Deploying a multimodal RAG application with Gemma 3 and CircleCI on GKE

Retrieval-Augmented Generation (RAG) is a transformative approach for enhancing interactions with Large Language Models (LLMs) by grounding responses in external knowledge to improve accuracy and reduce errors. Traditional RAG systems are limited to text processing, but multimodal RAG overcomes this by integrating text, images, and potentially audio, creating a more comprehensive understanding similar to human sensory integration. This tutorial guides the construction of a multimodal RAG application using Google’s Gemma 3 model served via Ollama, which processes PDF documents containing both text and images. The application employs Qdrant as a vector store, creates an interactive UI with Streamlit, and is deployed to Google Kubernetes Engine (GKE) using CircleCI, resulting in a scalable system that allows users to query document content irrespective of format. The process involves setting up a GKE cluster, creating Docker containers, and deploying services using Kubernetes, while CircleCI automates the build and deployment pipeline to ensure a streamlined workflow.

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