Added new tutorial

rag_tutorials/local_rag_agent/README.md

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+## 🦙 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
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+cd 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
+```bash
+ollama pull llama3.2
+
+```
+
+4. Run the AI RAG Agent 
+```bash
+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.
+
+