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--- |
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title: Personal AI Assistant with RAG |
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emoji: 🤗 |
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colorFrom: indigo |
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colorTo: purple |
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sdk: docker |
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app_port: 7860 |
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pinned: true |
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license: mit |
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--- |
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# Personal AI Assistant with RAG |
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A powerful personal AI assistant that uses Retrieval-Augmented Generation (RAG) to provide responses based on your documents and notes. |
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## Features |
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- Uses free Hugging Face models for language processing and embeddings |
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- Stores and retrieves information in a vector database |
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- Upload PDF, TXT, and CSV files to expand the knowledge base |
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- Add direct text input to your knowledge base |
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- View sources for AI responses |
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- Conversation history tracking |
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## How to Use |
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1. **Upload Documents**: Use the sidebar to upload files (PDF, TXT, CSV) |
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2. **Add Text**: Enter text directly into the knowledge base |
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3. **Ask Questions**: Chat with the assistant about your documents |
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4. **View Sources**: See where information is coming from |
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## Deployment |
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### Local Deployment |
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To run the app locally: |
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1. Clone this repository |
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2. Install requirements: `pip install -r requirements.txt` |
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3. Run the Streamlit UI: `python run.py --ui` |
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4. Or run the API server: `python run.py --api` |
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### Deploying to Hugging Face Spaces |
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This application can be easily deployed to Hugging Face Spaces for free hosting: |
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1. Make sure you have a Hugging Face account |
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2. Create a Hugging Face API token at https://huggingface.co/settings/tokens |
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3. Run the deployment script: `python deploy_to_hf.py` |
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4. Follow the prompts to enter your username, token, and space name |
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5. Wait for the deployment to complete |
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If you encounter any issues during deployment, run `python check_git_status.py` to diagnose and fix common problems. |
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The deployment process: |
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- Creates a Hugging Face Space using the Spaces SDK |
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- Configures git for pushing to Hugging Face |
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- Pushes your code to the Space |
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- Builds and deploys the Docker container automatically |
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## Built With |
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- Hugging Face Models |
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- LLM: google/flan-t5-large |
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- Embeddings: sentence-transformers/all-MiniLM-L6-v2 |
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- LangChain for orchestration |
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- Qdrant for vector storage |
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- Streamlit for UI |
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Created by [p3rc03](https://huggingface.co/p3rc03) |
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## License |
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MIT License - See LICENSE file for details |
