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README.md

@@ -3,35 +3,12 @@ title: RAG Engine
 emoji: 🧠
 colorFrom: blue
 colorTo: indigo
-sdk: streamlit
-sdk_version: 1.36.0
-app_file: app.py
+sdk: static
 pinned: true
 ---
 
 # RAG Engine 🧠
 
-A modular, educational **Retrieval-Augmented Generation (RAG)** system built to demonstrate core RAG principles:
-1. **Document Ingestion & Chunking** (sliding window with overlap)
-2. **Dense Vector Embeddings** (cosine similarity search)
-3. **Sparse BM25 Keyword Search** (lexical matching for exact terms)
-4. **Hybrid Retrieval with Reciprocal Rank Fusion (RRF)**
-5. **Context-Augmented Generation** with source citations
+Interactive demonstration of **Retrieval-Augmented Generation (RAG)** built from scratch to explain how modern RAG pipelines work.
 
-### 💻 Live Interactive Demo
-Use the tabs on the left to:
-- **💬 Ask Question**: Query the knowledge base with real-time source grounding and faithfulness scoring.
-- **🔬 Pipeline Inspector**: Directly observe the raw output of Vector Search vs. BM25 Search vs. RRF Rank Fusion.
-- **📚 Knowledge Base**: Inspect loaded documents or upload custom text/PDF files to index them immediately.
-
-### 📐 Architecture
-- **Chunking**: ~400-word passages with 50-word overlap to balance semantic focus and boundary continuity.
-- **Embeddings**: Deterministic token hash embeddings by default (100% offline, zero-dependency), compatible with HuggingFace SentenceTransformers.
-- **Hybrid Fusion**: Merges dense and sparse ranks via Reciprocal Rank Fusion:
-  RRF(d) = \sum_{m} \frac{w_m}{60 + \text{rank}_m(d)}
-- **Generation**: Formats grounded context into system prompts. Provides extractive summaries on CPU or full LLM generation when an API key is provided.
-
-### 🛠️ Transparency & Code
-- **Author**: Jayan Gupta
-- **Source Code & Unit Tests**: [GitHub Repository](https://github.com/JayanGupta/RAG-Engine)
-- **Design Philosophy**: Simple, clean Python without heavy abstraction layers or type boilerplate.
+- **GitHub Repository**: [JayanGupta/RAG-Engine](https://github.com/JayanGupta/RAG-Engine) (Full Python pipeline, Streamlit dashboard, Jupyter notebook, and test suite).