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…

We are happy to announce the 1.0.0 Milestone 2 release of Spring AI.

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

Key Focus: Observability

A major emphasis for this release has been on observability functionality, crucial for monitoring, debugging, and optimizing AI applications. Comprehensive observability features have been introduced for:

• ChatClient (including Advisors)
• Chat Models (OpenAI, Ollama, Mistral, Anthropic)
• Embedding Models
• Image Generation Models
• Vector Stores

Note: Support for additional ChatModel implementation will be added in future…

We are happy to announce the 1.0.0 Milestone 1 release of Spring AI.

This release has the following new features an improvements.

ChatClient Fluent API

The fluent API provides methods to construct a Prompt, which is then passed as input to the AI model. You create a ChatClient using the ChatClient.Builder object. You can obtain an auto-configured ChatClient.Builder from Spring Boot autoconfiguration or create one programmatically.

If you're familiar with other Spring client classes like WebClient, RestClient, and JdbcClient, this will feel familiar.

Here is a simple usage example

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We are happy to announce the milestone 0.8.1 release of Spring AI. Thanks for all the community contributions and great discussion on GitHub issues.

This release has the following new features and improvements.

Google Gemini

Google recently released its Gemini AI Model. It has many exciting features and functionalities; check their release blog for an overview.

Features supported in Spring AI are:

• One of Gemini's marquee features is its multimodal support. This lets you chat with the model using images, videos, and text. For example, you can send the model a photo of a plate of cookies and ask it to give you a recipe for those cookies. Spring AI supports this feature; read more about it here and take a look at the sample application that showcases many Gemini features.
• Streaming and Non-Streaming chat clients
• Function Calling
• Native compilation of Gemini applications
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We are happy to announce the first milestone release of Spring AI, version 0.8.0. A big thanks to the over 50 community members and employees in other companies who have contributed to the project. Your work is very much appreciated!

The Spring AI project was founded based on the realization that creating AI applications was no longer the sole domain of Machine Learning or AI Specialists, who typically use Python. With the meteoric rise in popularity of ChatGPT, which provides a simple Web API to access pre-trained models, creating compelling AI applications has become accessible to software…