servers/Retrieve/readme.md

Bio RAG Server

A FastAPI-based Biomedical RAG service that supports PubMed retrieval, web search, and vector DB queries, providing intelligent Q&A and document retrieval with streaming responses.

🚀 Features

• Multi-source retrieval: PubMed, Web search, personal vector DBs
• Intelligent Q&A: RAG-based answers with streaming SSE responses
• Query rewrite: Smart multi-query and rewrite to improve recall and precision
• Primary/backup LLM: Automatic failover between main and backup providers
• Internationalization: Chinese/English responses (87 i18n messages, 8 categories)
• Logging & tracing: Full request tracing with correlation IDs
• CORS: Easy frontend integration

🏗️ Project Structure (partial)

bio_rag_server/
├── bio_agent/
├── bio_requests/
├── config/
├── dto/
├── routers/
├── search_service/
├── service/
├── utils/
└── test/

📋 Requirements

• Python 3.11+
• LLM providers (OpenAI-compatible or others per your config)

🛠️ Setup

1) Install dependencies

pip install -r requirements.txt

2) Configure environment

Create a .env file (see env_example.txt for keys):

• QA_LLM_MAIN_API_KEY, QA_LLM_MAIN_BASE_URL
• QA_LLM_BACKUP_API_KEY, QA_LLM_BACKUP_BASE_URL
• REWRITE_LLM_MAIN_API_KEY, REWRITE_LLM_MAIN_BASE_URL
• REWRITE_LLM_BACKUP_API_KEY, REWRITE_LLM_BACKUP_BASE_URL
• SERPER_API_KEY (web search)
• ENVIRONMENT (e.g., dev)

3) Run the service

python main.py

Service runs at http://localhost:9487.

Run with Docker

docker build -t bio-rag-server .
docker run --rm -p 9487:9487 --env-file .env bio-rag-server

Note: The Dockerfile pre-installs crawl4ai and runs basic setup checks during build.

📚 API

1) Document Retrieval

Endpoint: POST /retrieve

Request body:

{
  "query": "cancer treatment",
  "top_k": 5,
  "search_type": "keyword",
  "is_rewrite": true,
  "data_source": ["pubmed"],
  "user_id": "user123",
  "pubmed_topk": 30
}

Response (example):

[
  {
    "title": "Cancer Treatment Advances",
    "abstract": "Recent advances in cancer treatment...",
    "url": "https://pubmed.ncbi.nlm.nih.gov/...",
    "score": 0.95
  }
]

2) Streaming Chat (RAG)

Endpoint: POST /stream-chat

Request body:

{
  "query": "What are the latest treatments for breast cancer?",
  "is_web": true,
  "is_pubmed": true,
  "language": "en"
}

Response: Server-Sent Events (SSE) streaming

3) Internationalization

All APIs support i18n via the language field:

• zh (default)
• en

Success response shape:

{
  "success": true,
  "data": [...],
  "message": "Search successful",
  "language": "en"
}

Error response shape:

{
  "success": false,
  "error": {
    "code": 500,
    "message": "Search failed",
    "language": "en",
    "details": "..."
  }
}

📊 Monitoring & Logs

• Log files: logs/bio_rag_YYYY-MM-DD.log
• Correlation ID tracing per request
• Processing time recorded via middleware

🔒 Security

• API key and endpoint configuration via environment variables
• Request logging
• CORS enabled
• Error handling with safe messages

🤝 Contributing

1. Fork
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit (git commit -m 'Add some AmazingFeature')
4. Push (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

MIT (see LICENSE).

🆘 Support

1. Check Issues
2. Open a new Issue
3. Contact maintainers

🗺️ Roadmap

• More data sources
• Auth & permissions
• Vector search optimization
• More LLM providers
• Result caching
• API rate limiting

---

Note: Ensure all required API keys and provider endpoints are configured before use.