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

# 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