After reviewing our API design and deprecations post M7, we determined that several significant changes would become breaking changes in an RC1 release. Therefore, we've introduced this additional milestone to provide developers with a transitional release where deprecated APIs coexist alongside their replacements, enabling a smoother upgrade experience before RC1.

Here are the key changes in this release.

Important Links

• Upgrade Notes
• You can automate the upgrade process to 1.0.0-M8 using an OpenRewrite recipe. This recipe helps apply many of the necessary code changes for this version. Find the recipe and usage instructions at Arconia Spring AI Migrations.
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We are excited to announce the release of Spring AI 1.0.0 Milestone 7. This will be the last milestone release. Next month will be the RC1 release followed quickly by a GA release in time for the Spring IO conference in Barcelona.

To celebrate this release, we have added a new song to our AI-generated music playlist featuring lyrics by Josh Long and Claude! Check out the latest track to enhance your blog reading and coding experience.

Here are the key changes in this release. Note, there are breaking changes!

Breaking Changes

Spring AI 1.0.0-M7 introduces several important changes that align…

This blog post is authored by Eddú Meléndez.

Docker recently released a Model Runner in Docker Desktop for Mac 4.40.0 on Apple silicon. The Docker Model Runner provides a local Inference API designed to be compatible with the OpenAI API, enabling easy integration with Spring AI as part of the Spring AI 1.0.0-M7 release. Models are distributed as standard OCI artifacts on Docker Hub under the ai namespace.

Prerequisites

• Download Docker Desktop for Mac 4.40.0.

• Choose one of the following options to enable the Model Runner:

  Option 1:

• Enable Model Runner `docker desktop enable model-runner --tcp 12434`.

• Set the base-url to `http://localhost:12434/engines\`

  Option 2:

• Enable Model Runner `docker desktop enable model-runner`.

• Use Testcontainers and set the base-url as follows:

…

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…

Using Spring AI 1.0.0-SNAPSHOT: Important Changes and Updates

Spring AI 1.0.0-SNAPSHOT introduces several important changes to artifact IDs, dependency management, and autoconfiguration. This blog post outlines these changes and provides guidance on how to update your projects.

The most significant change is the naming pattern for Spring AI starter artifacts:

• Model starters: spring-ai-{model}-spring-boot-starter → spring-ai-starter-model-{model}
• Vector Store starters: spring-ai-{store}-store-spring-boot-starter → spring-ai-starter-vector-store-{store}
• MCP starters: spring-ai-mcp-{type}-spring-boot-starter → spring-ai-starter-mcp-{type}
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This blog post is co-authored with David Soria Parra, Christian Tzolov, and Dariusz Jędrzejczyk.

What is MCP

The Model Context Protocol (MCP), an open protocol developed by Anthropic, is transforming the way AI applications connect and share context. It has garnered extensive support across AI applications, functioning as a standardized interface for Large Language Models (LLMs) to interact with data sources, tools, and AI agents. Whether you're building autonomous systems that need to access databases, orchestrating complex AI workflows, or creating systems where multiple agents collaborate…

We are excited to announce the release of Spring AI 1.0.0 Milestone 6. To celebrate this release, we have created a special AI-generated music playlist to enhance your blog reading and coding experiences!

As usual, this release includes several new features and bug fixes. We have continued to focus on reviewing the codebase from a design perspective. While we have tried to make this transition smooth by deprecating methods and classes for one release cycle, there are some breaking changes we know about and potentially some that we don't, so please bear with us. See the Breaking Changes section…

We are happy to announce the 1.0.0 Milestone 5 release of Spring AI

As usual, this release has had several new features and bug fixes, however we have put a focus on reviewing the code base from a design perspective with the aim to go GA in early 2025. While we have tried to make this transition smooth, by deprecating methods and classes for one release cycle, there are some breaking changes we know about and perhaps some that we don’t, so please bear with us. See the section on Breaking Changes below.

New Features

Model Context Protocol (MCP) Support - Incubating

MCP is a game-changer for…

We are happy to announce the 1.0.0 Milestone 4 release of Spring AI.

This release has squashed most of the reported bugs and brings significant enhancements across various areas.

Amazon Bedrock Converse

Spring AI now supports the Amazon Bedrock Converse API, which provides a unified interface for the AI chat models that Amazon offers. Unlike the older Bedrock Invoke API, Converse introduces exciting new features such as Tool Calling and Multimodality/Visual capabilities (for models that support these features). This makes it a more powerful and versatile option for working with Amazon’s chat…

We are happy to announce the 1.0.0 Milestone 3 release of Spring AI.

This release brings significant enhancements and new features across various areas.

Observability

This release introduces many refinements to the observability stack, particularly for streaming responses from Chat Models. Many thanks to Thomas Vitale and Dariusz Jedrzejczyk for all their help in this area!

Observability covers the ChatClient, ChatModel, Embedding Models and Vector stores enabling you to view all touchpoints with your AI infrstructure in fine grained detail..

In the M2 release we introduced observability…