How to build a Google GenAI chat app using Spring AI and Gemini

# Overview This guide explains how to integrate Google's Gemini generative models into a Spring Boot application using Spring AI. It covers what Spring AI provides, how a GenAI chat interaction flows through a typical Spring app, the dependency and configuration you need, and the runtime prerequisites for calling Gemini models.

# Java developers Spring AI is not a model provider. It supplies Spring-style APIs, auto-configuration, and reusable abstractions to communicate with supported AI providers. That means you keep familiar patterns such as dependency injection and configuration properties while avoiding tight coupling to raw provider APIs.

Concrete capabilities Spring AI offers:

• Chat model integration via ChatClient and related components.
• Prompt creation and management with structured response handling.
• Conversation handling and state support so chat sessions behave predictably.
• Embeddings and vector-database integration for Retrieval-Augmented Generation (RAG).

# How a GenAI chat request flows

1. 1User submits a prompt to the application (for example, via an HTTP POST).
2. 2REST controller receives the prompt and forwards it to a GenAiService.
3. 3GenAiService uses Spring AI's ChatClient to prepare and send the request to the configured Gemini model.
4. 4Gemini analyzes the prompt and returns a generated response.
5. 5ChatClient passes the model response back to the service and REST controller, which returns it to the user.

This flow keeps AI integration logic encapsulated in service and client components while controllers remain small and focused on transport concerns.

# Project setup and dependencies Start with a Spring Boot project and include the Spring AI Google GenAI starter dependency:

spring-ai-starter-model-google-genai

Using the Spring AI BOM helps keep module versions compatible across Spring AI components.

# Authentication and runtime prerequisites You need access to Google's Gemini models. Spring AI's Google GenAI integration accepts either a Gemini Developer API key or Google Cloud credentials (Vertex AI). Ensure the appropriate credentials and project configuration are available at runtime before invoking model calls.

Also verify you have a working Java toolchain (Java and Maven) and an IDE or build system to run and test the Spring Boot app.

# Example components to implement

• GenAiService: encapsulate ChatClient usage, transform requests and responses, and manage conversation context.
• Configuration properties: model selection, timeouts, authentication method, and any RAG/vector DB settings.

# When to use Spring AI patterns Use Spring AI when you want to: keep your application code provider-agnostic, integrate RAG or embeddings, manage conversational state within a Spring idiom, or use streaming and tool-calling features without implementing low-level API plumbing.

# Related learning paths If you plan to expand beyond simple chat, follow up with materials on RAG with Spring Boot, long-term memory (AutoMemoryTools), and other Spring AI tutorials that cover embeddings, vector DB integration, and conversation memory.

# Bottom line Spring AI gives Java developers a familiar Spring framework for integrating Google Gemini models. Add the Google GenAI starter, provide the correct credentials, and structure your app around controllers and a GenAiService that delegates prompts to Spring AI's ChatClient. This keeps provider-specific details out of your core application code and makes it easier to adopt advanced AI patterns like RAG and streaming responses.