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| title: LightRAG Practical Guide | |
| emoji: 📚 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: 1.31.0 | |
| app_file: app.py | |
| pinned: false | |
| # LightRAG Practical Guide for Students | |
| This Hugging Face Space is a practical implementation of the **LightRAG** framework using LangChain and Streamlit. | |
| ## Features | |
| - **PDF Upload**: Load your own documents for analysis. | |
| - **Configurable Parameters**: Adjust chunk size, overlap, and top-k retrieval. | |
| - **Real-time Feedback**: View processing messages and retrieved text chunks. | |
| - **Knowledge Graph**: Visualize the relationships between document chunks in a dedicated tab. | |
| - **Local LLM**: Powered by `TinyLlama-1.1B-Chat` for efficient local inference. | |
| ## How to use | |
| 1. Upload a PDF file in the sidebar. | |
| 2. Wait for the indexing to finish (you'll see progress messages). | |
| 3. Ask questions in the "Q&A Interface" tab. | |
| 4. Explore the document structure in the "Knowledge Graph" tab. | |