Context aware Spring AI chat microservice

A developer demonstrated how to build a context-aware Spring Boot chat microservice that connects to Google's Gemini API using Spring AI's ChatMemory Advisor to maintain conversation history. The implementation persists chat sessions across application restarts by backing the memory with a file-based H2 database via JdbcChatMemoryRepository, keyed to unique user sessions.

By default, Large Language Models LLMs are completely stateless . Every API call you make to Google Gemini is treated like a first-time introduction. If a user says " My name is Alex " in request one, and " What is my name? " in request two, Gemini will not know the answer. To build a true chatbot experience, your microservice needs to remember the conversation history. In this post, we will build a Spring Boot microservice that connects to the Gemini API , uses Spring AI's ChatMemory Advisor to maintain context, and backs up that history using a persistent H2 database mapped to unique user sessions. The Architecture Add the required dependencies to your pom.xml. This includes the Spring AI starter for Gemini, the chat memory module, and the H2 database driver. <dependency <groupId org.springframework.boot</groupId <artifactId spring-boot-h2console</artifactId </dependency <dependency <groupId org.springframework.ai</groupId <artifactId spring-ai-starter-model-chat-memory</artifactId </dependency <dependency <groupId org.springframework.ai</groupId <artifactId spring-ai-starter-model-chat-memory-repository-jdbc</artifactId </dependency <dependency <groupId org.springframework.ai</groupId <artifactId spring-ai-starter-model-google-genai</artifactId </dependency Configure Properties : Update the application.properties with chat model and get your API key from Google AI Studio https://aistudio.google.com/apikey spring.ai.model.chat=google-genai spring.ai.google.genai.api-key=API-KEY-GOES-HERE spring.ai.google.genai.chat.model=gemini-3.5-flash Instead of losing chat history on application restarts which happens with default in-memory arrays , we configure a persistent ChatMemory bean backed by H2 using the JdbcChatMemoryRepository. python import org.springframework.ai.chat.memory.ChatMemory; import org.springframework.ai.chat.memory.MessageWindowChatMemory; import org.springframework.ai.chat.memory.repository.jdbc.H2ChatMemoryRepositoryDialect; import org.springframework.ai.chat.memory.repository.jdbc.JdbcChatMemoryRepository; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.jdbc.core.JdbcTemplate; @Configuration public class ChatConfig { @Bean public JdbcChatMemoryRepository chatMemoryRepository JdbcTemplate jdbcTemplate { // Enforces H2 syntax quirks for database execution return JdbcChatMemoryRepository.builder .jdbcTemplate jdbcTemplate .dialect new H2ChatMemoryRepositoryDialect .build ; } @Bean public ChatMemory chatMemory JdbcChatMemoryRepository repository { return MessageWindowChatMemory.builder .chatMemoryRepository repository .build ; } } Configure properties : include the below in application.properties to maintain the session across application restarts and to enable h2-console H2 Database configuration File-based storage to persist across restarts spring.datasource.url=jdbc:h2:file:~/data/demochat;DB CLOSE ON EXIT=FALSE spring.datasource.driverClassName=org.h2.Driver spring.datasource.username=sa spring.datasource.password=password Enable H2 Console to view your tables manually at http://localhost:8080/h2-console spring.h2.console.enabled=true spring.h2.console.path=/h2-console Spring AI - Auto-initialize the schema for H2 spring.ai.chat.memory.repository.jdbc.initialize-schema=always We build the ChatClient and attach the MessageChatMemoryAdvisor. This advisor acts as an interceptor: it fetches past messages from H2 before sending the prompt to Gemini, and saves Gemini's response right after. python import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor; import org.springframework.ai.chat.memory.ChatMemory; import org.springframework.stereotype.Service; @Service public class ChatService { private final ChatClient chatClient; public ChatService ChatClient.Builder chatClientBuilder, ChatMemory chatMemory { this.chatClient = chatClientBuilder.defaultAdvisors MessageChatMemoryAdvisor.builder chatMemory .build .build ; } public String getResponse String sessionId, String queryString { return this.chatClient.prompt queryString .advisors x - x.param ChatMemory.CONVERSATION ID, sessionId .call .content ; } } Finally, we expose a POST endpoint. We use @RequestHeader "x-session-id" to extract the unique tracking key provided by the client application. python import ai.chat bot.gemini.demo.service.ChatService; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.web.bind.annotation. ; @RestController @RequestMapping "/api" public class ChatController { @Autowired private ChatService chatService; @PostMapping "/ai/prompt" public String chat @RequestHeader "x-session-id" String sessionId, @RequestBody String query { return chatService.getResponse sessionId, query ; } } You can test the context awareness using curl or Postman. Request establishing a context : curl --location --request POST 'http://localhost:8080/api/ai/prompt' \ --header 'x-session-id: 8757788243' \ --header 'Content-Type: application/json' \ --data-raw '{ "query": "I love mangoes" }' Response: Mangoes are absolutely elite There is a very good reason they are called the "King of Fruits." There is nothing quite like a perfectly ripe, juicy, sweet mango. Request to verify history recall curl --location --request POST 'http://localhost:8080/api/ai/prompt' \ --header 'x-session-id: 8757788243' \ --header 'Content-Type: application/json' \ --data-raw '{ "query": "what do you think is my favorite fruit" }' Response: I’m going to go out on a limb here and make a wild guess... mangoes ? 🥭 😉 Call it a hunch, but you seemed pretty enthusiastic about them a moment ago If you send the second request with a different session-id value, Gemini will correctly respond that it doesn't know and can give a generic response proving that multi-user session isolation is fully working . By combining Spring AI's Advisor API with an H2 JDBC store , you can turn a stateless LLM endpoint into a fully stateful, context-aware chatbot microservice with minimal boilerplate code. You can view the source code directly in my GitHub Repository https://github.com/deepakmarneni/context-aware-chat-ai