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.
+
+

rag_tutorials/local_rag_agent/local_rag_agent.py

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+# Import necessary libraries
+from phi.agent import Agent
+from phi.model.ollama import Ollama
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.qdrant import Qdrant
+from phi.embedder.ollama import OllamaEmbedder
+from phi.playground import Playground, serve_playground_app
+
+# Define the collection name for the vector database
+collection_name = "thai-recipe-index"
+
+# Set up Qdrant as the vector database with the embedder
+vector_db = Qdrant(
+    collection=collection_name,
+    url="http://localhost:6333/",
+    embedder=OllamaEmbedder()
+)
+
+# Define the knowledge base with the specified PDF URL
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=vector_db,
+)
+
+# Load the knowledge base, comment out after the first run to avoid reloading
+knowledge_base.load(recreate=True, upsert=True)
+
+# Create the Agent using Ollama's llama3.2 model and the knowledge base
+agent = Agent(
+    name="Local RAG Agent",
+    model=Ollama(id="llama3.2"),
+    knowledge=knowledge_base,
+)
+
+# UI for RAG agent
+app = Playground(agents=[agent]).get_app()
+
+# Run the Playground app
+if __name__ == "__main__":
+    serve_playground_app("local_rag_agent:app", reload=True)

rag_tutorials/local_rag_agent/requirements.txt

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+phidata
+qdrant-client
+ollama
+pypdf