README

## 🦙 Local RAG Agent with Llama 3.2

### 🎓 FREE Step-by-Step Tutorial 
**👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-rag-agent) 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
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
```

2. Install the required dependencies:

```bash
cd awesome-llm-apps/rag_tutorials/local_rag_agent
pip install -r requirements.txt
```

3. Install and start [Qdrant](https://qdrant.tech/) vector database locally

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

4. Install [Ollama](https://ollama.com/download) and pull Llama 3.2 for LLM and OpenHermes as the embedder for OllamaEmbedder
```bash
ollama pull llama3.2
ollama pull openhermes
```

5. Run the AI RAG Agent 
```bash
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.