| # Quick Start Guide | |
| ## Installation & Setup (5 minutes) | |
| ### 1. Install Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 2. Configure Azure OpenAI | |
| Create a `.env` file with your Azure OpenAI credentials: | |
| ```bash | |
| cp .env.example .env | |
| ``` | |
| Edit `.env`: | |
| ``` | |
| AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/ | |
| AZURE_OPENAI_API_KEY=your-api-key-here | |
| AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini | |
| ``` | |
| ### 3. Run the Application | |
| ```bash | |
| python app.py | |
| ``` | |
| Visit `http://localhost:7860` in your browser. | |
| ## First Query | |
| Try this example query: | |
| ``` | |
| Research Question: "What are the latest advances in multi-agent reinforcement learning?" | |
| Category: cs.AI - Artificial Intelligence | |
| Number of Papers: 3 | |
| ``` | |
| Click "Analyze Papers" and wait ~1-2 minutes. | |
| ## Expected Output | |
| You should see: | |
| 1. **Papers Tab**: Table with 3 retrieved papers | |
| 2. **Analysis Tab**: Detailed analysis of each paper | |
| 3. **Synthesis Tab**: | |
| - Executive summary | |
| - Consensus findings (green highlights) | |
| - Contradictions (yellow highlights) | |
| - Research gaps | |
| 4. **Citations Tab**: APA-formatted references | |
| 5. **Stats Tab**: Processing time and cost (~$0.20-0.40) | |
| ## Troubleshooting | |
| ### Error: "No module named 'xyz'" | |
| ```bash | |
| pip install -r requirements.txt --upgrade | |
| ``` | |
| ### Error: "Azure OpenAI authentication failed" | |
| - Check your `.env` file has correct credentials | |
| - Verify your Azure OpenAI deployment name matches your actual deployment | |
| ### Error: "Failed to download paper" | |
| - Some arXiv papers may have download issues | |
| - Try a different query or category | |
| ### Error: "ChromaDB error" | |
| ```bash | |
| rm -rf data/chroma_db/ | |
| # Restart the app | |
| ``` | |
| ## Architecture Overview | |
| ``` | |
| User Query | |
| ↓ | |
| Retriever Agent (arXiv search + PDF processing) | |
| ↓ | |
| Analyzer Agent (RAG-based analysis per paper) | |
| ↓ | |
| Synthesis Agent (Cross-paper comparison) | |
| ↓ | |
| Citation Agent (Validation + APA formatting) | |
| ↓ | |
| Gradio UI (4 output tabs) | |
| ``` | |
| ## Key Features | |
| - **Temperature=0**: Deterministic outputs | |
| - **RAG Grounding**: All claims backed by source text | |
| - **Semantic Caching**: Repeated queries use cache | |
| - **Cost Tracking**: Real-time cost estimates | |
| - **Error Handling**: Graceful failures with user-friendly messages | |
| ## Performance Benchmarks | |
| | Papers | Time | Cost | Chunks | | |
| |--------|------|------|--------| | |
| | 3 | ~90s | $0.25 | ~150 | | |
| | 5 | ~120s| $0.40 | ~250 | | |
| | 10 | ~180s| $0.75 | ~500 | | |
| ## Next Steps | |
| 1. **Customize Categories**: Edit `ARXIV_CATEGORIES` in `app.py` | |
| 2. **Adjust Chunking**: Modify `chunk_size` in `utils/pdf_processor.py` | |
| 3. **Change Top-K**: Update `top_k` in `rag/retrieval.py` | |
| 4. **Add Logging**: Increase log level in agents for debugging | |
| ## Deployment to Hugging Face | |
| ```bash | |
| # 1. Create a new Space on huggingface.co | |
| # 2. Upload all files | |
| # 3. Add secrets in Space settings: | |
| # - AZURE_OPENAI_ENDPOINT | |
| # - AZURE_OPENAI_API_KEY | |
| # - AZURE_OPENAI_DEPLOYMENT_NAME | |
| # 4. Space will auto-deploy | |
| ``` | |
| ## Support | |
| For issues: https://github.com/yourusername/Multi-Agent-Research-Paper-Analysis-System/issues | |