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Using Spring AI 1.0.0-SNAPSHOT: Part 2 - Important Changes and Updates

This blog post is a continuation of our previous article Using Spring AI 1.0.0-SNAPSHOT: Important Changes and Updates, where we introduced the significant changes to artifact IDs, dependency management, and autoconfiguration in Spring AI 1.0.0-SNAPSHOT. Since publishing that article, the Spring team has released an updates to the snapshots, In this follow-up article, we’ll explore the latest changes and provide additional guidance for developers working with the snapshot versions. For comprehensive details, refer to the Spring AI Upgrade Notes.

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Module Restructuring

As of April 4, 2025, the main branch has undergone significant changes to the module and artifact structure. Previously, spring-ai-core contained all central interfaces, but this has now been split into specialized domain modules to reduce unnecessary dependencies in your applications.

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Package Name Changes

Some classes have moved to new packages:
  • ContentFormatTransformer and KeywordMetadataEnricher moved from org.springframework.ai.transformer to org.springframework.ai.chat.transformer
  • Content, MediaContent, and Media moved from org.springframework.ai.model to org.springframework.ai.content

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New Module Structure

Spring AI Dependencies

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spring-ai-commons

Base module with no dependencies on other Spring AI modules. Contains:
  • Core domain models (Document, TextSplitter)
  • JSON utilities and resource handling
  • Structured logging and observability support

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spring-ai-model

Provides AI capability abstractions:
  • Interfaces like ChatModel, EmbeddingModel, and ImageModel
  • Message types and prompt templates
  • Function-calling framework (ToolDefinition, ToolCallback)
  • Content filtering and observation support

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spring-ai-vector-store

Unified vector database abstraction:
  • VectorStore interface for similarity search
  • Advanced filtering with SQL-like expressions
  • SimpleVectorStore for in-memory usage
  • Batching support for embeddings

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spring-ai-client-chat

High-level conversational AI APIs:
  • ChatClient interface
  • Conversation persistence via ChatMemory
  • Response conversion with OutputConverter
  • Advisor-based interception
  • Synchronous and reactive streaming support

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spring-ai-advisors-vector-store

Bridges chat with vector stores for RAG:
  • QuestionAnswerAdvisor: injects context into prompts
  • VectorStoreChatMemoryAdvisor: stores/retrieves conversation history

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spring-ai-model-chat-memory-cassandra

Apache Cassandra persistence for ChatMemory:
  • CassandraChatMemory implementation
  • Type-safe CQL with Cassandra’s QueryBuilder

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spring-ai-model-chat-memory-neo4j

Neo4j graph database persistence for chat conversations.

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spring-ai-rag

Comprehensive framework for Retrieval Augmented Generation:
  • Modular architecture for RAG pipelines
  • RetrievalAugmentationAdvisor as main entry point
  • Functional programming principles with composable components

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Dependency Hierarchy

The new dependency structure follows this pattern:
  1. spring-ai-commons (foundation)
  2. spring-ai-model (depends on commons)
  3. spring-ai-vector-store and spring-ai-client-chat (both depend on model)
  4. spring-ai-advisors-vector-store and spring-ai-rag (depend on both client-chat and vector-store)
  5. spring-ai-model-chat-memory-* modules (depend on client-chat)

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Migration Guide

To migrate your existing Spring AI applications:
  1. If you are using the Spring Boot starters, you don’t need to do anything as you will pick up the new artifacts as necessary.
  2. Refactor imports for relocated classes (your IDE should assist with this)
  3. If using spring-ai-core directly, replace with the appropriate new modules

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Conclusion

These changes represent a significant improvement in the Spring AI architecture, allowing for more targeted dependencies and cleaner separation of concerns. By breaking the monolithic core into domain-specific modules, applications can now include only the functionality they need, resulting in smaller deployments and clearer boundaries between components. For more details, refer to the official Spring AI documentation.