Building a Chatbot with Llama Stack

Throughout our learning process, we’ve explored the different building blocks of Llama Stack: running inference, using external tools, adding retrieval, applying safety shields, and managing agents. Now, we’ll combine all of that into a single application.

Think of it like assembling a superhero. A hero needs a brain (our LLM), special powers to interact with the world (tools), and a strong moral compass to keep them in check (our safety shield). Our job is to be the chief engineer, putting all these pieces together.

• 🧠 Hold a natural, intelligent conversation.

• 🧮 Perform complex calculations using Wolfram Alpha.

• 🌐 Access up-to-the-minute information from the internet.

• 🛡️ Automatically block and filter unsafe or inappropriate prompts.

• ✨ All wrapped in a slick, interactive web interface you can share!

Ready to build? Let’s get started.

The blueprint: our chatbot’s architecture

Before we write a single line of code, let’s look at the plan. Every component has a specific job, and they all work together under the command of our “Agent.”

• The brain (LLM): This is the core of our chatbot, responsible for understanding language, reasoning, and generating human-like responses. We’ll use a powerful model hosted on Together AI.

• The superpowers (Tools): An LLM’s knowledge is frozen in time. To make our chatbot truly useful, we’ll give it tools:

  • Web search: This will allow the agent to “look things up” on the internet to answer questions about recent events or find specific information.

  • Wolfram Alpha: This is our math and data wizard, perfect for precise calculations and structured data queries that LLMs can sometimes struggle with.

• The moral compass (safety shield): We’ll add Llama Guard to act as a security checkpoint. It will inspect both user inputs and the chatbot’s own generated responses to ensure the conversation stays safe and on-topic.

• The public face (Gradio UI): Finally, we’ll wrap our entire system in a simple, clean Gradio web interface so we can easily chat with our creation.

This would be a good place to test your understanding and knowledge of Llama Stack. We will provide a breakdown of each task, and you will write the code for it.

Let’s build this step by step.

Step 1: Setting the stage and connecting to our LLM

First, we need to connect to our LLM. We’ll use the Together AI API, which provides a simple and reliable way to access powerful models without needing to host them ourselves. This connection is managed through a client object.

Your task: Create a client instance pointing to the Together AI URL https://llama-stack.together.ai and configure it with your API key.