Meta just released multilingual and multimodal open-source LLMs. Today, we're building a RAG app powered by @Meta's Llama4. Tech stack: - @Llama_Index for orchestration. - @CerebrasSystems for blazing-fast Llama4 inference. - @Streamlit for the UI. Let's build it!

Before we dive into the code, here's a diagram that illustrates the key components & how they interact with each other! It will be followed by detailed descriptions & code for each component:

1️⃣ Setup LLM Llama 4 is available on Cerebras, and you can use it for free. Here's how you integrate it with LlamaIndex:

2️⃣ Setup embedding model We'll use @nomic_ai's embedding model, which provides some of the most powerful open-source embedding models. Check this out👇

3️⃣ Create the workflow For orchestration, we'll use the LlamaIndex Workflow. It's an event-driven, step-based method to control the execution flow of an app. A start event triggers the workflow, and a stop event terminates it. Here's what it looks like👇

This workflow will two paths: Ingestion and Retrieval: - Path 1: Start→ Ingest data → End - Path 2: Start→ Retrieve data → Generate response → End Here's the implement it (full code in the GitHub repo)👇

4️⃣ Visualize the workflow If needed, you can automatically visualize the orchestrated workflow using the following code. It's helpful for documentation purposes and also to understand how the app works 👇

6️⃣ Kickoff the workflow Finally, we kick off the workflow. Check this out👇

7️⃣ The Chat interface We create a UI using @streamlit to provide a chat interface for our RAG application. Check this out👇

That's a wrap! If you enjoyed this tutorial: Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.