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| title: Full RAG Assistant | |
| emoji: 💻 | |
| colorFrom: purple | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 6.0.1 | |
| app_file: app.py | |
| pinned: false | |
| 📚 RAG AI Assistant — Document-Aware Chatbot Powered by Retrieval-Augmented Generation | |
| Welcome to the RAG AI Assistant, a lightweight, open-source document question-answering system that lets you chat with your own knowledge base. | |
| Upload documents → Rebuild the index → Ask questions → Get grounded, explainable answers sourced from your files. | |
| Built with: | |
| FAISS for fast vector search | |
| Sentence Transformers for embeddings | |
| Transformers QA pipeline for extraction | |
| Gradio Blocks UI for clean chat + KB management | |
| 🚀 Features | |
| 🧠 Retrieval-Augmented Generation (RAG) | |
| The assistant retrieves relevant document chunks and uses a QA model to produce accurate, grounded answers. | |
| 📂 Knowledge Base Uploads | |
| You can upload your own documents directly inside the Space: | |
| Supported formats: | |
| txt, md, pdf, docx, doc | |
| ⚙️ Rebuildable FAISS Index | |
| After uploading files, click Rebuild index to update the vector store instantly — no need to restart the Space. | |
| 💬 Interactive Chat | |
| Ask free-form questions about your uploaded documents. | |
| The model will: | |
| Retrieve relevant context | |
| Extract answers | |
| Show confidence scores | |
| Cite the source document | |
| 🔍 Full Transparency | |
| If no answer is found, you’ll receive helpful suggestions and context previews. | |
| 🛠️ How It Works | |
| 1. Chunking | |
| Documents are split into overlapping chunks: | |
| Size: 500 characters | |
| Overlap: 50 characters | |
| 2. Embedding | |
| Each chunk is embedded with: | |
| sentence-transformers/all-MiniLM-L6-v2 | |
| 3. Vector Search | |
| FAISS (IndexFlatIP) finds the closest matches using cosine similarity. | |
| 4. Answer Extraction | |
| A QA model extracts precise answers: | |
| deepset/roberta-base-squad2 | |
| 🧪 Usage Instructions | |
| 1. Upload Your Documents | |
| Go to the Knowledge Base tab and upload as many files as you want. | |
| 2. Rebuild the Index | |
| Click Rebuild index to process the files and generate embeddings. | |
| 3. Start Asking Questions | |
| Switch to the Chat tab and ask questions like: | |
| What is the main topic of the report? | |
| Summarize the key findings. | |
| What does section 3 say about metrics? | |
| 📁 Project Structure | |
| ├── app.py # Main application logic | |
| ├── config.yaml # Optional configuration file | |
| ├── knowledge_base/ # User-uploaded documents | |
| ├── index/ # Saved FAISS index + metadata | |
| └── README.md # This file | |
| 🧩 Tech Stack | |
| Python 3.10 | |
| Gradio | |
| FAISS | |
| Sentence Transformers | |
| Transformers (HuggingFace) | |
| PyPDF2 / python-docx | |
| 🧱 Roadmap (Upcoming Enhancements) | |
| 🔄 Streaming responses (LLM-style typing) | |
| 📊 Document preview inside the chat | |
| 📝 Source highlighting of extracted spans | |
| 🎨 Theming + cleaner chat UI | |
| ⚡ Optional lightweight QA model for faster inference | |
| Feel free to suggest improvements or contribute! |