Building AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI

Author(s): Shenggang Li

Originally published on Towards AI.

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A Step-by-Step Guide to Configuring Google Vertex AI, Leveraging the Gemini API, and Integrating Knowledge Bases for Intelligent Conversational Applications

Photo by Annie Spratt on Unsplash

AI is everywhere, and Google’s Gemini API and Vertex AI make it easy to build smart applications. So, what’s 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, I’ll cover two key parts:

Getting Started with Google Vertex AI: You’ll 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: I’ll 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. You’ll also learn how RAG (Retrieval-Augmented Generation) combines search results with Gemini’s responses… Read the full blog for free on Medium.

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Published via Towards AI