Let's build a "Chat with your Code" RAG app using Qwen3-Coder:

Before we begin, take a look at what we're about to create! Tech stack: - @Llama_Index for orchestration - @Milvusio to self-host a vectorDB - @CleanlabAI codex to validate the response - @OpenRouterAI to access @Alibaba_Qwen 3 Coder. Let's go! 🚀

The architecture diagram presented below illustrates some of the key components & how they interact with each other! It will be followed by detailed descriptions & code for each component:

1️⃣ & 2️⃣ : Loading the knowledge base A knowledge base is a collection of relevant and up-to-date information that serves as a foundation for RAG. In our case it's a GitHub repository! Here's how we chunk & parse our code base using @Llama_Index's hierarchical code parser:

3️⃣ The embedding model Embedding is a meaningful representation of text in form of numbers. The embedding model is responsible for creating embeddings for the document chunks & user queries. Here's how we load our embedding model:

4️⃣ Indexing & storing Embeddings created by embedding model are stored in a vector store that offers fast retrieval and similarity search by creating an index over our data. We'll use a self-hosted @Milvusio vector database:

5️⃣ Creating a prompt template A custom prompt template is use to refine the response from LLM & include the context as well:

6️⃣ Setting up a query engine The query engine takes query string to use it to fetch relevant context and combines them using the prompt template before sending it to the LLM that generates final response! The LLM used here is the latest Qwen3-Coder!

7️⃣ The Chat interface We create a UI using Streamlit to provide a chat interface for our RAG application. The code for this & all we discussed so far is shared in the next tweet! Check this out👇

Bonus! We will use @CleanlabAI's AI codex, a smart way to validate and improve your responses. We've used the same for getting the trustworthiness score. Seamlessly integrates with any agentic or AI chat application you're developing. Check this out👇

You can find all the code in this GitHub repo: (don't forget to star 🌟)

Finally, I'll leave you with the architecture diagram of the app we've built. Hope you enjoyed this tutorial. Stay tuned for more! 🥂

If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!