Build a production-ready RAG agent with Google Agent Development Kit (ADK), Gemini, and Cloud Run

This guide demonstrates how to build a production-ready Retrieval-Augmented Generation (RAG) agent using Google's Agent Development Kit (ADK), Gemini API, and Cloud Run. The tutorial covers creating a Streamlit-based RAG application, integrating it with Gemini for intelligent responses, and deploying it to Google Cloud Run for scalable production use. Developers can leverage this approach to create AI agents that retrieve and process information from custom knowledge bases while maintaining enterprise-grade reliability.

Key Points

• •Use Google Agent Development Kit (ADK) to scaffold and manage RAG agent development with built-in best practices
• •Integrate Gemini API as the LLM backbone for natural language understanding and generation in RAG workflows
• •Build Streamlit frontend for interactive user interface to query and interact with the RAG agent
• •Implement retrieval mechanisms to fetch relevant documents/data from knowledge bases before generating responses
• •Deploy RAG agent to Google Cloud Run for serverless, auto-scaling production deployment
• •Leverage Cloud Run's container-based architecture for easy CI/CD integration and version management
• •Configure authentication and API credentials securely for Gemini and Cloud services
• •Test agent responses locally before deploying to ensure quality and accuracy of RAG pipeline

Workflow Diagram

Concepts