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# 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
```bash
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
```bash
python main.py
```
Service runs at `http://localhost:9487`.
### Run with Docker
```bash
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:
```json
{
"query": "cancer treatment",
"top_k": 5,
"search_type": "keyword",
"is_rewrite": true,
"data_source": ["pubmed"],
"user_id": "user123",
"pubmed_topk": 30
}
```
Response (example):
```json
[
{
"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:
```json
{
"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:
```json
{
"success": true,
"data": [...],
"message": "Search successful",
"language": "en"
}
```
Error response shape:
```json
{
"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. |