📄 恢复RAG发动机
人工智能驱动的智能简历搜索 -上传PDF文件，提出问题，通过源引用获得智能答案。
  ](https://www.docker.com/)
🎯 概述
一个可用于生产的简历发现API，它使用语义搜索和LLM支持的问答来查找和分析简历。采用隐私优先设计，包括PII编辑和匿名ID。
flowchart LR
subgraph Input
PDF[📄 PDF Resume]
Q[❓ Question]
end
subgraph Processing
Extract[🤖 Gemini
Extract Fields]
Chunk[✂️ Semantic
Chunker]
Embed[🔢 HuggingFace
Embeddings]
Search[🔍 Vector
Search]
Rerank[📊 Reranker]
Answer[💬 Gemini
Answer]
end
subgraph Storage
DB[(ChromaDB)]
Redis[(Redis Queue)]
end
PDF --> Extract --> Chunk --> Embed --> DB
Q --> Search --> DB
DB --> Rerank --> Answer
PDF -.-> Redis -.-> Extract✨ 特性
- 🔍 语义搜索 -按含义查找简历，而不仅仅是关键字
- 🤖 人工智能问答 -询问关于候选人的自然语言问题
- 📄 PDF处理 -从简历中提取文本和元数据
- 🔒 隐私第一 -PII编辑和匿名候选人ID
- ⚡ 异步处理 -用于批量上传的可选Redis队列
- 🎯 多跳检索 -复杂的查询分解以获得更好的答案
- 📊 置信度分数 -了解每个答案的可靠性
🚀 快速开始
选项1:Docker（推荐）
# 1. Clone the repository
git clone https://github.com/your-repo/mcp-resume-engine.git
cd mcp-resume-engine
# 2. Create .env file
echo "GEMINI_API_KEY=your_gemini_key" > .env
echo "HUGGINGFACE_API_TOKEN=your_hf_token" >> .env
# 3. Start with Docker Compose
docker-compose up --build
# 4. Open API docs
# http://localhost:8000/docs方案2：地方发展
# 1. Create virtual environment
python -m venv .venv
.venv\Scripts\activate # Windows
source .venv/bin/activate # Linux/Mac
# 2. Install dependencies
pip install -r requirements.txt
# 3. Set environment variables
export GEMINI_API_KEY=your_key
export HUGGINGFACE_API_TOKEN=your_token
# 4. Run the server
uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000📁 项目结构
mcp-resume-engine/
├── backend/
│ ├── core/ # Text processing & semantic chunking
│ │ ├── chunker.py # Section-aware resume chunking
│ │ └── preprocessor.py # Text cleaning & normalization
│ ├── embeddings/ # Vector embeddings & storage
│ │ ├── embedder.py # HuggingFace API embeddings
│ │ └── vectorstore.py # ChromaDB vector store
│ ├── retrieval/ # Search & reranking
│ │ ├── reranker.py # Result reranking with skill matching
│ │ ├── multihop.py # Multi-hop retrieval for complex queries
│ │ └── verifier.py # Confidence scoring
│ ├── queue/ # Redis queue infrastructure
│ │ ├── worker.py # Background job processor
│ │ └── jobs.py # Job definitions
│ ├── main.py # FastAPI endpoints
│ ├── llm.py # Gemini LLM integration
│ ├── models.py # Pydantic data models
│ └── anonymizer.py # PII redaction
├── docker-compose.yml # Docker orchestration
├── Dockerfile # Container build
├── requirements.txt # Python dependencies
└── README.md🔌 API终点
| 方法 | 端点 | 描述 |
|---|---|---|
POST | /ingest_pdf | 上传并处理简历PDF |
POST | /qa | 询问有关简历的问题 |
GET | /health | 使用队列状态进行健康检查 |
GET | /resumes | 列出所有已处理的简历 |
DELETE | /resumes/{id} | 删除简历 |
上传简历
curl -X POST "http://localhost:8000/ingest_pdf" \
-F "file=@resume.pdf"答复：
{
"status": "processed",
"id": "abc123",
"name": "John Doe",
"skills": ["Python", "React", "AWS"],
"chunk_count": 8
}提问
curl -X POST "http://localhost:8000/qa" \
-H "Content-Type: application/json" \
-d '{"question": "Who has Python and machine learning experience?", "top_k": 5}'答复：
{
"answer": "John Doe has extensive Python and ML experience...",
"confidence_score": 0.92,
"sources": [...],
"is_fallback": false
}异步上传（批量处理）
curl -X POST "http://localhost:8000/ingest_pdf?async_mode=true" \
-F "file=@resume.pdf"退货 job_id 用于状态检查。
⚙️ 建筑
数据摄取管道
sequenceDiagram
participant User
participant API
participant LLM as Gemini LLM
participant HF as HuggingFace API
participant Vec as ChromaDB
User->>API: Upload PDF
API->>API: Extract text & strip PII
API->>LLM: Extract fields (name, skills, projects)
API->>API: Semantic chunking by sections
API->>HF: Generate embeddings
HF->>API: 384-dim vectors
API->>Vec: Store chunks + metadata
API->>User: ✓ Resume processed问答管道
sequenceDiagram
participant User
participant API
participant HF as HuggingFace API
participant Vec as ChromaDB
participant LLM as Gemini LLM
User->>API: Ask question
API->>API: Expand query
API->>HF: Embed question
HF->>API: Query vector
API->>Vec: Semantic search (top 15)
Vec->>API: Candidate chunks
API->>API: Rerank by skills & sections
API->>LLM: Generate answer with context
LLM->>API: Natural language answer
API->>User: Answer + confidence + sources🐳 Docker编写服务
services:
api: # FastAPI server (port 8000)
redis: # Job queue (port 6379)
worker: # Background processors (5 replicas)缩放工人：
docker-compose up --scale worker=10🔧 环境变量
| 变量 | 必填 | 描述 |
|---|---|---|
GEMINI_API_KEY | ✅ | 用于LLM的Google Gemini API密钥 |
HUGGINGFACE_API_TOKEN | ✅ | 用于嵌入的HuggingFace API令牌 |
ALLOWED_ORIGINS | ❌ | CORS源（默认值： *) |
REDIS_URL | ❌ | Redis连接URL（在Docker中自动配置） |
📊 技术栈
| 组件 | 技术 |
|---|---|
| API框架 | FastAPI+Pydantic |
| LLM | 谷歌双子座2.5 Flash |
| 嵌入 | HuggingFace推理API（MiniLM-L6-v2） |
| 矢量存储 | ChromaDB |
| 队列 | Redis+RQ |
| 容器 | Docker+Docker组合 |
🔒 隐私功能
- PII补救措施：电话号码、电子邮件、地址自动屏蔽
- 匿名ID：基于哈希的候选标识符
- 无原始存储：不存储原始PDF，只存储处理过的文本
📝 许可证
麻省理工学院