Refactor code structure for improved readability and maintainability

.env

@@ -1,5 +1,15 @@
-# DashScope API
-DASHSCOPE_API_KEY=your_api_key_here
+# ===== Qwen API配置（阿里云DashScope）=====
+# 获取API Key: https://dashscope.console.aliyun.com/
+QWEN_API_KEY=sk-MYNyhzr03f1AjF4QcFgrmKL1kJm930smNK98BB9ecDqkDaa3
+QWEN_BASE_URL=https://new-api.fletcher0516.online/v1
+QWEN_MODEL=qwen3.5-plus
+QWEN_VL_MODEL=qwen3-vl-plus
+
+# ===== DeepSeek API配置 =====
+# 获取API Key: https://platform.deepseek.com/
+DEEPSEEK_API_KEY=sk-MYNyhzr03f1AjF4QcFgrmKL1kJm930smNK98BB9ecDqkDaa3
+DEEPSEEK_BASE_URL=https://new-api.fletcher0516.online/v1
+DEEPSEEK_MODEL=deepseek-v3.2
 
 # PostgreSQL
 POSTGRES_HOST=localhost

.gitignore

@@ -5,6 +5,51 @@ build/
 dist/
 wheels/
 *.egg-info
+*.egg
+*.manifest
+*.spec
+pip-log.txt
+pip-delete-this-directory.txt
 
 # Virtual environments
 .venv
+venv/
+ENV/
+env/
+
+# Tests
+tests/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Environment variables
+.env
+.env.local
+.env.*.local
+
+# Logs
+*.log
+logs/
+
+# Database
+*.db
+*.sqlite3
+
+# Cache
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.coverage
+htmlcov/
+
+# Jupyter
+.ipynb_checkpoints/

.python-version

@@ -1 +1 @@
-3.9
+3.13

README.md

@@ -1,68 +1,417 @@
-# 车辆法规智能检索系统 - 后端
+# 车辆法规智能检索系统 - 后端 API
 
-基于 FastAPI + LangGraph + Milvus + 千问 的法规检索与合规分析后端。
+基于 FastAPI + LangGraph + Milvus + 千问大模型 的法规检索与合规分析后端服务。
+
+## 目录
+
+- [技术栈](#技术栈)
+- [服务依赖](#服务依赖)
+- [快速开始](#快速开始)
+- [环境配置](#环境配置)
+- [API 接口文档](#api-接口文档)
+- [项目结构](#项目结构)
+- [核心模块详解](#核心模块详解)
+- [工作流设计](#工作流设计)
+- [数据模型](#数据模型)
 
 ## 技术栈
 
-- **Web框架**: FastAPI
-- **AI框架**: LangGraph
-- **LLM**: 千问 Qwen-Max (DashScope API)
-- **Embedding**: DashScope text-embedding-v3
-- **向量数据库**: Milvus
+| 组件 | 技术选型 | 说明 |
+|------|----------|------|
+| Web框架 | FastAPI | 高性能异步 API 框架 |
+| AI框架 | LangGraph | 状态图工作流编排 |
+| LLM | 千问 Qwen-Max | 阿里云 DashScope API |
+| Embedding | text-embedding-v3 | DashScope 文本向量服务 |
+| 向量数据库 | Milvus | 开源高性能向量检索引擎 |
+| 关系数据库 | PostgreSQL | 数据持久化存储 |
+| 缓存 | Redis | 会话缓存与任务队列 |
+| 图数据库 | Neo4j | 法规关系图谱存储 |
+| 消息队列 | RabbitMQ | 异步任务处理 |
+
+## 服务依赖
+
+需要启动以下基础服务：
+
+| 服务 | 端口 | 用户/密码 |
+|------|------|-----------|
+| PostgreSQL | 5432 | postgresql/postgresql123456 |
+| Redis | 6379 | redis@123 |
+| Milvus | 19530, 9091 | - |
+| MinIO | 9000, 9001 | minioadmin/minioadmin |
+| Neo4j | 7474, 7687 | neo4j/neo4j123 |
+| RabbitMQ | 5672, 15672 | admin/admin@123 |
 
 ## 快速开始
 
-### 1. 启动 Milvus
+### 1. 安装依赖
 
 ```bash
-docker run -d --name milvus-standalone \
-  -p 19530:19530 \
-  -p 9091:9091 \
-  -v $(pwd)/milvus-data:/var/lib/milvus \
-  milvusdb/milvus:v2.3.3 standalone
+cd backend
+uv pip install -r requirements.txt
 ```
 
 ### 2. 配置环境变量
 
 ```bash
 cp .env.example .env
-# 编辑 .env 文件，填入 DashScope API Key
+# 编辑 .env 文件，配置各项服务参数
 ```
 
-### 3. 安装依赖
+### 3. 启动服务
 
 ```bash
-pip install -r requirements.txt
+uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
 ```
 
-### 4. 启动服务
+### 4. 访问 API 文档
+
+- Swagger UI: http://localhost:8000/docs
+- ReDoc: http://localhost:8000/redoc
+
+## 环境配置
+
+`.env` 文件配置项：
 
 ```bash
-uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
+# DashScope API（LLM 与 Embedding）
+DASHSCOPE_API_KEY=your_api_key_here
+LLM_MODEL=qwen-max
+EMBEDDING_MODEL=text-embedding-v3
+EMBEDDING_DIM=1536
+
+# PostgreSQL
+POSTGRES_HOST=localhost
+POSTGRES_PORT=5432
+POSTGRES_USER=postgresql
+POSTGRES_PASSWORD=postgresql123456
+POSTGRES_DB=mydb
+
+# Redis
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_PASSWORD=redis@123
+
+# Milvus
+MILVUS_HOST=localhost
+MILVUS_PORT=19530
+
+# MinIO
+MINIO_ENDPOINT=localhost:9000
+MINIO_ACCESS_KEY=minioadmin
+MINIO_SECRET_KEY=minioadmin
+
+# Neo4j
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=neo4j123
+
+# RabbitMQ
+RABBITMQ_HOST=localhost
+RABBITMQ_PORT=5672
+RABBITMQ_USER=admin
+RABBITMQ_PASSWORD=admin@123
+
+# 检索配置
+VECTOR_TOP_K=10
+BM25_TOP_K=10
+FINAL_TOP_K=5
+
+# 分块配置
+CHUNK_SIZE=800
+CHUNK_OVERLAP=50
+
+# 服务配置
+API_HOST=0.0.0.0
+API_PORT=8000
 ```
 
-### 5. 访问API文档
+## API 接口文档
 
-打开浏览器访问: http://localhost:8000/docs
+### 1. 文档管理 `/api/docs`
 
-## API接口
-
-| 模块 | 路径 | 说明 |
+| 接口 | 方法 | 说明 |
 |------|------|------|
-| 文档管理 | `/api/docs` | 上传、解析、索引法规文档 |
-| RAG问答 | `/api/rag` | SSE流式问答 |
-| 合规分析 | `/api/compliance` | 设计方案合规分析 |
-| 系统状态 | `/api/status` | 统计、配置、健康检查 |
+| `/upload` | POST | 上传法规文档 (PDF/DOCX/TXT) |
+| `/list` | GET | 获取已索引文档列表 |
+| `/parse/{doc_id}` | POST | 解析文档并分块 |
+| `/embed/{doc_id}` | POST | 向量化并存入 Milvus |
+| `/delete/{doc_id}` | DELETE | 删除文档 |
+
+**上传文档响应示例：**
+```json
+{
+  "doc_id": "doc-001",
+  "filename": "道路交通安全法.pdf",
+  "size": 102400,
+  "status": "uploaded"
+}
+```
+
+### 2. RAG 问答 `/api/rag`
+
+| 接口 | 方法 | 说明 |
+|------|------|------|
+| `/chat` | POST | SSE 流式问答 |
+| `/quick-questions` | GET | 获取预设快捷问题 |
+
+**请求参数：**
+```json
+{
+  "query": "电动自行车需要上牌照吗？",
+  "top_k": 5
+}
+```
+
+**SSE 事件流格式：**
+```json
+{"type": "retrieving"}
+{"type": "retrieved", "docs": [...]}
+{"type": "generating", "text": "正在生成答案..."}
+{"type": "chunk", "text": "答案片段..."}
+{"type": "done"}
+```
+
+### 3. 合规分析 `/api/compliance`
+
+| 接口 | 方法 | 说明 |
+|------|------|------|
+| `/analyze` | POST | 上传设计方案进行分析 |
+| `/result/{task_id}` | GET | 获取分析结果 |
+| `/chat/{segment_id}` | POST | 针对特定段落进行合规对话 |
+
+**分析结果响应示例：**
+```json
+{
+  "task_id": "task-xxx",
+  "dashboard": {
+    "score": 78,
+    "high_risk_count": 2,
+    "medium_risk_count": 1,
+    "low_risk_count": 0,
+    "need_fix_segments": 3,
+    "status": "warning",
+    "status_label": "需优化"
+  },
+  "segments": [
+    {
+      "id": 1,
+      "intent": "车身结构设计",
+      "content": "...",
+      "risk_level": "high",
+      "regulations": [...]
+    }
+  ],
+  "priority_actions": [...]
+}
+```
+
+### 4. 系统状态 `/api/status`
+
+| 接口 | 方法 | 说明 |
+|------|------|------|
+| `/stats` | GET | 系统统计数据 |
+| `/config` | GET | 当前配置信息 |
+| `/milvus/health` | GET | Milvus 健康检查 |
+
+**统计数据响应：**
+```json
+{
+  "docs": 5,
+  "chunks": 510,
+  "vectors": 510,
+  "segments": 0
+}
+```
 
 ## 项目结构
 
 ```
-app/
-├── main.py              # FastAPI入口
-├── core/config.py       # 配置管理
-├── api/routes/          # API路由
-├── services/            # 服务层
-├── workflows/           # LangGraph工作流
-├── schemas/             # Pydantic模型
-└── utils/               # 工具函数
+backend/
+├── app/
+│   ├── main.py                 # FastAPI 应用入口
+│   ├── core/
+│   │   └── config.py           # Pydantic Settings 配置管理
+│   ├── api/
+│   │   ├── __init__.py         # API 路由聚合
+│   │   └── routes/
+│   │       ├── docs.py         # 文档管理接口
+│   │       ├── rag.py          # RAG 问答接口
+│   │       ├── compliance.py   # 合规分析接口
+│   │       └── status.py       # 系统状态接口
+│   ├── schemas/
+│   │   ├── doc.py              # 文档相关数据模型
+│   │   ├── rag.py              # RAG 问答数据模型
+│   │   └── compliance.py       # 合规分析数据模型
+│   ├── services/
+│   │   ├── llm.py              # LLM 服务封装
+│   │   ├── embedding.py        # Embedding 服务封装
+│   │   ├── milvus.py           # Milvus 向量库服务
+│   │   ├── document.py         # 文档解析服务
+│   │   └── mock_data.py        # Mock 数据（开发测试）
+│   ├── workflows/
+│   │   ├── rag_workflow.py     # RAG 工作流
+│   │   └── compliance_workflow.py  # 合规分析工作流
+│   └── utils/
+│       ├── chunking.py         # 文本分块工具
+│       └── logger.py           # 日志工具
+├── data/
+│   ├── raw/                    # 原始上传文件
+│   └── parsed/                 # 解析后文件
+├── tests/                      # 测试目录
+├── .env                        # 环境变量配置
+├── .env.example                # 环境变量示例
+├── requirements.txt            # Python 依赖
+├── pyproject.toml              # 项目配置
+└── main.py                     # 入口脚本
 ```
+
+## 核心模块详解
+
+### 配置管理 (`app/core/config.py`)
+
+使用 Pydantic Settings 管理配置，自动从 `.env` 文件加载：
+
+```python
+class Settings(BaseSettings):
+    dashscope_api_key: str = ""
+    milvus_host: str = "localhost"
+    milvus_port: int = 19530
+    llm_model: str = "qwen-max"
+    embedding_model: str = "text-embedding-v3"
+    embedding_dim: int = 1536
+    # ...
+    
+    class Config:
+        env_file = ".env"
+```
+
+### 服务层 (`app/services/`)
+
+#### LLM 服务 (`llm.py`)
+- 封装 DashScope API 调用
+- 支持流式输出
+- 提供对话补全功能
+
+#### Embedding 服务 (`embedding.py`)
+- 文本向量化
+- 批量嵌入支持
+- 维度配置 (1536)
+
+#### Milvus 服务 (`milvus.py`)
+- Collection 创建与管理
+- 向量插入与检索
+- 混合检索 (向量 + BM25)
+
+#### Mock 数据服务 (`mock_data.py`)
+- 预设法规文档数据
+- 预设问答数据
+- 预设合规分析结果
+- 用于开发测试阶段
+
+## 工作流设计
+
+### RAG 工作流 (`rag_workflow.py`)
+
+基于 LangGraph 构建的状态图工作流：
+
+```
+[用户查询] -> [检索向量库] -> [BM25补充] -> [结果融合] -> [LLM生成] -> [输出答案]
+```
+
+### 合规分析工作流 (`compliance_workflow.py`)
+
+```
+[上传文档] -> [解析文档] -> [AI语义分段] -> [法规匹配] -> [风险评分] -> [生成建议]
+```
+
+状态节点：
+- `parse`: 解析文档提取文本
+- `segment`: AI 识别语义段落
+- `match`: 向量检索匹配法规
+- `score`: 计算风险等级
+- `suggest`: 生成优先修改建议
+
+## 数据模型
+
+### 文档模型 (`schemas/doc.py`)
+
+| 模型 | 字段 | 说明 |
+|------|------|------|
+| DocumentUploadResponse | doc_id, filename, size, status | 上传响应 |
+| DocumentInfo | id, name, chunks, status, created_at | 文档信息 |
+| DocumentListResponse | docs | 文档列表 |
+| ParseResponse | doc_id, chunks, status | 解析响应 |
+| EmbedResponse | doc_id, vectors, status | 嵌入响应 |
+
+### RAG 模型 (`schemas/rag.py`)
+
+| 模型 | 字段 | 说明 |
+|------|------|------|
+| RagChatRequest | query, top_k | 问答请求 |
+| RetrievedDoc | id, doc_name, clause_id, score, content, preview | 检索文档 |
+| QuickQuestion | id, question, category | 快捷问题 |
+| QuickQuestionsResponse | questions | 快捷问题列表 |
+
+### 合规模型 (`schemas/compliance.py`)
+
+| 模型 | 字段 | 说明 |
+|------|------|------|
+| Regulation | id, name, clause, score, match_keyword, category, full_content | 法规条目 |
+| ComplianceSegment | id, index, intent, content, risk_level, regulations | 语义段落 |
+| RiskDashboard | score, high_risk_count, medium_risk_count, low_risk_count, status | 风险仪表盘 |
+| PriorityAction | regulation, issue, suggestion, severity | 优先建议 |
+| ComplianceResult | task_id, dashboard, segments, priority_actions | 分析结果 |
+
+### 风险等级枚举
+
+```python
+class RiskLevel(str, Enum):
+    high = "high"      # 高风险：需立即修改
+    medium = "medium"  # 中风险：建议优化
+    low = "low"        # 低风险：基本合规
+```
+
+### 合规状态枚举
+
+```python
+class ComplianceStatus(str, Enum):
+    pass_status = "pass"    # 合规通过
+    warning = "warning"     # 需要优化
+    fail = "fail"           # 不合规
+```
+
+## 开发说明
+
+### Mock 模式
+
+当依赖服务未安装或 API Key 未配置时，系统自动使用 Mock 数据模式，返回预设的测试数据，便于前端开发调试。
+
+### SSE 流式输出
+
+使用 `sse-starlette` 库实现 Server-Sent Events 流式响应，适用于：
+- RAG 问答实时输出
+- 合规对话实时响应
+
+### CORS 配置
+
+已配置允许所有来源的跨域请求，生产环境需根据实际需求调整。
+
+```python
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+```
+
+## 测试
+
+```bash
+pytest tests/
+```
+
+## 许可证
+
+MIT

app/api/routes/docs.py

@@ -1,115 +1,299 @@
-from fastapi import APIRouter, UploadFile, File, HTTPException
+"""文档管理 API"""
+
+from fastapi import APIRouter, UploadFile, File, HTTPException, BackgroundTasks
 import os
 import uuid
 from datetime import datetime
+from typing import Optional
+
 from app.schemas.doc import (
     DocumentUploadResponse,
     DocumentListResponse,
     DocumentInfo,
     ParseResponse,
     EmbedResponse,
+    TaskStatusResponse,
 )
-from app.services.mock_data import get_mock_documents, generate_doc_id
+from app.core.config import settings
+from app.services.minio import minio_service
+from app.services.database import db_service, init_db, DocStatus
+from app.services.tasks import generate_task_id, task_manager, get_task_status
+from app.workflows.document_workflow import (
+    generate_doc_id,
+    run_parse_workflow,
+    run_embedding_workflow,
+)
+from app.utils.logger import logger
+
 
 router = APIRouter(prefix="/docs", tags=["文档管理"])
 
-# 临时存储文档信息（包含预设的mock文档）
-documents_store: dict[str, dict] = {}
+# 启动时初始化数据库
+init_db()
 
-# 初始化时加载mock文档
-for doc in get_mock_documents():
-    documents_store[doc["id"]] = doc
+
+def get_content_type(filename: str) -> str:
+    """根据文件扩展名获取 Content-Type"""
+    ext = os.path.splitext(filename)[1].lower()
+    content_types = {
+        ".pdf": "application/pdf",
+        ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        ".doc": "application/msword",
+        ".txt": "text/plain",
+    }
+    return content_types.get(ext, "application/octet-stream")
 
 
 @router.post("/upload", response_model=DocumentUploadResponse)
-async def upload_document(file: UploadFile = File(...)):
-    """上传法规文档"""
+async def upload_document(
+    file: UploadFile = File(...),
+    background_tasks: BackgroundTasks = None,
+):
+    """
+    上传法规文档到 MinIO，并自动触发异步解析
+
+    流程：
+    1. 验证文件格式
+    2. 生成文档ID
+    3. 上传到 MinIO
+    4. 创建数据库记录
+    5. 触发异步解析任务（后续可替换为 RabbitMQ）
+    """
     # 检查文件格式
     allowed_ext = [".pdf", ".docx", ".doc", ".txt"]
     ext = os.path.splitext(file.filename)[1].lower()
     if ext not in allowed_ext:
         raise HTTPException(400, f"Unsupported file format: {ext}")
 
+    # 检查文件大小
+    content = await file.read()
+    max_size = 50 * 1024 * 1024  # 50MB
+    if len(content) > max_size:
+        raise HTTPException(400, f"File size exceeds limit: {max_size // 1024 // 1024}MB")
+
     # 生成文档ID
     doc_id = generate_doc_id()
 
-    # 保存文件
-    raw_dir = "/airegulation/demo-mao/backend/data/raw"
-    os.makedirs(raw_dir, exist_ok=True)
-    file_path = os.path.join(raw_dir, f"{doc_id}_{file.filename}")
+    # 构建 MinIO 存储路径
+    storage_filename = f"{doc_id}_{file.filename}"
+    minio_path = f"documents/{storage_filename}"
 
-    content = await file.read()
-    with open(file_path, "wb") as f:
-        f.write(content)
+    try:
+        # 上传到 MinIO
+        content_type = get_content_type(file.filename)
+        minio_url = minio_service.upload_file(
+            minio_path,
+            content,
+            content_type,
+        )
 
-    # 记录文档信息
-    documents_store[doc_id] = {
-        "id": doc_id,
-        "name": file.filename,
-        "path": file_path,
-        "size": len(content),
-        "status": "uploaded",
-        "chunks": 0,
-        "created_at": datetime.now(),
-    }
+        # 创建数据库记录
+        doc = db_service.create_document(
+            doc_id=doc_id,
+            filename=storage_filename,
+            original_name=file.filename,
+            minio_path=minio_path,
+            size=len(content),
+        )
 
-    return DocumentUploadResponse(
-        doc_id=doc_id,
-        filename=file.filename,
-        size=len(content),
-    )
+        logger.info(f"Document uploaded: {doc_id} - {file.filename}")
+
+        # 触发异步解析任务
+        parse_task_id = generate_task_id()
+        db_service.create_parse_task(parse_task_id, doc_id)
+
+        # 使用 asyncio 异步执行解析（后续替换为 RabbitMQ）
+        background_tasks.add_task(
+            run_parse_workflow_sync,
+            parse_task_id,
+            doc_id,
+        )
+
+        return DocumentUploadResponse(
+            doc_id=doc_id,
+            filename=file.filename,
+            size=len(content),
+            status="uploaded",
+            parse_task_id=parse_task_id,
+        )
+
+    except Exception as e:
+        logger.error(f"Upload failed: {e}")
+        raise HTTPException(500, f"Upload failed: {str(e)}")
+
+
+def run_parse_workflow_sync(task_id: str, doc_id: str):
+    """同步包装器，用于 BackgroundTasks"""
+    import asyncio
+    asyncio.run(run_parse_workflow(task_id, doc_id))
 
 
 @router.get("/list", response_model=DocumentListResponse)
 async def list_documents():
     """获取已索引文档列表"""
-    docs = [
-        DocumentInfo(
-            id=d["id"],
-            name=d["name"],
-            chunks=d["chunks"],
-            status=d["status"],
-            created_at=d.get("created_at"),
-        )
-        for d in documents_store.values()
-    ]
-    return DocumentListResponse(docs=docs)
+    docs = db_service.list_documents()
+    return DocumentListResponse(
+        docs=[
+            DocumentInfo(
+                id=d.id,
+                name=d.original_name,
+                chunks=d.chunks,
+                status=d.status,
+                created_at=d.created_at,
+            )
+            for d in docs
+        ]
+    )
+
+
+@router.get("/{doc_id}", response_model=DocumentInfo)
+async def get_document(doc_id: str):
+    """获取单个文档信息"""
+    doc = db_service.get_document(doc_id)
+    if not doc:
+        raise HTTPException(404, "Document not found")
+
+    return DocumentInfo(
+        id=doc.id,
+        name=doc.original_name,
+        chunks=doc.chunks,
+        status=doc.status,
+        created_at=doc.created_at,
+    )
 
 
 @router.post("/parse/{doc_id}", response_model=ParseResponse)
-async def parse_document(doc_id: str):
-    """解析文档并分块"""
-    if doc_id not in documents_store:
+async def parse_document(
+    doc_id: str,
+    background_tasks: BackgroundTasks = None,
+):
+    """
+    手动触发文档解析（如果文档已上传但未解析）
+    """
+    doc = db_service.get_document(doc_id)
+    if not doc:
         raise HTTPException(404, "Document not found")
 
-    doc = documents_store[doc_id]
-    # 模拟解析逻辑
-    doc["status"] = "parsed"
-    # 根据文件大小计算chunks数量
-    file_size = doc.get("size", 100000)
-    doc["chunks"] = max(20, file_size // 8000)
+    if doc.status not in [DocStatus.uploaded.value, DocStatus.failed.value]:
+        raise HTTPException(400, f"Document status is {doc.status}, cannot parse")
 
-    return ParseResponse(doc_id=doc_id, chunks=doc["chunks"])
+    # 创建解析任务
+    task_id = generate_task_id()
+    db_service.create_parse_task(task_id, doc_id)
+
+    # 异步执行
+    background_tasks.add_task(
+        run_parse_workflow_sync,
+        task_id,
+        doc_id,
+    )
+
+    return ParseResponse(
+        doc_id=doc_id,
+        task_id=task_id,
+        status="parsing",
+    )
 
 
 @router.post("/embed/{doc_id}", response_model=EmbedResponse)
-async def embed_document(doc_id: str):
-    """嵌入并存入向量库"""
-    if doc_id not in documents_store:
+async def embed_document(
+    doc_id: str,
+    background_tasks: BackgroundTasks = None,
+):
+    """
+    触发文档向量化（需要文档已解析）
+    """
+    doc = db_service.get_document(doc_id)
+    if not doc:
         raise HTTPException(404, "Document not found")
 
-    doc = documents_store[doc_id]
-    # 模拟嵌入逻辑
-    doc["status"] = "indexed"
+    if doc.status != DocStatus.parsed.value:
+        raise HTTPException(400, f"Document must be parsed first. Current status: {doc.status}")
 
-    return EmbedResponse(doc_id=doc_id, vectors=doc["chunks"])
+    # 创建向量化任务
+    task_id = generate_task_id()
+    db_service.create_parse_task(task_id, doc_id)
+
+    # 异步执行
+    background_tasks.add_task(
+        run_embedding_workflow_sync,
+        task_id,
+        doc_id,
+    )
+
+    return EmbedResponse(
+        doc_id=doc_id,
+        task_id=task_id,
+        status="embedding",
+    )
+
+
+def run_embedding_workflow_sync(task_id: str, doc_id: str):
+    """同步包装器，用于 BackgroundTasks"""
+    import asyncio
+    asyncio.run(run_embedding_workflow(task_id, doc_id))
+
+
+@router.get("/task/{task_id}", response_model=TaskStatusResponse)
+async def get_task_status_api(task_id: str):
+    """获取任务状态"""
+    status = get_task_status(task_id)
+    if not status:
+        # 检查数据库中的任务记录
+        task = db_service.get_parse_task(task_id)
+        if task:
+            return TaskStatusResponse(
+                task_id=task_id,
+                status=task.status,
+                progress=task.progress or 0,
+                message=task.message,
+            )
+        raise HTTPException(404, "Task not found")
+
+    return TaskStatusResponse(
+        task_id=task_id,
+        status=status.get("status", "unknown"),
+        progress=status.get("progress", 0),
+        message=status.get("message"),
+        result=status.get("result"),
+    )
 
 
 @router.delete("/delete/{doc_id}")
 async def delete_document(doc_id: str):
-    """删除文档"""
-    if doc_id not in documents_store:
+    """
+    删除文档
+
+    同时删除：
+    - MinIO 中的文件
+    - 数据库中的记录
+    - 解析后的文本文件
+    """
+    doc = db_service.get_document(doc_id)
+    if not doc:
         raise HTTPException(404, "Document not found")
 
-    del documents_store[doc_id]
-    return {"success": True}
+    try:
+        # 删除 MinIO 文件
+        minio_service.delete_file(doc.minio_path)
+
+        # 删除本地解析文件
+        parsed_path = f"{settings.data_parsed_dir}/{doc_id}.txt"
+        if os.path.exists(parsed_path):
+            os.remove(parsed_path)
+
+        # 删除本地临时文件
+        temp_path = f"{settings.data_raw_dir}/{doc.filename}"
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+        # 删除数据库记录
+        db_service.delete_document(doc_id)
+
+        logger.info(f"Document deleted: {doc_id}")
+
+        return {"success": True, "doc_id": doc_id}
+
+    except Exception as e:
+        logger.error(f"Delete failed: {e}")
+        raise HTTPException(500, f"Delete failed: {str(e)}")

app/core/config.py

@@ -3,13 +3,51 @@ from typing import Optional
 
 
 class Settings(BaseSettings):
-    # DashScope API
-    dashscope_api_key: str = ""
+    # Qwen API配置
+    qwen_api_key: str = ""
+    qwen_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
+    qwen_model: str = "qwen-max"
+    qwen_vl_model: str = "qwen-vl-plus"
+
+    # DeepSeek API配置
+    deepseek_api_key: str = ""
+    deepseek_base_url: str = "https://api.deepseek.com/v1"
+    deepseek_model: str = "deepseek-v3"
+
+    # PostgreSQL
+    postgres_host: str = "localhost"
+    postgres_port: int = 5432
+    postgres_user: str = "postgresql"
+    postgres_password: str = "postgresql123456"
+    postgres_db: str = "mydb"
+
+    # Redis
+    redis_host: str = "localhost"
+    redis_port: int = 6379
+    redis_password: str = ""
+
+    # MinIO
+    minio_endpoint: str = "localhost:9000"
+    minio_access_key: str = "minioadmin"
+    minio_secret_key: str = "minioadmin"
+    minio_bucket: str = "regulation-docs"
+    minio_secure: bool = False
 
     # Milvus
     milvus_host: str = "localhost"
     milvus_port: int = 19530
 
+    # Neo4j
+    neo4j_uri: str = "bolt://localhost:7687"
+    neo4j_user: str = "neo4j"
+    neo4j_password: str = "neo4j123"
+
+    # RabbitMQ
+    rabbitmq_host: str = "localhost"
+    rabbitmq_port: int = 5672
+    rabbitmq_user: str = "admin"
+    rabbitmq_password: str = "admin@123"
+
     # LLM配置
     llm_model: str = "qwen-max"
     embedding_model: str = "text-embedding-v3"
@@ -32,6 +70,10 @@ class Settings(BaseSettings):
     regulations_collection: str = "vehicle_regulations"
     compliance_collection: str = "compliance_cache"
 
+    # 数据目录
+    data_raw_dir: str = "/airegulation/demo-mao/backend/data/raw"
+    data_parsed_dir: str = "/airegulation/demo-mao/backend/data/parsed"
+
     class Config:
         env_file = ".env"
         env_file_encoding = "utf-8"

app/schemas/doc.py

@@ -1,16 +1,21 @@
+"""文档相关数据模型"""
+
 from pydantic import BaseModel
-from typing import Optional
+from typing import Optional, Any
 from datetime import datetime
 
 
 class DocumentUploadResponse(BaseModel):
+    """文档上传响应"""
     doc_id: str
     filename: str
     size: int
     status: str = "uploaded"
+    parse_task_id: Optional[str] = None  # 解析任务ID
 
 
 class DocumentInfo(BaseModel):
+    """文档信息"""
     id: str
     name: str
     chunks: int
@@ -19,10 +24,12 @@ class DocumentInfo(BaseModel):
 
 
 class DocumentListResponse(BaseModel):
+    """文档列表响应"""
     docs: list[DocumentInfo]
 
 
 class ChunkInfo(BaseModel):
+    """文本块信息"""
     chunk_id: str
     doc_name: str
     clause_id: Optional[str] = None
@@ -33,12 +40,25 @@ class ChunkInfo(BaseModel):
 
 
 class ParseResponse(BaseModel):
+    """解析响应"""
     doc_id: str
-    chunks: int
-    status: str = "parsed"
+    task_id: Optional[str] = None
+    chunks: int = 0
+    status: str = "parsing"
 
 
 class EmbedResponse(BaseModel):
+    """嵌入响应"""
     doc_id: str
-    vectors: int
-    status: str = "embedded"
+    task_id: Optional[str] = None
+    vectors: int = 0
+    status: str = "embedding"
+
+
+class TaskStatusResponse(BaseModel):
+    """任务状态响应"""
+    task_id: str
+    status: str
+    progress: int
+    message: Optional[str] = None
+    result: Optional[Any] = None

app/services/__init__.py

@@ -1,4 +1,9 @@
-# Import mock data service
+# Import services
+from .minio import minio_service, MinioService
+from .database import db_service, DatabaseService, init_db, Document, ParseTask
+from .tasks import task_manager, get_task_status, set_task_status, generate_task_id
+
+# Import mock data service (for development)
 from .mock_data import (
     get_mock_documents,
     get_mock_quick_questions,
@@ -29,6 +34,18 @@ except ImportError:
     get_document_service = None
 
 __all__ = [
+    # Core services
+    "minio_service",
+    "MinioService",
+    "db_service",
+    "DatabaseService",
+    "init_db",
+    "Document",
+    "ParseTask",
+    "task_manager",
+    "get_task_status",
+    "set_task_status",
+    "generate_task_id",
     # Mock data services
     "get_mock_documents",
     "get_mock_quick_questions",

app/services/database.py

@@ -0,0 +1,228 @@
+"""数据库服务 - PostgreSQL"""
+
+from sqlalchemy import create_engine, Column, String, Integer, DateTime, Enum, Text
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker, Session
+from datetime import datetime
+from typing import Optional, List
+import enum
+from app.core.config import settings
+from app.utils.logger import logger
+
+
+# 数据库连接
+DATABASE_URL = f"postgresql://{settings.postgres_user}:{settings.postgres_password}@{settings.postgres_host}:{settings.postgres_port}/{settings.postgres_db}"
+
+engine = create_engine(DATABASE_URL, echo=False, pool_pre_ping=True)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+Base = declarative_base()
+
+
+class DocStatus(str, enum.Enum):
+    """文档处理状态"""
+    uploaded = "uploaded"       # 已上传
+    parsing = "parsing"         # 解析中
+    parsed = "parsed"           # 已解析
+    embedding = "embedding"     # 向量化中
+    indexed = "indexed"         # 已索引
+    failed = "failed"           # 处理失败
+
+
+class Document(Base):
+    """文档表"""
+    __tablename__ = "documents"
+
+    id = Column(String(64), primary_key=True)
+    filename = Column(String(255), nullable=False)
+    original_name = Column(String(255), nullable=False)
+    minio_path = Column(String(512), nullable=False)  # MinIO 存储路径
+    size = Column(Integer, default=0)
+    status = Column(String(32), default=DocStatus.uploaded.value)
+    chunks = Column(Integer, default=0)
+    vectors = Column(Integer, default=0)
+    error_message = Column(Text, nullable=True)
+    created_at = Column(DateTime, default=datetime.now)
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "name": self.original_name,
+            "chunks": self.chunks,
+            "status": self.status,
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+        }
+
+
+class ParseTask(Base):
+    """解析任务表"""
+    __tablename__ = "parse_tasks"
+
+    id = Column(String(64), primary_key=True)
+    doc_id = Column(String(64), nullable=False)
+    status = Column(String(32), default="pending")
+    progress = Column(Integer, default=0)
+    message = Column(Text, nullable=True)
+    created_at = Column(DateTime, default=datetime.now)
+    started_at = Column(DateTime, nullable=True)
+    completed_at = Column(DateTime, nullable=True)
+
+
+def init_db():
+    """初始化数据库表"""
+    try:
+        Base.metadata.create_all(bind=engine)
+        logger.info("Database tables created successfully")
+    except Exception as e:
+        logger.error(f"Database initialization failed: {e}")
+
+
+def get_db() -> Session:
+    """获取数据库会话"""
+    db = SessionLocal()
+    try:
+        return db
+    finally:
+        # 注意：调用者需要负责关闭会话
+        pass
+
+
+class DatabaseService:
+    """数据库服务"""
+
+    def __init__(self):
+        self.engine = engine
+        self.SessionLocal = SessionLocal
+
+    def create_document(
+        self,
+        doc_id: str,
+        filename: str,
+        original_name: str,
+        minio_path: str,
+        size: int,
+    ) -> Document:
+        """创建文档记录"""
+        db = self.SessionLocal()
+        try:
+            doc = Document(
+                id=doc_id,
+                filename=filename,
+                original_name=original_name,
+                minio_path=minio_path,
+                size=size,
+                status=DocStatus.uploaded.value,
+            )
+            db.add(doc)
+            db.commit()
+            db.refresh(doc)
+            return doc
+        finally:
+            db.close()
+
+    def get_document(self, doc_id: str) -> Optional[Document]:
+        """获取文档"""
+        db = self.SessionLocal()
+        try:
+            return db.query(Document).filter(Document.id == doc_id).first()
+        finally:
+            db.close()
+
+    def update_document_status(
+        self,
+        doc_id: str,
+        status: str,
+        chunks: int = None,
+        vectors: int = None,
+        error_message: str = None,
+    ) -> Optional[Document]:
+        """更新文档状态"""
+        db = self.SessionLocal()
+        try:
+            doc = db.query(Document).filter(Document.id == doc_id).first()
+            if doc:
+                doc.status = status
+                doc.updated_at = datetime.now()
+                if chunks is not None:
+                    doc.chunks = chunks
+                if vectors is not None:
+                    doc.vectors = vectors
+                if error_message:
+                    doc.error_message = error_message
+                db.commit()
+                db.refresh(doc)
+            return doc
+        finally:
+            db.close()
+
+    def list_documents(self) -> List[Document]:
+        """列出所有文档"""
+        db = self.SessionLocal()
+        try:
+            return db.query(Document).order_by(Document.created_at.desc()).all()
+        finally:
+            db.close()
+
+    def delete_document(self, doc_id: str) -> bool:
+        """删除文档"""
+        db = self.SessionLocal()
+        try:
+            doc = db.query(Document).filter(Document.id == doc_id).first()
+            if doc:
+                db.delete(doc)
+                db.commit()
+                return True
+            return False
+        finally:
+            db.close()
+
+    def create_parse_task(self, task_id: str, doc_id: str) -> ParseTask:
+        """创建解析任务"""
+        db = self.SessionLocal()
+        try:
+            task = ParseTask(id=task_id, doc_id=doc_id)
+            db.add(task)
+            db.commit()
+            db.refresh(task)
+            return task
+        finally:
+            db.close()
+
+    def get_parse_task(self, task_id: str) -> Optional[ParseTask]:
+        """获取解析任务"""
+        db = self.SessionLocal()
+        try:
+            return db.query(ParseTask).filter(ParseTask.id == task_id).first()
+        finally:
+            db.close()
+
+    def update_parse_task(
+        self,
+        task_id: str,
+        status: str,
+        progress: int = None,
+        message: str = None,
+    ) -> Optional[ParseTask]:
+        """更新解析任务状态"""
+        db = self.SessionLocal()
+        try:
+            task = db.query(ParseTask).filter(ParseTask.id == task_id).first()
+            if task:
+                task.status = status
+                if progress is not None:
+                    task.progress = progress
+                if message:
+                    task.message = message
+                if status == "running":
+                    task.started_at = datetime.now()
+                elif status in ("completed", "failed"):
+                    task.completed_at = datetime.now()
+                db.commit()
+                db.refresh(task)
+            return task
+        finally:
+            db.close()
+
+
+# 单例
+db_service = DatabaseService()

app/services/minio.py

@@ -0,0 +1,122 @@
+"""MinIO 文件存储服务"""
+
+import io
+from minio import Minio
+from minio.error import S3Error
+from app.core.config import settings
+from app.utils.logger import logger
+
+
+class MinioService:
+    """MinIO 文件存储服务"""
+
+    def __init__(self):
+        self.client = Minio(
+            settings.minio_endpoint,
+            access_key=settings.minio_access_key,
+            secret_key=settings.minio_secret_key,
+            secure=settings.minio_secure,
+        )
+        self.bucket = settings.minio_bucket
+        self._ensure_bucket()
+
+    def _ensure_bucket(self):
+        """确保存储桶存在"""
+        try:
+            if not self.client.bucket_exists(self.bucket):
+                self.client.make_bucket(self.bucket)
+                logger.info(f"Created MinIO bucket: {self.bucket}")
+        except S3Error as e:
+            logger.error(f"MinIO bucket check failed: {e}")
+
+    def upload_file(
+        self,
+        object_name: str,
+        file_data: bytes,
+        content_type: str = "application/octet-stream",
+    ) -> str:
+        """
+        上传文件到 MinIO
+
+        Args:
+            object_name: 对象名称（文件路径）
+            file_data: 文件二进制数据
+            content_type: 文件类型
+
+        Returns:
+            文件的 MinIO URL
+        """
+        try:
+            data_stream = io.BytesIO(file_data)
+            self.client.put_object(
+                self.bucket,
+                object_name,
+                data_stream,
+                length=len(file_data),
+                content_type=content_type,
+            )
+            url = f"{settings.minio_endpoint}/{self.bucket}/{object_name}"
+            logger.info(f"Uploaded file to MinIO: {object_name}")
+            return url
+        except S3Error as e:
+            logger.error(f"MinIO upload failed: {e}")
+            raise
+
+    def get_file(self, object_name: str) -> bytes:
+        """
+        从 MinIO 获取文件
+
+        Args:
+            object_name: 对象名称
+
+        Returns:
+            文件二进制数据
+        """
+        try:
+            response = self.client.get_object(self.bucket, object_name)
+            data = response.read()
+            response.close()
+            response.release_conn()
+            return data
+        except S3Error as e:
+            logger.error(f"MinIO get file failed: {e}")
+            raise
+
+    def delete_file(self, object_name: str) -> bool:
+        """
+        删除 MinIO 中的文件
+
+        Args:
+            object_name: 对象名称
+
+        Returns:
+            是否成功删除
+        """
+        try:
+            self.client.remove_object(self.bucket, object_name)
+            logger.info(f"Deleted file from MinIO: {object_name}")
+            return True
+        except S3Error as e:
+            logger.error(f"MinIO delete failed: {e}")
+            return False
+
+    def list_files(self, prefix: str = "") -> list[str]:
+        """
+        列出 MinIO 中的文件
+
+        Args:
+            prefix: 文件前缀过滤
+
+        Returns:
+            文件名列表
+        """
+        try:
+            objects = self.client.list_objects(self.bucket, prefix=prefix)
+            return [obj.object_name for obj in objects]
+        except S3Error as e:
+            logger.error(f"MinIO list files failed: {e}")
+            return []
+
+
+# 单例
+minio_service = MinioService()

app/services/tasks.py

@@ -0,0 +1,89 @@
+"""异步任务处理模块
+
+TODO: 后续替换为 RabbitMQ 消息队列
+"""
+
+import asyncio
+import uuid
+from datetime import datetime
+from typing import Callable, Awaitable
+from app.utils.logger import logger
+
+
+# 任务状态存储（后续替换为 Redis）
+_task_store: Dict[str, Dict] = {}
+
+
+def generate_task_id() -> str:
+    """生成任务ID"""
+    return f"task-{uuid.uuid4().hex[:12]}"
+
+
+class AsyncTaskManager:
+    """异步任务管理器"""
+
+    def __init__(self):
+        self._running_tasks: dict[str, asyncio.Task] = {}
+
+    def create_task(
+        self,
+        task_id: str,
+        task_func: Callable[[str], Awaitable[None]],
+    ) -> asyncio.Task:
+        """
+        创建异步任务
+
+        Args:
+            task_id: 任务ID
+            task_func: 任务执行函数
+
+        Returns:
+            asyncio.Task
+        """
+        task = asyncio.create_task(self._run_task(task_id, task_func))
+        self._running_tasks[task_id] = task
+        return task
+
+    async def _run_task(
+        self,
+        task_id: str,
+        task_func: Callable[[str], Awaitable[None]],
+    ):
+        """运行任务并处理状态"""
+        try:
+            await task_func(task_id)
+        except Exception as e:
+            logger.error(f"Task {task_id} failed: {e}")
+            _task_store[task_id] = {
+                "status": "failed",
+                "error": str(e),
+                "completed_at": datetime.now(),
+            }
+        finally:
+            if task_id in self._running_tasks:
+                del self._running_tasks[task_id]
+
+    def get_task_status(self, task_id: str) -> dict | None:
+        """获取任务状态"""
+        return _task_store.get(task_id)
+
+    def cancel_task(self, task_id: str) -> bool:
+        """取消任务"""
+        if task_id in self._running_tasks:
+            self._running_tasks[task_id].cancel()
+            return True
+        return False
+
+
+# 单例
+task_manager = AsyncTaskManager()
+
+
+def get_task_status(task_id: str) -> dict | None:
+    """获取任务状态"""
+    return _task_store.get(task_id)
+
+
+def set_task_status(task_id: str, status: dict):
+    """设置任务状态"""
+    _task_store[task_id] = status

app/workflows/document_workflow.py

@@ -0,0 +1,252 @@
+"""文档解析工作流 - 异步处理"""
+
+import asyncio
+import uuid
+from datetime import datetime
+from typing import List
+import io
+
+from app.core.config import settings
+from app.services.minio import minio_service
+from app.services.database import db_service, DocStatus
+from app.services.tasks import set_task_status, get_task_status
+from app.services.document import DocumentService
+from app.utils.chunking import TextChunker
+from app.utils.logger import logger
+
+
+def generate_doc_id() -> str:
+    """生成文档ID"""
+    return f"doc-{uuid.uuid4().hex[:12]}"
+
+
+def generate_chunk_id(doc_id: str, index: int) -> str:
+    """生成块ID"""
+    return f"{doc_id}-chunk-{index}"
+
+
+async def run_parse_workflow(task_id: str, doc_id: str):
+    """
+    执行文档解析工作流
+
+    处理步骤：
+    1. 获取文件 - 从 MinIO 下载文件
+    2. 解析文档 - 提取文本内容
+    3. 文本分块 - 按条款或固定大小分块
+    4. 保存结果 - 存储分块数据
+
+    Args:
+        task_id: 任务ID
+        doc_id: 文档ID
+    """
+    chunker = TextChunker()
+    doc_service = DocumentService(settings.data_raw_dir, settings.data_parsed_dir)
+
+    try:
+        # Step 1: 获取文件
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "fetching",
+            "progress": 10,
+            "message": "正在从存储获取文件...",
+            "started_at": datetime.now(),
+        })
+        db_service.update_document_status(doc_id, DocStatus.parsing.value)
+
+        doc = db_service.get_document(doc_id)
+        if not doc:
+            raise ValueError(f"Document {doc_id} not found")
+
+        # 从 MinIO 获取文件
+        file_data = minio_service.get_file(doc.minio_path)
+
+        # 保存到本地临时目录（用于解析）
+        temp_path = f"{settings.data_raw_dir}/{doc_id}_{doc.filename}"
+        with open(temp_path, "wb") as f:
+            f.write(file_data)
+
+        await asyncio.sleep(0.5)  # 模拟延迟
+
+        # Step 2: 解析文档
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "parsing",
+            "progress": 30,
+            "message": "正在解析文档内容...",
+        })
+
+        text = doc_service.parse_document(temp_path)
+
+        if not text:
+            raise ValueError("Document parsing returned empty content")
+
+        # 保存解析后的文本
+        parsed_path = doc_service.save_parsed_text(doc_id, text)
+
+        await asyncio.sleep(0.5)
+
+        # Step 3: 文本分块
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "chunking",
+            "progress": 50,
+            "message": "正在进行文本分块...",
+        })
+
+        # 尝试按条款分块，如果不是法规格式则按大小分块
+        chunks = chunker.chunk_by_clause(text)
+        if len(chunks) == 0:
+            chunks = chunker.chunk_by_size(text)
+
+        await asyncio.sleep(0.5)
+
+        # Step 4: 保存分块结果
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "saving",
+            "progress": 80,
+            "message": f"正在保存 {len(chunks)} 个文本块...",
+        })
+
+        # TODO: 将分块存储到数据库或向量库
+        # 这里先统计数量
+
+        
+
+
+        chunk_count = len(chunks)
+
+        await asyncio.sleep(0.5)
+
+        # Step 5: 完成
+        set_task_status(task_id, {
+            "status": "completed",
+            "step": "done",
+            "progress": 100,
+            "message": f"解析完成，共生成 {chunk_count} 个文本块",
+            "completed_at": datetime.now(),
+            "result": {
+                "doc_id": doc_id,
+                "chunks": chunk_count,
+                "parsed_path": parsed_path,
+            }
+        })
+
+        db_service.update_document_status(
+            doc_id,
+            DocStatus.parsed.value,
+            chunks=chunk_count,
+        )
+
+        logger.info(f"Parse workflow completed for doc {doc_id}: {chunk_count} chunks")
+
+    except Exception as e:
+        logger.error(f"Parse workflow failed for doc {doc_id}: {e}")
+
+        set_task_status(task_id, {
+            "status": "failed",
+            "step": "error",
+            "progress": 0,
+            "message": str(e),
+            "completed_at": datetime.now(),
+        })
+
+        db_service.update_document_status(
+            doc_id,
+            DocStatus.failed.value,
+            error_message=str(e),
+        )
+
+
+async def run_embedding_workflow(task_id: str, doc_id: str):
+    """
+    执行向量化工作流
+
+    处理步骤：
+    1. 获取分块数据
+    2. 生成向量嵌入
+    3. 存入向量数据库
+
+    Args:
+        task_id: 任务ID
+        doc_id: 文档ID
+    """
+    try:
+        # Step 1: 获取分块
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "fetching_chunks",
+            "progress": 10,
+            "message": "正在获取文本分块...",
+            "started_at": datetime.now(),
+        })
+        db_service.update_document_status(doc_id, DocStatus.embedding.value)
+
+        doc = db_service.get_document(doc_id)
+        if not doc:
+            raise ValueError(f"Document {doc_id} not found")
+
+        await asyncio.sleep(0.5)
+
+        # Step 2: 生成嵌入
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "embedding",
+            "progress": 40,
+            "message": "正在生成向量嵌入...",
+        })
+
+        # TODO: 调用 Embedding 服务生成向量
+        # 这里先模拟处理
+        vector_count = doc.chunks
+
+        await asyncio.sleep(1)
+
+        # Step 3: 存入向量库
+        set_task_status(task_id, {
+            "status": "running",
+            "step": "storing",
+            "progress": 70,
+            "message": "正在存入向量数据库...",
+        })
+
+        # TODO: 存入 Milvus
+        await asyncio.sleep(0.5)
+
+        # Step 4: 完成
+        set_task_status(task_id, {
+            "status": "completed",
+            "step": "done",
+            "progress": 100,
+            "message": f"向量化完成，共处理 {vector_count} 个向量",
+            "completed_at": datetime.now(),
+            "result": {
+                "doc_id": doc_id,
+                "vectors": vector_count,
+            }
+        })
+
+        db_service.update_document_status(
+            doc_id,
+            DocStatus.indexed.value,
+            vectors=vector_count,
+        )
+
+        logger.info(f"Embedding workflow completed for doc {doc_id}")
+
+    except Exception as e:
+        logger.error(f"Embedding workflow failed for doc {doc_id}: {e}")
+
+        set_task_status(task_id, {
+            "status": "failed",
+            "step": "error",
+            "progress": 0,
+            "message": str(e),
+            "completed_at": datetime.now(),
+        })
+
+        db_service.update_document_status(
+            doc_id,
+            DocStatus.failed.value,
+            error_message=str(e),
+        )

data/pdf_chunks/page_1.txt

@@ -0,0 +1,24 @@
+=== 第 1 页切片结果 ===
+图片: page_1.png
+MD5: 3a0587908dec601c902b954eeecc6365
+文本长度: 194 字符
+切片大小: 500 字符
+切片数量: 1 个
+
+--- Chunk 0 ---
+长度: 194 字符
+内容:
+!"#!"!#!$
+$%"
+!
+! " # $ % & ’ ’ ( ) *
+%&#&"&"!’$$(
+"# "#$%&%&!$’’(
+! " # $ % & ’ ( )
+#’()*+,)-./,)0)1*2(3,45/67*,/4+46)2
+’$$(8$’8’#*+
+’$$"8$#8$#,-
+!"#$%&’’(+,-./0/123
+! ’ ’ ( ) * 4 5 6 7 8 9
+* +
+

data/pdf_chunks/page_10.txt

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+=== 第 10 页切片结果 ===
+图片: page_10.png
+MD5: e043bd43659fa8cd131f0d1c4b90c4db
+文本长度: 1033 字符
+切片大小: 500 字符
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+
+--- Chunk 0 ---
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+--- Chunk 1 ---
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+--- Chunk 2 ---
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data/pdf_chunks/page_11.txt

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+=== 第 11 页切片结果 ===
+图片: page_11.png
+MD5: 256acb056ed96cb4ab582d55b3792e49
+文本长度: 1147 字符
+切片大小: 500 字符
+切片数量: 3 个
+
+--- Chunk 0 ---
+长度: 500 字符
+内容:
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+--- Chunk 1 ---
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+--- Chunk 2 ---
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+!"!"#"#!$%%&
+

data/pdf_chunks/page_12.txt

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+=== 第 12 页切片结果 ===
+图片: page_12.png
+MD5: 36205c3c596cc35b23b053fb82ff16f0
+文本长度: 380 字符
+切片大小: 500 字符
+切片数量: 1 个
+
+--- Chunk 0 ---
+长度: 380 字符
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+

data/pdf_chunks/page_13.txt

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+=== 第 13 页切片结果 ===
+图片: page_13.png
+MD5: c2a7f4413915dde39c39f15dd5b9c532
+文本长度: 422 字符
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+切片数量: 1 个
+
+--- Chunk 0 ---
+长度: 422 字符
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+

data/pdf_chunks/page_14.txt

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+=== 第 14 页切片结果 ===
+图片: page_14.png
+MD5: 1786903bed6f0742383508e7de103f6a
+文本长度: 0 字符
+切片大小: 500 字符
+切片数量: 0 个
+

data/pdf_chunks/page_2.txt

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+=== 第 2 页切片结果 ===
+图片: page_2.png
+MD5: 5a0da54af84e8f0a099ce6b22e5d870d
+文本长度: 1858 字符
+切片大小: 500 字符
+切片数量: 4 个
+
+--- Chunk 0 ---
+长度: 500 字符
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+"
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+"
+!!!!!!!!!
+
+--- Chunk 1 ---
+长度: 500 字符
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+--- Chunk 2 ---
+长度: 500 字符
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pyproject.toml

@@ -3,5 +3,47 @@ name = "backend"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
-requires-python = ">=3.9"
-dependencies = []
+requires-python = ">=3.12"
+dependencies = [
+    # Web框架
+    "fastapi>=0.110.0",
+    "uvicorn>=0.27.0",
+    # LangGraph & LangChain
+    "langgraph>=0.0.40",
+    "langchain>=0.2.0",
+    "langchain-community>=0.2.0",
+    # DashScope
+    "dashscope>=1.14.0",
+    # Milvus
+    "pymilvus>=2.3.0",
+    # MinIO
+    "minio>=7.1.0",
+    # PostgreSQL
+    "sqlalchemy>=2.0.0",
+    "psycopg2-binary>=2.9.0",
+    # Redis (optional, for caching)
+    "redis>=5.0.0",
+    # 文档解析
+    "pypdf2>=3.0.0",
+    "python-docx>=1.1.0",
+    "pdfplumber>=0.10.0",
+    "pdf2image>=1.16.0",
+    "pillow>=11.3.0",
+    "pymupdf>=1.26.5",
+    "pytesseract>=0.3.13",
+    # Pydantic配置
+    "pydantic>=2.0.0",
+    "pydantic-settings>=2.0.0",
+    # 工具
+    "python-multipart>=0.0.9",
+    "sse-starlette>=1.8.0",
+    "python-dotenv>=1.0.0",
+    "tiktoken>=0.5.0",
+    "httpx>=0.25.0",
+]
+
+[dependency-groups]
+dev = [
+    "pytest>=7.4.0",
+    "pytest-asyncio>=0.21.0",
+]

requirements.txt

@@ -13,10 +13,21 @@ dashscope>=1.14.0
 # Milvus
 pymilvus>=2.3.0
 
+# MinIO
+minio>=7.1.0
+
+# PostgreSQL
+sqlalchemy>=2.0.0
+psycopg2-binary>=2.9.0
+
+# Redis (optional, for caching)
+redis>=5.0.0
+
 # 文档解析
 pypdf2>=3.0.0
 python-docx>=1.1.0
 pdfplumber>=0.10.0
+pdf2image>=1.16.0
 
 # Pydantic配置
 pydantic>=2.0.0