awesome-llm-apps/rag_tutorials/local_rag_agent

🦙 Local RAG Agent with Llama 3.2

This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database.

Features

• Fully local RAG implementation
• Powered by Llama 3.2 through Ollama
• Vector search using Qdrant
• Interactive playground interface
• No external API dependencies

How to get Started?

1. Clone the GitHub repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

2. Install the required dependencies:

cd rag_tutorials/local_rag_agent
pip install -r requirements.txt

3. Install and start Qdrant vector database locally

docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant

4. Install Ollama and pull Llama 3.2

ollama pull llama3.2

4. Run the AI RAG Agent

python local_rag_agent.py

5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.