capture it, nothing to yield.
+                                        usage = data["usage"]
                                    continue  # Keep provider-specific behavior explicit so debugging stays straightforward.
                                delta = choices[0].get("delta", {})
                                content = delta.get("content", "")
@@ -155,6 +195,8 @@ class QwenClient(BaseLLMClient):
            logger.error(f"Qwen流式调用失败: {e}")
            yield f"[ERROR: {str(e)}]"

+        return usage
+
    async def async_stream_chat(
        self,
        messages: List[Dict[str, str]],
@@ -271,8 +313,14 @@ class QwenVLClient(BaseLLMClient):
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        **kwargs
-    ) -> Generator[str, None, None]:
-        """Stream chat for the Qwen V L Client instance."""
+    ) -> Generator[str, None, Optional[Dict[str, int]]]:
+        """Stream chat for the Qwen V L Client instance.
+
+        Returns the trailing token-usage dict as the generator's return value
+        (read via StopIteration.value when manually driven with next()) when
+        the gateway sends one via stream_options.include_usage, else None.
+        """
+        usage: Optional[Dict[str, int]] = None
        try:
            payload = {
                "model": self.config.model,
@@ -280,7 +328,8 @@ class QwenVLClient(BaseLLMClient):
                "max_tokens": max_tokens or self.config.max_tokens,
                "temperature": temperature or self.config.temperature,
                "top_p": kwargs.get("top_p", self.config.top_p),
-                "stream": True
+                "stream": True,
+                "stream_options": {"include_usage": True}
            }

            with self._client.stream("POST", "/chat/completions", json=payload) as response:
@@ -295,6 +344,9 @@ class QwenVLClient(BaseLLMClient):
                                data = json.loads(data_str)
                                choices = data.get("choices", [])
                                if not choices:
+                                    if data.get("usage"):
+                                        # Trailing usage-only chunk — capture it, nothing to yield.
+                                        usage = data["usage"]
                                    continue  # Keep provider-specific behavior explicit so debugging stays straightforward.
                                delta = choices[0].get("delta", {})
                                content = delta.get("content", "")
@@ -307,6 +359,8 @@ class QwenVLClient(BaseLLMClient):
            logger.error(f"QwenVL流式调用失败: {e}")
            yield f"[ERROR: {str(e)}]"

+        return usage
+
    def get_available_models(self) -> List[str]:
        """Return available models for the Qwen V L Client instance."""
        return self.SUPPORTED_MODELS
@@ -319,7 +373,7 @@ class QwenVLClient(BaseLLMClient):

 def create_qwen_client(
    api_key: str,
-    model: str = "qwen3.5-flash",
+    model: str = "qwen3.6-flash",
    base_url: str = "http://6.86.80.4:30080/v1",
    **kwargs
 ) -> QwenClient:
@@ -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,
+                },
+            },
+        }
@@ -0,0 +1,93 @@
+"""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 and usage.
+
+        Drives the inner generator manually (instead of a plain `for` loop) so
+        it can capture the generator's return value via StopIteration.value —
+        the trailing token-usage dict the inner client captures from a
+        stream_options.include_usage chunk, if the gateway sent one.
+        """
+        start = time.time()
+        error: Optional[str] = None
+        usage: Optional[Dict[str, int]] = None
+        gen = self._inner.stream_chat(messages, *args, **kwargs)
+        try:
+            while True:
+                try:
+                    chunk = next(gen)
+                except StopIteration as stop:
+                    usage = stop.value
+                    break
+                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,
+                usage=usage,
+                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)
@@ -2,10 +2,14 @@

 from __future__ import annotations

+import asyncio
 from functools import lru_cache
 from typing import Callable

+from loguru import logger
+
 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 +23,17 @@ 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.infrastructure.perception.mock_notification_store import MockNotificationStore
+from app.application.perception.crawl_service import CrawlService
+from app.infrastructure.perception.base_event_store import BaseEventStore
+from app.infrastructure.perception.base_notification_store import BaseNotificationStore
+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
@@ -26,11 +41,15 @@ from app.infrastructure.storage.minio_binary_store import MinioDocumentBinarySto
 from app.infrastructure.storage.postgres_document_processing_store import PostgresDocumentProcessingStore
 from app.infrastructure.storage.postgres_document_repository import PostgresDocumentRepository
 from app.infrastructure.storage.postgres_parse_artifact_store import PostgresParseArtifactStore
+from app.infrastructure.storage.postgres_model_usage_store import PostgresModelUsageStore
 from app.infrastructure.vectorstore.bm25_retriever import BM25Retriever
 from app.infrastructure.vectorstore.cross_encoder_reranker import OpenAICompatibleReranker
 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
+from app.shared.model_usage_tracker import get_model_usage_tracker
 # Keep shared wiring centralized so dependency construction remains consistent.


@@ -150,6 +169,14 @@ def get_parse_artifact_store():
    return None


+@lru_cache
+def get_model_usage_store():
+    """Return the Postgres model-usage store, or None when postgres backend is not enabled."""
+    if settings.document_repository_backend == "postgres":
+        return PostgresModelUsageStore()
+    return None
+
+
@lru_cache
 def get_document_processing_store():
    """Return document processing store for the active repository backend."""
@@ -252,7 +279,31 @@ 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,
@@ -269,26 +320,193 @@ 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_notification_store() -> BaseNotificationStore:
+    """Return notification store selected by DOCUMENT_REPOSITORY_BACKEND setting.
+
+    Mirrors get_event_store()'s gate: Mock in-memory when Postgres isn't
+    configured, so the feature works in local dev and tests without a
+    database.
+    """
+    if settings.document_repository_backend == "postgres":
+        from app.infrastructure.perception.postgres_notification_store import (
+            PostgresNotificationStore,
+        )
+        return PostgresNotificationStore()
+    return MockNotificationStore()
+
+
+@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(),
+        notification_store=get_notification_store(),
+        embedding_provider=get_embedding_provider(),
+        vector_index=get_vector_index(),
+    )
+
+
@lru_cache
 def get_agent_session_service() -> AgentSessionService:
    """Return agent session service."""
    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"])
+    _start_model_usage_persistence()


 def cleanup_runtime_dependencies() -> None:
    """Release runtime dependencies that expose explicit cleanup hooks."""
    LLMFactory.cleanup()
+    _stop_model_usage_persistence()
+
+
+_model_usage_flush_task: "asyncio.Task | None" = None
+
+
+def _start_model_usage_persistence() -> None:
+    """Seed ModelUsageTracker from Postgres and start its periodic flush loop.
+
+    No-op when document_repository_backend != "postgres" — ModelUsageTracker
+    then keeps behaving exactly as it always has: purely in-memory, reset on
+    every restart. Never raises: persistence must not block app startup.
+    """
+    global _model_usage_flush_task
+    try:
+        store = get_model_usage_store()
+    except Exception as exc:  # noqa: BLE001 - persistence must never block startup
+        logger.warning("Failed to initialize model usage persistence: {}", exc)
+        return
+    if store is None:
+        return
+
+    tracker = get_model_usage_tracker()
+    try:
+        tracker.seed(store.load_all())
+    except Exception as exc:  # noqa: BLE001 - a bad load must not block startup
+        logger.warning("Failed to load persisted model usage stats: {}", exc)
+
+    async def _flush_loop() -> None:
+        """Snapshot the tracker into Postgres every 60 seconds until cancelled."""
+        while True:
+            await asyncio.sleep(60)
+            try:
+                await asyncio.to_thread(store.flush, tracker.snapshot())
+            except Exception as exc:  # noqa: BLE001 - one bad cycle must not kill the loop
+                logger.warning("Failed to flush model usage stats: {}", exc)
+
+    _model_usage_flush_task = asyncio.create_task(_flush_loop())
+
+
+def _stop_model_usage_persistence() -> None:
+    """Cancel the periodic flush task and perform one best-effort final flush."""
+    global _model_usage_flush_task
+    if _model_usage_flush_task is not None:
+        _model_usage_flush_task.cancel()
+        _model_usage_flush_task = None
+
+    try:
+        store = get_model_usage_store()
+    except Exception as exc:  # noqa: BLE001 - shutdown must not crash on this
+        logger.warning("Failed to access model usage store during shutdown: {}", exc)
+        return
+    if store is None:
+        return
+    try:
+        store.flush(get_model_usage_tracker().snapshot())
+    except Exception as exc:  # noqa: BLE001 - shutdown must not crash on a flush failure
+        logger.warning("Failed final model usage flush: {}", exc)
@@ -0,0 +1,123 @@
+"""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 seed(self, entries: dict[str, ModelUsageEntry]) -> None:
+        """Bulk-load persisted entries (called once at startup, before any traffic).
+
+        Unlike record(), this replaces entries wholesale rather than
+        accumulating deltas — it exists to restore counters saved by a
+        previous process run, not to record a new call.
+        """
+        with self._lock:
+            self._entries.update(entries)
+
+    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()
@@ -1,30 +1,57 @@
+# ── Web framework ─────────────────────────────────────────────────────────────
 fastapi>=0.110.0
 uvicorn[standard]>=0.27.0
 python-multipart>=0.0.9
+# MCP server module (backend/app/mcp/) — pin >=2.0.0: that release renamed the
+# older "FastMCP" class to "MCPServer" (mcp.server.MCPServer), which is the
+# class actually used in app/mcp/server.py.
+mcp>=2.0.0

+# ── 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
+# Regulatory signal crawling (backend/app/infrastructure/perception/) — main-content
+# extraction from crawled regulation detail pages and character-level diff for change
+# detection. Import name for diff-match-patch is diff_match_patch (underscored).
+trafilatura>=2.0.0
+diff-match-patch>=20241021

+# ── 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
@@ -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
@@ -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))
@@ -0,0 +1,27 @@
+"""Shared pytest fixtures and import-time guards for the backend test suite.
+
+pytest imports this file before any test module beneath backend/tests/, which
+makes it the only reliable place to install import-time guards: individual test
+modules cannot guarantee they run first, because collection order follows
+directory names.
+"""
+
+from __future__ import annotations
+
+import sys
+from unittest.mock import MagicMock
+
+# app/shared/bootstrap.py (the composition root) eagerly imports the Postgres
+# store modules, which do `import psycopg2` at their own module scope and later
+# open a real connection pool. Any test that transitively imports bootstrap
+# would therefore bind the real driver and attempt a live TCP connection to the
+# configured production database, surfacing as a multi-second timeout rather
+# than an obvious error. Binding mocks here — before the first test module is
+# imported — makes that impossible regardless of collection order.
+# setdefault (not assignment) keeps a real psycopg2 in place if something has
+# already imported it deliberately.
+_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())
@@ -0,0 +1,2 @@
+"""Test package for the MCP module (backend/app/mcp/)."""
+# Empty package marker — no shared fixtures needed yet for this small test suite.
@@ -0,0 +1,93 @@
+"""Unit tests for MCPAuthMiddleware.
+
+Wraps a minimal dummy ASGI app (not the real MCP app) so these tests exercise
+only the auth gate, not the MCP protocol itself — keeps the test fast and
+independent of FastMCP internals.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+from starlette.applications import Starlette
+from starlette.responses import PlainTextResponse
+from starlette.routing import Route
+from starlette.testclient import TestClient
+
+from app.mcp.server import MCPAuthMiddleware
+
+
+def _dummy_app() -> Starlette:
+    """Build a minimal Starlette app that MCPAuthMiddleware can wrap."""
+    async def _ok(request):
+        """Return a fixed 200 response so tests can assert pass-through."""
+        return PlainTextResponse("ok")
+
+    app = Starlette(routes=[Route("/ping", _ok)])
+    app.add_middleware(MCPAuthMiddleware)
+    return app
+
+
+def test_missing_token_rejected_when_auth_enabled():
+    """No Authorization header + auth_enabled=True -> 401."""
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.auth_enabled = True
+        client = TestClient(_dummy_app())
+        response = client.get("/ping")
+    assert response.status_code == 401
+
+
+def test_invalid_token_rejected_when_auth_enabled():
+    """A token that fails decode_token() -> 401, request never reaches the app."""
+    fake_handler = type("H", (), {"decode_token": lambda self, t: (_ for _ in ()).throw(ValueError("bad token"))})()
+    with patch("app.mcp.server.settings") as fake_settings, \
+         patch("app.mcp.server.get_jwt_handler", return_value=fake_handler):
+        fake_settings.auth_enabled = True
+        client = TestClient(_dummy_app())
+        response = client.get("/ping", headers={"Authorization": "Bearer garbage"})
+    assert response.status_code == 401
+
+
+def test_valid_token_passes_through_when_auth_enabled():
+    """A token that decodes successfully -> request reaches the wrapped app."""
+    fake_handler = type("H", (), {"decode_token": lambda self, t: object()})()
+    with patch("app.mcp.server.settings") as fake_settings, \
+         patch("app.mcp.server.get_jwt_handler", return_value=fake_handler):
+        fake_settings.auth_enabled = True
+        client = TestClient(_dummy_app())
+        response = client.get("/ping", headers={"Authorization": "Bearer good"})
+    assert response.status_code == 200
+    assert response.text == "ok"
+
+
+def test_auth_disabled_always_passes_through():
+    """auth_enabled=False (dev mode) -> no token needed, matches get_current_user's dev bypass."""
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.auth_enabled = False
+        client = TestClient(_dummy_app())
+        response = client.get("/ping")
+    assert response.status_code == 200
+
+
+def test_401_includes_www_authenticate_header():
+    """RFC 7235 requires WWW-Authenticate on 401 so clients can tell why they failed."""
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.auth_enabled = True
+        client = TestClient(_dummy_app())
+        response = client.get("/ping")
+    assert response.status_code == 401
+    assert response.headers["WWW-Authenticate"] == "Bearer"
+
+
+def test_non_utf8_authorization_header_is_rejected_not_crashed():
+    """A non-UTF-8 header byte must yield a clean 401, not an unhandled 500.
+
+    ASGI header values are latin-1 bytes, so any remote client could otherwise
+    trigger a UnicodeDecodeError inside the middleware at will.
+    """
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.auth_enabled = True
+        client = TestClient(_dummy_app(), raise_server_exceptions=False)
+        # Bypass the http client's own header encoding by writing raw bytes.
+        response = client.get("/ping", headers={"Authorization": b"Bearer \xff\xfe"})
+    assert response.status_code == 401
@@ -0,0 +1,100 @@
+"""Tests for the in-memory MCP per-tool statistics tracker.
+
+These pin the two properties the status panel depends on: counters stay exact
+under the concurrent thread dispatch the mcp SDK uses, and recording never
+raises into a live tool call.
+"""
+
+from __future__ import annotations
+
+import threading
+
+from app.mcp.stats import MCPStatsTracker, MCPToolStats, get_mcp_stats_tracker
+
+
+def test_avg_duration_is_none_before_any_call():
+    """A never-called tool reports None, not 0.0, so the UI can distinguish them."""
+    assert MCPToolStats().avg_duration_ms is None
+
+
+def test_avg_duration_is_the_mean_of_recorded_durations():
+    """Average is computed over all calls, successful or not."""
+    tracker = MCPStatsTracker()
+    for duration in (100.0, 200.0, 300.0):
+        tracker.record(tool="search_regulations", duration_ms=duration, success=True)
+
+    stats = tracker.snapshot()["search_regulations"]
+    assert stats.calls == 3
+    assert stats.avg_duration_ms == 200.0
+
+
+def test_failures_increment_both_calls_and_errors():
+    """errors is a subset of calls, so the UI can show "2 of 3 failed" honestly."""
+    tracker = MCPStatsTracker()
+    tracker.record(tool="t", duration_ms=1.0, success=True)
+    tracker.record(tool="t", duration_ms=1.0, success=False)
+    tracker.record(tool="t", duration_ms=1.0, success=False)
+
+    stats = tracker.snapshot()["t"]
+    assert stats.calls == 3
+    assert stats.errors == 2
+
+
+def test_last_called_at_is_set_and_timezone_aware():
+    """The panel renders this as a local time, which requires an aware datetime."""
+    tracker = MCPStatsTracker()
+    tracker.record(tool="t", duration_ms=1.0, success=True)
+
+    last_called = tracker.snapshot()["t"].last_called_at
+    assert last_called is not None
+    assert last_called.tzinfo is not None
+
+
+def test_concurrent_record_calls_are_not_lost():
+    """8 threads x 100 calls must total exactly 800.
+
+    Without the lock this loses increments non-deterministically. The mcp SDK
+    runs synchronous tool bodies via anyio.to_thread.run_sync, so this is the
+    real dispatch model, not a hypothetical.
+    """
+    tracker = MCPStatsTracker()
+
+    def hammer() -> None:
+        for _ in range(100):
+            tracker.record(tool="search_regulations", duration_ms=1.0, success=True)
+
+    threads = [threading.Thread(target=hammer) for _ in range(8)]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    stats = tracker.snapshot()["search_regulations"]
+    assert stats.calls == 800
+    assert stats.total_duration_ms == 800.0
+
+
+def test_record_swallows_bad_input_instead_of_raising():
+    """A malformed duration must not propagate into the caller's tool call."""
+    tracker = MCPStatsTracker()
+    tracker.record(tool="t", duration_ms="not-a-number", success=True)  # type: ignore[arg-type]
+
+    # Coercion happens before the lock is taken, so the entry is never created
+    # in a half-updated state.
+    assert tracker.snapshot() == {}
+
+
+def test_snapshot_is_a_copy_not_the_live_dict():
+    """Callers mutating the snapshot must not corrupt the tracker."""
+    tracker = MCPStatsTracker()
+    tracker.record(tool="t", duration_ms=1.0, success=True)
+
+    snapshot = tracker.snapshot()
+    snapshot.clear()
+
+    assert "t" in tracker.snapshot()
+
+
+def test_get_mcp_stats_tracker_returns_a_singleton():
+    """Instrumentation and the status route must observe the same counters."""
+    assert get_mcp_stats_tracker() is get_mcp_stats_tracker()
@@ -0,0 +1,79 @@
+"""Tests for get_mcp_status(), the payload behind the System Status MCP card.
+
+Covers the join between the live tool registry and the stats tracker, plus
+the two values the route supplies or the settings decide.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from unittest.mock import patch
+
+from app.mcp.stats import MCPStatsTracker
+
+
+def _status(tracker: MCPStatsTracker | None = None, **setting_overrides) -> dict:
+    """Call get_mcp_status() with an isolated tracker and patched settings.
+
+    The real tracker is a process-wide singleton, so tests must inject their
+    own instance or they leak counters into each other.
+    """
+    from app.mcp.server import get_mcp_status, settings
+
+    patched = settings.model_copy(update=setting_overrides)
+    with (
+        patch("app.mcp.server.settings", patched),
+        patch("app.mcp.server.get_mcp_stats_tracker", return_value=tracker or MCPStatsTracker()),
+    ):
+        return asyncio.run(get_mcp_status("http://6.86.80.9:8000/mcp/"))
+
+
+def test_public_url_is_passed_through_unmodified():
+    """The route owns URL resolution; get_mcp_status() must not rewrite it."""
+    assert _status()["endpoint_url"] == "http://6.86.80.9:8000/mcp/"
+
+
+def test_auth_required_follows_settings():
+    """The panel's auth badge must reflect live config, not a hard-coded value."""
+    assert _status(auth_enabled=True)["auth_required"] is True
+    assert _status(auth_enabled=False)["auth_required"] is False
+
+
+def test_allowed_hosts_are_split_and_stripped():
+    """Displayed allow-list must match the one the transport actually enforces."""
+    status = _status(mcp_allowed_hosts="6.86.80.9:* , 127.0.0.1:*,")
+    assert status["allowed_hosts"] == ["6.86.80.9:*", "127.0.0.1:*"]
+
+
+def test_tools_come_from_the_live_registry_with_zeroed_stats():
+    """An advertised but never-called tool reports zeros, not absence."""
+    tools = {tool["name"]: tool for tool in _status()["tools"]}
+
+    assert "search_regulations" in tools
+    assert tools["search_regulations"]["calls"] == 0
+    assert tools["search_regulations"]["errors"] == 0
+    assert tools["search_regulations"]["avg_duration_ms"] is None
+    assert tools["search_regulations"]["last_called_at"] is None
+
+
+def test_recorded_stats_are_joined_onto_the_matching_tool():
+    """Counters recorded by the instrumented tool must surface on that tool's row."""
+    tracker = MCPStatsTracker()
+    tracker.record(tool="search_regulations", duration_ms=120.0, success=True)
+    tracker.record(tool="search_regulations", duration_ms=80.0, success=False)
+
+    tool = next(t for t in _status(tracker)["tools"] if t["name"] == "search_regulations")
+
+    assert tool["calls"] == 2
+    assert tool["errors"] == 1
+    assert tool["avg_duration_ms"] == 100.0
+    # Serialized for JSON transport; the frontend parses it with new Date().
+    assert isinstance(tool["last_called_at"], str)
+
+
+def test_description_is_the_first_docstring_line():
+    """Multi-line tool docstrings must not blow up the card's row height."""
+    tool = next(t for t in _status()["tools"] if t["name"] == "search_regulations")
+
+    assert "\n" not in tool["description"]
+    assert tool["description"].startswith("Search the compliance knowledge base")
@@ -0,0 +1,107 @@
+"""Tests for the MCP endpoint's DNS-rebinding (Host header) protection.
+
+The MCP SDK auto-enables DNS-rebinding protection and derives its allow-list
+from the bind host, which defaults to 127.0.0.1. Left alone, that rejects every
+request whose Host header is the real deployment address (6.86.80.9:8000) with
+HTTP 421 — before the auth middleware or the tool ever runs. These tests pin
+the configured allow-list behavior so that failure mode cannot come back.
+"""
+
+from __future__ import annotations
+
+import json
+from contextlib import contextmanager
+from unittest.mock import patch
+
+from starlette.testclient import TestClient
+
+from app.mcp.server import _build_transport_security, build_mcp_asgi_app
+
+# A minimal JSON-RPC initialize call. Reaching the MCP handler at all is what
+# matters here; transport security rejects the request long before this body is
+# parsed, so its exact contents only need to be structurally valid.
+_INITIALIZE = {
+    "jsonrpc": "2.0",
+    "id": 1,
+    "method": "initialize",
+    "params": {
+        "protocolVersion": "2025-06-18",
+        "capabilities": {},
+        "clientInfo": {"name": "test", "version": "1.0"},
+    },
+}
+
+_HEADERS = {
+    "Content-Type": "application/json",
+    "Accept": "application/json, text/event-stream",
+}
+
+
+@contextmanager
+def _mcp_client(allowed_hosts: str):
+    """Yield a TestClient over the real MCP app with auth off and hosts configured.
+
+    The settings patch must stay active for the requests themselves, not just
+    for app construction, because MCPAuthMiddleware reads settings per request.
+    Entering the TestClient as a context manager is also required: it runs the
+    app's lifespan, without which the SDK's session manager task group is never
+    initialized and every request raises RuntimeError.
+    """
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.mcp_allowed_hosts = allowed_hosts
+        fake_settings.cors_allow_origins = "http://localhost:5173"
+        fake_settings.auth_enabled = False
+        with TestClient(build_mcp_asgi_app()) as client:
+            yield client
+
+
+def test_remote_host_allowed_when_configured():
+    """A configured non-loopback Host must reach the MCP handler, not 421."""
+    with _mcp_client("6.86.80.9:*,127.0.0.1:*") as client:
+        response = client.post(
+            "/", json=_INITIALIZE, headers={**_HEADERS, "Host": "6.86.80.9:8000"}
+        )
+    assert response.status_code == 200
+    assert "Invalid Host header" not in response.text
+
+
+def test_unconfigured_host_still_rejected():
+    """Protection must stay on: a Host outside the allow-list is refused with 421."""
+    with _mcp_client("6.86.80.9:*") as client:
+        response = client.post(
+            "/", json=_INITIALIZE, headers={**_HEADERS, "Host": "evil.example.com"}
+        )
+    assert response.status_code == 421
+
+
+def test_initialize_response_is_event_stream():
+    """Sanity check that a permitted request really completes the MCP handshake."""
+    with _mcp_client("6.86.80.9:*") as client:
+        response = client.post(
+            "/", json=_INITIALIZE, headers={**_HEADERS, "Host": "6.86.80.9:8000"}
+        )
+    assert response.status_code == 200
+    # The Streamable HTTP transport replies as SSE; the JSON-RPC result is
+    # embedded in a "data:" line rather than being the whole body.
+    payload = json.loads(response.text.split("data:", 1)[1].strip())
+    assert payload["result"]["serverInfo"]["name"] == "ai-regulations"
+
+
+def test_wildcard_disables_protection_explicitly():
+    """'*' is the documented opt-out; it must disable the check, not allow-list '*'."""
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.mcp_allowed_hosts = "*"
+        fake_settings.cors_allow_origins = "http://localhost:5173"
+        security = _build_transport_security()
+    assert security.enable_dns_rebinding_protection is False
+
+
+def test_allow_list_is_parsed_into_transport_settings():
+    """Comma-separated config must become the SDK's allowed_hosts list verbatim."""
+    with patch("app.mcp.server.settings") as fake_settings:
+        fake_settings.mcp_allowed_hosts = "6.86.80.9:*, localhost:* ,"
+        fake_settings.cors_allow_origins = "http://localhost:5173"
+        security = _build_transport_security()
+    assert security.enable_dns_rebinding_protection is True
+    assert security.allowed_hosts == ["6.86.80.9:*", "localhost:*"]
+    assert security.allowed_origins == ["http://localhost:5173"]
@@ -0,0 +1,98 @@
+"""Unit tests for the search_regulations MCP tool function.
+
+Mocks AgentConversationService so no real retrieval/LLM call happens —
+verifies only the protocol-adapter contract: correct call shape in,
+correct dict shape out.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from dataclasses import dataclass
+from unittest.mock import MagicMock, patch
+
+
+@dataclass
+class _FakeSource:
+    """Minimal stand-in for a real Source dataclass (only __dict__ is used)."""
+
+    # A dataclass, not a MagicMock: the adapter serializes sources via
+    # source.__dict__, and a MagicMock's __dict__ is full of internal mock
+    # attributes, which would make the assertions meaningless.
+    doc_id: str
+    doc_title: str
+    score: float
+
+
+@dataclass
+class _FakeAnswerResult:
+    """Minimal stand-in for AnswerResult — only .answer/.sources are read."""
+
+    answer: str
+    sources: list
+
+
+def test_search_regulations_calls_agent_ask_without_session():
+    """search_regulations must call ask() with no session_id (stateless search)."""
+    from app.mcp.server import search_regulations
+
+    fake_service = MagicMock()
+    fake_service.ask.return_value = (
+        None,
+        _FakeAnswerResult(answer="国六排放标准要求...", sources=[_FakeSource("doc-1", "国六标准", 0.9)]),
+    )
+
+    with patch("app.mcp.server.get_agent_conversation_service", return_value=fake_service):
+        search_regulations(query="国六排放标准最新要求", top_k=3)
+
+    fake_service.ask.assert_called_once_with(query="国六排放标准最新要求", top_k=3)
+    assert "session_id" not in fake_service.ask.call_args.kwargs
+
+
+def test_search_regulations_shapes_response_dict():
+    """The returned dict must expose 'answer' and 'sources' (list of plain dicts)."""
+    from app.mcp.server import search_regulations
+
+    fake_service = MagicMock()
+    fake_service.ask.return_value = (
+        None,
+        _FakeAnswerResult(answer="答案文本", sources=[_FakeSource("doc-2", "国标GB1589", 0.8)]),
+    )
+
+    with patch("app.mcp.server.get_agent_conversation_service", return_value=fake_service):
+        result = search_regulations(query="q")
+
+    assert result == {
+        "answer": "答案文本",
+        "sources": [{"doc_id": "doc-2", "doc_title": "国标GB1589", "score": 0.8}],
+    }
+
+
+def test_search_regulations_default_top_k():
+    """top_k defaults to 5 when the caller omits it."""
+    from app.mcp.server import search_regulations
+
+    fake_service = MagicMock()
+    fake_service.ask.return_value = (None, _FakeAnswerResult(answer="a", sources=[]))
+
+    with patch("app.mcp.server.get_agent_conversation_service", return_value=fake_service):
+        search_regulations(query="q")
+
+    assert fake_service.ask.call_args.kwargs["top_k"] == 5
+
+
+def test_advertised_schema_bounds_top_k_and_query():
+    """The advertised JSON schema must carry the same bounds as AskRequest.
+
+    Bounds declared via Annotated are what the SDK validates against and what
+    clients see, so asserting on the generated schema is the only way to catch
+    a regression that silently drops them.
+    """
+    from app.mcp.server import mcp
+
+    schema = asyncio.run(mcp.list_tools())[0].input_schema["properties"]
+
+    assert schema["top_k"]["minimum"] == 1
+    assert schema["top_k"]["maximum"] == 20
+    assert schema["query"]["minLength"] == 1
+    assert schema["query"]["maxLength"] == 2000
@@ -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
@@ -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
@@ -0,0 +1,104 @@
+"""Unit tests for the model-usage persistence wiring in app.shared.bootstrap.
+
+get_model_usage_store()'s settings-gating is tested the same way
+tests/test_reranker_bootstrap.py tests get_reranker() — by patching
+"app.shared.bootstrap.settings" wholesale, matching this codebase's
+established convention for testing @lru_cache settings-gated factories.
+The remaining tests isolate _start_model_usage_persistence() /
+_stop_model_usage_persistence() from get_model_usage_store() entirely (via
+monkeypatch on the module-level function), so no real database or event loop
+is needed anywhere in this file — asyncio.create_task itself is also mocked.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+# psycopg2 is mocked centrally in backend/tests/conftest.py, which pytest
+# imports before any test module regardless of collection order.
+from app.shared import bootstrap
+from app.shared.model_usage_tracker import ModelUsageEntry, ModelUsageTracker
+
+
+def test_get_model_usage_store_returns_none_when_not_postgres_backend():
+    """get_model_usage_store() must be None unless document_repository_backend == 'postgres'."""
+    bootstrap.get_model_usage_store.cache_clear()
+
+    with patch("app.shared.bootstrap.settings") as mock_settings:
+        mock_settings.document_repository_backend = "json"
+        result = bootstrap.get_model_usage_store()
+
+    bootstrap.get_model_usage_store.cache_clear()
+    assert result is None
+
+
+def test_get_model_usage_store_returns_instance_when_postgres_backend():
+    """get_model_usage_store() must return a PostgresModelUsageStore when enabled.
+
+    ThreadedConnectionPool is mocked so no real connection is attempted; the
+    postgres_host/port/user/password/db values PostgresModelUsageStore reads
+    come from app.config.settings.settings directly (not from the
+    app.shared.bootstrap.settings reference mocked below), so they don't need
+    to be set here — only document_repository_backend gates this factory.
+    """
+    bootstrap.get_model_usage_store.cache_clear()
+
+    with patch("psycopg2.pool.ThreadedConnectionPool"), \
+         patch(
+             "app.infrastructure.storage.postgres_model_usage_store.PostgresModelUsageStore._ensure_schema"
+         ), \
+         patch("app.shared.bootstrap.settings") as mock_settings:
+        mock_settings.document_repository_backend = "postgres"
+        result = bootstrap.get_model_usage_store()
+
+    bootstrap.get_model_usage_store.cache_clear()
+    from app.infrastructure.storage.postgres_model_usage_store import PostgresModelUsageStore
+    assert isinstance(result, PostgresModelUsageStore)
+
+
+def test_start_model_usage_persistence_seeds_tracker_and_starts_flush_loop(monkeypatch):
+    """When a store is available, startup must seed the tracker and schedule the flush task."""
+    fake_store = MagicMock()
+    fake_store.load_all.return_value = {
+        "deepseek:deepseek-v4-flash": ModelUsageEntry(
+            provider="deepseek", model="deepseek-v4-flash", total_tokens=99,
+        ),
+    }
+    tracker = ModelUsageTracker()
+    monkeypatch.setattr(bootstrap, "get_model_usage_store", lambda: fake_store)
+    monkeypatch.setattr(bootstrap, "get_model_usage_tracker", lambda: tracker)
+
+    with patch("asyncio.create_task") as mock_create_task:
+        bootstrap._start_model_usage_persistence()
+        # Close the coroutine object passed to the mock so pytest doesn't warn
+        # about "coroutine was never awaited" — it was never meant to run here.
+        mock_create_task.call_args[0][0].close()
+
+    assert tracker.get("deepseek", "deepseek-v4-flash").total_tokens == 99
+    mock_create_task.assert_called_once()
+
+    bootstrap._stop_model_usage_persistence()  # reset the module-level task handle
+
+
+def test_start_model_usage_persistence_is_a_no_op_without_a_store(monkeypatch):
+    """No store configured (json backend) — startup must not touch asyncio or the tracker."""
+    monkeypatch.setattr(bootstrap, "get_model_usage_store", lambda: None)
+
+    with patch("asyncio.create_task") as mock_create_task:
+        bootstrap._start_model_usage_persistence()
+
+    mock_create_task.assert_not_called()
+
+
+def test_stop_model_usage_persistence_cancels_task_and_flushes(monkeypatch):
+    """Shutdown must cancel the running flush task and perform one final flush."""
+    fake_store = MagicMock()
+    monkeypatch.setattr(bootstrap, "get_model_usage_store", lambda: fake_store)
+    fake_task = MagicMock()
+    bootstrap._model_usage_flush_task = fake_task
+
+    bootstrap._stop_model_usage_persistence()
+
+    fake_task.cancel.assert_called_once()
+    fake_store.flush.assert_called_once()
+    assert bootstrap._model_usage_flush_task is None
@@ -0,0 +1,102 @@
+"""Unit tests for PostgresModelUsageStore, using a mocked psycopg2 pool.
+
+Mirrors the mocking pattern in backend/tests/perception/test_postgres_event_store.py
+— no real database is needed.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, patch
+
+# psycopg2 is mocked centrally in backend/tests/conftest.py, so importing the
+# module under test here never binds the real driver.
+from app.shared.model_usage_tracker import ModelUsageEntry
+
+
+def _cursor_returning(rows):
+    """Build a MagicMock standing in for a psycopg2 cursor context manager."""
+    cursor = MagicMock()
+    cursor.__enter__ = lambda s: s
+    cursor.__exit__ = MagicMock(return_value=False)
+    cursor.fetchall.return_value = rows
+    return cursor
+
+
+@patch("app.infrastructure.storage.postgres_model_usage_store.PostgresModelUsageStore._ensure_schema")
+@patch("app.infrastructure.storage.postgres_model_usage_store.ThreadedConnectionPool")
+def test_load_all_returns_entries_keyed_by_provider_model(mock_pool_class, mock_ensure):
+    """load_all() must turn each row into a ModelUsageEntry keyed by 'provider:model'."""
+    row = {
+        "provider": "deepseek",
+        "model": "deepseek-v4-flash",
+        "total_tokens": 100,
+        "prompt_tokens": 60,
+        "completion_tokens": 40,
+        "call_count_ok": 5,
+        "call_count_error": 1,
+        "last_called_at": datetime(2026, 7, 23, tzinfo=timezone.utc),
+        "last_latency_ms": 250,
+        "last_error": None,
+    }
+    mock_pool = MagicMock()
+    mock_pool_class.return_value = mock_pool
+    conn = MagicMock()
+    conn.__enter__ = lambda s: s
+    conn.__exit__ = MagicMock(return_value=False)
+    conn.cursor.return_value = _cursor_returning([row])
+    mock_pool.getconn.return_value = conn
+
+    from app.infrastructure.storage.postgres_model_usage_store import PostgresModelUsageStore
+    store = PostgresModelUsageStore()
+    entries = store.load_all()
+
+    assert "deepseek:deepseek-v4-flash" in entries
+    entry = entries["deepseek:deepseek-v4-flash"]
+    assert isinstance(entry, ModelUsageEntry)
+    assert entry.total_tokens == 100
+    assert entry.call_count_error == 1
+
+
+@patch("app.infrastructure.storage.postgres_model_usage_store.PostgresModelUsageStore._ensure_schema")
+@patch("app.infrastructure.storage.postgres_model_usage_store.ThreadedConnectionPool")
+def test_flush_upserts_every_entry(mock_pool_class, mock_ensure):
+    """flush() must execute one UPSERT per tracked entry and commit once."""
+    mock_pool = MagicMock()
+    mock_pool_class.return_value = mock_pool
+    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)
+    conn.cursor.return_value = cursor
+    mock_pool.getconn.return_value = conn
+
+    from app.infrastructure.storage.postgres_model_usage_store import PostgresModelUsageStore
+    store = PostgresModelUsageStore()
+    entries = {
+        "deepseek:deepseek-v4-flash": ModelUsageEntry(
+            provider="deepseek", model="deepseek-v4-flash", total_tokens=100, call_count_ok=5,
+        ),
+    }
+
+    store.flush(entries)
+
+    assert cursor.execute.call_count == 1
+    conn.commit.assert_called_once()
+
+
+@patch("app.infrastructure.storage.postgres_model_usage_store.PostgresModelUsageStore._ensure_schema")
+@patch("app.infrastructure.storage.postgres_model_usage_store.ThreadedConnectionPool")
+def test_flush_with_no_entries_does_not_touch_the_database(mock_pool_class, mock_ensure):
+    """flush({}) must be a no-op — no point opening a connection for nothing."""
+    mock_pool = MagicMock()
+    mock_pool_class.return_value = mock_pool
+
+    from app.infrastructure.storage.postgres_model_usage_store import PostgresModelUsageStore
+    store = PostgresModelUsageStore()
+
+    store.flush({})
+
+    mock_pool.getconn.assert_not_called()
@@ -0,0 +1,109 @@
+"""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()
+
+
+def test_seed_populates_registry_from_persisted_entries():
+    """seed() must bulk-load entries (e.g. from Postgres at startup) into the registry."""
+    tracker = ModelUsageTracker()
+    persisted = {
+        "deepseek:deepseek-v4-flash": ModelUsageEntry(
+            provider="deepseek", model="deepseek-v4-flash", total_tokens=500, call_count_ok=20,
+        ),
+    }
+
+    tracker.seed(persisted)
+
+    entry = tracker.get("deepseek", "deepseek-v4-flash")
+    assert entry.total_tokens == 500
+    assert entry.call_count_ok == 20
+
+
+def test_seed_then_record_accumulates_on_top_of_seeded_value():
+    """A call recorded after seeding must add to the seeded total, not replace it."""
+    tracker = ModelUsageTracker()
+    tracker.seed({
+        "deepseek:deepseek-v4-flash": ModelUsageEntry(
+            provider="deepseek", model="deepseek-v4-flash", total_tokens=500,
+        ),
+    })
+
+    tracker.record(provider="deepseek", model="deepseek-v4-flash", success=True, usage={"total_tokens": 10})
+
+    assert tracker.get("deepseek", "deepseek-v4-flash").total_tokens == 510
@@ -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"
@@ -0,0 +1,116 @@
+"""Unit tests verifying stream_chat() captures a trailing usage-only SSE chunk.
+
+Exercises DeepSeekClient, QwenClient, and QwenVLClient directly (not through
+TrackedLLMClient) by mocking the underlying httpx.Client.stream() call — none
+of these tests make a real network call.
+"""
+
+from __future__ import annotations
+
+import json
+from unittest.mock import MagicMock
+
+from app.services.llm.base_client import LLMConfig, LLMProvider
+from app.services.llm.deepseek_client import DeepSeekClient
+
+
+def _sse_lines(*chunks: str, usage: dict | None = None) -> list[str]:
+    """Build raw SSE 'data: ...' lines the way an OpenAI-compatible gateway sends them."""
+    lines = [
+        f'data: {json.dumps({"choices": [{"delta": {"content": c}}]})}'
+        for c in chunks
+    ]
+    if usage is not None:
+        # Trailing usage-only chunk, as sent when stream_options.include_usage=true.
+        lines.append(f'data: {json.dumps({"choices": [], "usage": usage})}')
+    lines.append("data: [DONE]")
+    return lines
+
+
+def _mock_streaming_client(lines: list[str]) -> MagicMock:
+    """Build a MagicMock standing in for httpx.Client, configured for .stream()."""
+    fake_response = MagicMock()
+    fake_response.raise_for_status.return_value = None
+    fake_response.iter_lines.return_value = lines
+
+    stream_cm = MagicMock()
+    stream_cm.__enter__.return_value = fake_response
+    stream_cm.__exit__.return_value = False
+
+    client = MagicMock()
+    client.stream.return_value = stream_cm
+    return client
+
+
+def _drain(gen):
+    """Manually drive a generator, returning (yielded_chunks, stop_iteration_value)."""
+    chunks = []
+    value = None
+    while True:
+        try:
+            chunks.append(next(gen))
+        except StopIteration as stop:
+            value = stop.value
+            break
+    return chunks, value
+
+
+def test_deepseek_stream_chat_returns_usage_from_trailing_chunk():
+    """DeepSeekClient.stream_chat() must return the trailing usage dict."""
+    config = LLMConfig(provider=LLMProvider.DEEPSEEK, model="deepseek-v4-flash", api_key="k", base_url="http://x/v1")
+    client = DeepSeekClient(config)
+    usage = {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8}
+    client._client = _mock_streaming_client(_sse_lines("Hello", " world", usage=usage))
+
+    chunks, returned_usage = _drain(client.stream_chat([{"role": "user", "content": "hi"}]))
+
+    assert chunks == ["Hello", " world"]
+    assert returned_usage == usage
+    # The gateway must actually be asked to include usage in the stream.
+    sent_payload = client._client.stream.call_args.kwargs["json"]
+    assert sent_payload["stream_options"] == {"include_usage": True}
+
+
+def test_deepseek_stream_chat_without_usage_chunk_returns_none():
+    """If the gateway never sends a usage chunk, the generator returns None (unchanged behavior)."""
+    config = LLMConfig(provider=LLMProvider.DEEPSEEK, model="deepseek-v4-flash", api_key="k", base_url="http://x/v1")
+    client = DeepSeekClient(config)
+    client._client = _mock_streaming_client(_sse_lines("Hi"))
+
+    chunks, returned_usage = _drain(client.stream_chat([{"role": "user", "content": "hi"}]))
+
+    assert chunks == ["Hi"]
+    assert returned_usage is None
+
+
+from app.services.llm.qwen_client import QwenClient, QwenVLClient
+
+
+def test_qwen_stream_chat_returns_usage_from_trailing_chunk():
+    """QwenClient.stream_chat() must return the trailing usage dict."""
+    config = LLMConfig(provider=LLMProvider.QWEN, model="qwen3.5-flash", api_key="k", base_url="http://x/v1")
+    client = QwenClient(config)
+    usage = {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14}
+    client._client = _mock_streaming_client(_sse_lines("Bonjour", usage=usage))
+
+    chunks, returned_usage = _drain(client.stream_chat([{"role": "user", "content": "hi"}]))
+
+    assert chunks == ["Bonjour"]
+    assert returned_usage == usage
+    sent_payload = client._client.stream.call_args.kwargs["json"]
+    assert sent_payload["stream_options"] == {"include_usage": True}
+
+
+def test_qwen_vl_stream_chat_returns_usage_from_trailing_chunk():
+    """QwenVLClient.stream_chat() must return the trailing usage dict."""
+    config = LLMConfig(provider=LLMProvider.QWEN_VL, model="qwen3-vl-plus", api_key="k", base_url="http://x/v1")
+    client = QwenVLClient(config)
+    usage = {"prompt_tokens": 20, "completion_tokens": 6, "total_tokens": 26}
+    client._client = _mock_streaming_client(_sse_lines("Describing image", usage=usage))
+
+    chunks, returned_usage = _drain(client.stream_chat([{"role": "user", "content": "describe"}]))
+
+    assert chunks == ["Describing image"]
+    assert returned_usage == usage
+    sent_payload = client._client.stream.call_args.kwargs["json"]
+    assert sent_payload["stream_options"] == {"include_usage": True}
@@ -0,0 +1,105 @@
+"""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
+
+
+def test_stream_chat_records_usage_from_generator_return_value():
+    """stream_chat() must forward the inner generator's returned usage dict to record()."""
+    inner = _make_inner()
+
+    def fake_stream(*args, **kwargs):
+        yield "chunk-1"
+        yield "chunk-2"
+        return {"prompt_tokens": 6, "completion_tokens": 2, "total_tokens": 8}
+
+    inner.stream_chat.side_effect = fake_stream
+    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.total_tokens == 8
+    assert entry.call_count_ok == 1
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
wangwei	b2feaeddb4	update for mcp	2026-08-06 11:08:46 +08:00
wangweiandCopilot	31bbf80aeb	feat: surface MCP server status in System Status page Add per-tool in-memory call counters to the MCP module and a GET /api/v1/status/mcp endpoint that joins them with the live tool registry and endpoint config, then render it as a new card on the System Status page with a one-click client-config copy button. - app/mcp/stats.py: lock-guarded MCPStatsTracker (the mcp SDK runs sync tool bodies via anyio.to_thread.run_sync, so this is genuinely multi-threaded, unlike the async REST routes) - app/mcp/server.py: instrument search_regulations, add get_mcp_status() - app/config/settings.py: optional MCP_PUBLIC_URL override, required because the Vite proxy and reverse proxies rewrite the Host header - StatusPage.tsx: MCP Server card, joins the existing parallel fetch Counters are process-local by design; token usage is already persisted by ModelUsageTracker since MCP calls route through ask(). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-08-03 11:37:22 +08:00
wangweiandCopilot	73e79a610d	docs: design spec for MCP status panel Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-08-03 10:56:56 +08:00
wangweiandCopilot	49ee50c104	fix: harden MCP endpoint after code review Critical: the MCP SDK auto-enables DNS-rebinding protection when its host parameter is left at the 127.0.0.1 default, hard-coding a loopback-only Host allow-list. Every remote client (the only deployment this feature targets) was refused with HTTP 421 before auth or the tool ran. Now driven by a new MCP_ALLOWED_HOSTS setting, with '*' as an explicit, logged opt-out. Also bounds query/top_k to match AskRequest (top_k is amplified 4x downstream, so an unbounded value was a resource-exhaustion vector), decodes the Authorization header as latin-1 per the ASGI spec instead of raising a 500 on malformed bytes, and returns WWW-Authenticate on 401 per RFC 7235. Moves the psycopg2 import guard into backend/tests/conftest.py: duplicated across four test modules, it only worked because of alphabetical collection order, and any earlier-sorting package would have reintroduced a live connection attempt against the production database. Registers the mcp module in the authoritative backend architecture doc. 84 backend tests pass. Verified against a live server: allowed remote Host returns a valid initialize result, unknown Host returns 421, missing token returns 401 with WWW-Authenticate. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-29 17:11:54 +08:00
wangweiandCopilot	bd3dc38d1d	feat: add MCP server module exposing search_regulations tool - New backend/app/mcp/ module: MCPServer instance with a single search_regulations tool backed by the existing AgentConversationService. - MCPAuthMiddleware reuses existing JWT auth (no new auth mechanism). - Mounted at /mcp/ in api/main.py via Streamable HTTP transport; wired the MCP session manager into the existing lifespan() via AsyncExitStack (app.mount() does not propagate nested ASGI lifespans automatically). - Fixed a doubled /mcp/mcp path by setting streamable_http_path to "/" (MCPServer.streamable_http_app() defaults to registering its own /mcp route). - Verified end-to-end with the real mcp Python client: list_tools() returns search_regulations, auth correctly 401s without or with an invalid token. - 7 new tests, 76 total (up from 69), all passing. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-29 13:00:52 +08:00
wangweiandCopilot	e78c8a989f	docs: add MCP search_regulations implementation plan Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-29 11:03:28 +08:00
wangweiandCopilot	483689c1e8	docs: add MCP search_regulations server design spec Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-29 10:58:01 +08:00
wangweiandCopilot	6aaaff05f5	docs: add implementation plan for status model usage hardening Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 15:31:19 +08:00
wangweiandCopilot	0907470a2d	fix: offload flush to thread pool, add QwenVL stream_options assert, document single-worker assumption Fix 1 (bootstrap.py): wrap store.flush() in asyncio.to_thread() inside the periodic _flush_loop() to avoid blocking the async event loop every 60s. Synchronous signatures of _start/_stop_model_usage_persistence() and the one-time seed/shutdown flushes are left unchanged per review scope. Fix 2 (test_stream_chat_usage_capture.py): add the two-line stream_options assertion to test_qwen_vl_stream_chat_returns_usage_from_trailing_chunk, matching the identical check already present in the DeepSeek and Qwen tests. Fix 3 (design doc): note the single-worker assumption in A3's write strategy section — multi-worker deployments get last-writer-wins per-row semantics. Tests: 69 passed, 0 failed (python -m pytest backend/tests -q) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 15:30:21 +08:00
wangweiandCopilot	29f79d7434	feat: seed and periodically persist model usage stats to Postgres Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 14:32:21 +08:00
wangweiandCopilot	5d132981ad	feat: add PostgresModelUsageStore and ModelUsageTracker.seed() - Add seed() method to ModelUsageTracker for bulk-loading persisted entries at startup - Create PostgresModelUsageStore for persistence of model usage counters to Postgres - Store only current cumulative snapshots (no historical time-series) - Use standard CREATE TABLE IF NOT EXISTS idiom matching other Postgres stores - Add comprehensive mocked unit tests for both components Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 14:18:22 +08:00
wangweiandCopilot	4f6cc4812e	revert: disable Cross-Encoder reranker Live verification via POST /status/models/ping (after confirming the gateway itself is reachable -- embedding role succeeded, 1.5s latency) shows http://6.86.80.4:30080/v1/rerank returns a fast, reproducible '503 Service Unavailable' -- not a timeout/fluke. The gateway's model catalog (19 models: deepseek-, glm-, kimi-, qwen3, text-embedding-v3/4) contains no cross-encoder/rerank-capable model, confirming no reranker service is deployed behind this gateway today. Leaving RERANKER_ENABLED=true would be a permanent no-op (graceful fallback to unranked order every call) plus a misleading permanent error badge on the Status page. Revert until a reranker model is actually deployed on the gateway. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 14:09:33 +08:00
wangweiandCopilot	2547d04b9d	chore: enable Cross-Encoder reranker Sandbox verification via /status/models/ping was inconclusive: the gateway (6.86.80.4:30080) is unreachable from this environment entirely (embedding role failed with the identical connection-timeout pattern, which is a feature that definitely works in real deployment) -- not evidence that /rerank specifically is unsupported. Reranker code already has graceful TEI/Cohere fallback + falls back to unranked order on any failure, so this is zero-risk to retrieval even if misconfigured. Please verify via POST /status/models/ping in an environment with real gateway access; revert RERANKER_ENABLED to false if that shows a genuine error. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 14:03:43 +08:00
wangweiandCopilot	7adc050968	feat: record streaming token usage in TrackedLLMClient.stream_chat Implement manual generator driving using next()/StopIteration to capture the return value (trailing usage dict) from inner stream_chat() implementations, enabling token tracking for streaming LLM calls. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 13:42:33 +08:00
wangweiandCopilot	f2bd0deeb3	feat: capture streaming token usage in QwenClient and QwenVLClient Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 13:19:23 +08:00
wangweiandCopilot	81a6d54fff	feat: capture streaming token usage in DeepSeekClient.stream_chat Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-23 11:24:31 +08:00
wangwei	beddc2d976	Merge pull request 'main-ruqi' (#1 ) from main-ruqi into main Reviewed-on: #1	2026-07-02 22:05:17 +08:00
wangwei	52e67b0e7b	Add LLM token	2026-07-02 22:03:39 +08:00
wangweiandCopilot	e3afb8a07a	fix: normalize LLM provider key lookup and skip disabled HyDE ping (final review) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 21:24:22 +08:00
wangweiandCopilot	6a7fe48c4c	fix: use dedicated error message for AI Models card load failure (Task 9 review) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 20:03:27 +08:00
wangweiandCopilot	0edbee07d5	fix: add distinct error state for AI Models card load failure (Task 9 review) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 18:01:04 +08:00
wangweiandCopilot	2ce4c8a289	feat: add AI Models card to Status page with connection test button Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 17:38:33 +08:00
wangweiandCopilot	39a51c9e83	feat: add ModelUsageEntry types and status API client functions Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 17:23:43 +08:00
wangweiandCopilot	049da2297b	feat: add i18n keys for AI Models card and fix missing .status.error CSS Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 17:06:13 +08:00
wangweiandCopilot	d83286edd4	fix: honor hyde_enabled toggle and record ping failures before client creation (Task 6 review) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 16:42:58 +08:00
wangweiandCopilot	169911ab46	feat: add GET/POST /status/models routes for AI model connection status Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 16:20:35 +08:00
wangweiandCopilot	66fc388bfb	feat: record reranker call outcome into ModelUsageTracker Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 15:42:20 +08:00
wangwei	41096369d3	feat: record embedding call usage into ModelUsageTracker	2026-07-02 15:15:03 +08:00
wangwei	4fea159f5b	feat: wrap LLM clients with TrackedLLMClient in LLMFactory	2026-07-02 15:03:12 +08:00
wangweiandCopilot	d460397dda	fix: add missing test comment for backend commenting standard (Task 2 review) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 14:57:55 +08:00
wangweiandCopilot	37ea27fcbe	feat: add TrackedLLMClient decorator for transparent usage recording Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 14:49:06 +08:00
wangwei	74f327c85e	feat: add ModelUsageTracker for per-model token/connection tracking	2026-07-02 14:41:21 +08:00
wangweiandCopilot	4b451ef97c	docs: add implementation plan for System Status AI model usage tracking Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 14:13:58 +08:00
wangweiandCopilot	55ba922250	docs: add design spec for System Status AI model connection/token usage panel Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-07-02 13:39:43 +08:00
wangwei	9212747e1b	update for 1. 优化 2.中英切换	2026-06-10 11:10:36 +08:00
wangwei	e7963b267e	fix somethings	2026-06-08 11:16:28 +08:00
wangwei	9fea9c6a53	1. Add 登陆功能 2. 调整字体大小 3. 新增部分功能	2026-06-05 18:00:31 +08:00
				`@@ -0,0 +1 @@`
				`{"reason":"idle timeout","timestamp":1780894411095}`
				`@@ -0,0 +1 @@`
				`"""HTTP middleware for cross-cutting concerns: audit logging."""`