LatestMachine LearningBuilding AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI 1 like February 20, 2025Share this postAuthor(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.A Step-by-Step Guide to Configuring Google Vertex AI, Leveraging the Gemini API, and Integrating Knowledge Bases for Intelligent Conversational ApplicationsPhoto by Annie Spratt on UnsplashAI is everywhere, and Googles Gemini API and Vertex AI make it easy to build smart applications. So, whats the deal with these two?The Gemini API gives you access to powerful AI models that can chat, answer questions, and create content. Meanwhile, Google Vertex AI is a cloud platform where you can build, run, and manage these models. Think of Vertex AI as your workspace and Gemini as the engine inside it. Together, they help you create and launch AI projects faster and easier.In this guide, Ill cover two key parts:Getting Started with Google Vertex AI: Youll learn how to set up your Vertex AI account, manage billing, and use essential tools and commands to access models like Gemini.Building an AI Chatbot Example: Ill show you how to create a chatbot using Gemini, LangChain, RAG, Flask, and a database, connecting a knowledge base with vector embeddings for fast retrieval and semantic search. Youll also learn how RAG (Retrieval-Augmented Generation) combines search results with Geminis responses Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post