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README.md

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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
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+📚 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!