Initial commit: RAG demo with build and query scripts

README.md

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+# RAG Demo
+
+Retrieval Augmented Generation using LlamaIndex with local models.
+
+This demo builds a semantic search system over a collection of text documents
+using a HuggingFace embedding model and Ollama for generation.
+
+## Tutorial
+
+See the full walkthrough at:
+https://lem.che.udel.edu/wiki/index.php?n=Main.RAG
+
+## Quick Start
+
+```bash
+# Create and activate virtual environment
+python3 -m venv .venv
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Pull the generating model
+ollama pull command-r7b
+
+# Place your .txt documents in ./data, then build the vector store
+python build.py
+
+# Run interactive queries
+python query.py
+```
+
+## Models
+
+- **Embedding:** BAAI/bge-large-en-v1.5 (downloaded automatically on first run)
+- **Generation:** command-r7b via Ollama