README

🦙 Local RAG Agent with Llama 3.2

🎓 FREE Step-by-Step Tutorial

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database. Built with Agno v2.0.

Features

• Fully local RAG implementation
• Powered by Llama 3.2 through Ollama
• Vector search using Qdrant
• Interactive AgentOS interface
• No external API dependencies
• Uses Agno v2.0 Knowledge class for document management

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 awesome-llm-apps/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 for LLM and OpenHermes as the embedder for OllamaEmbedder

ollama pull llama3.2
ollama pull openhermes

5. Run the AI RAG Agent

python local_rag_agent.py

6. Open your web browser and navigate to the URL provided in the console output (typically http://localhost:7777) to interact with the RAG agent through the AgentOS interface.

Note

• The knowledge base loads a Thai Recipes PDF on the first run. You can comment out the knowledge_base.add_content() line after the first run to avoid reloading.
• The AgentOS interface provides a web-based UI for interacting with your agent.