How to Build a Context-Aware Spring AI Chatbot Microservice with Gemini and H2

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Large Language Models like Google Gemini are stateless by default, meaning they have no memory of previous messages within a conversation. A tutorial published on DEV Community demonstrates how to overcome this limitation by building a Spring Boot microservice that retains conversation context across requests. The solution uses Spring AI's MessageChatMemoryAdvisor to inject chat history into each outgoing prompt, with conversation data persisted in an H2 file-based database rather than volatile in-memory storage. Unique user sessions are tracked via an HTTP session header, ensuring individual conversations remain isolated from one another. The setup relies on key dependencies including Spring AI's Gemini starter, JDBC-backed chat memory modules, and the H2 database driver configured through application properties.

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