Here’s a friendly guide to help you use a tool in your LiveKit agent that streams output from a RAG (Retrieval-Augmented Generation) system to generate answers on the fly: 1. Define the RAG Tool: Start by creating a function with the

@function_tool

decorator that talks to your RAG system. This function will fetch the relevant info and get it ready for processing. 2. Integrate RAG Handler: Next, set up a RAG handler in your agent. This will manage the whole RAG workflow—loading the Annoy index and data, handling any delays, and making responses richer. 3. Use the RAG Tool in the Agent: Add some logic to your agent so it can enhance responses using the context the RAG handler retrieves. This also includes streaming the response back to the user smoothly. 4. Handle Streaming Output: Use the

agent.say()

method along with the LLM stream to send real-time responses right to your user. 5. Initialize the Agent with RAG: Finally, make sure your agent is all set up with the RAG components, including any plugins and configurations needed. If you want me to walk you through any step or need more details, just let me know—I’m here to help!