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Spring AI gives Spring Boot applications a common way to work with generative-AI models, embeddings, vector stores, retrieval, and tools. The first decision is version compatibility: Spring AI 2.0.x is documented for Spring Boot 4.0.x and 4.1.x. From there, a sound design usually starts with a model interaction through ChatClient, adds retrieval when answers need application data, and exposes tools only when the application must perform controlled operations.
Choose a compatible Spring AI and Spring Boot line first
Spring AI’s Getting Started documentation identifies Spring AI 2.0.1 as stable and states that “Spring AI 2.0.x supports Spring Boot 4.0.x and 4.1.x.” The same documentation lists 1.1.8 as stable for the preceding line and 2.1.0-M1 as a preview. These are release facts from the documentation, not a guarantee that the same versions remain current indefinitely; check the release and compatibility guidance when creating or upgrading a project.
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Use Spring Initializr to select the model integration and any vector-store integration you need. Spring AI releases are available through Maven Central, and the project’s BOM manages recommended Spring AI dependency versions. Prefer the BOM and the component-specific starter or module for your selected release over copying a standalone coordinate from an older tutorial.
| Spring AI line identified in the Getting Started documentation | Spring Boot compatibility stated there | Status stated there |
|---|---|---|
| 2.0.x | 4.0.x and 4.1.x | 2.0.1 listed as stable |
| 1.1.x | Not stated in the cited compatibility statement | 1.1.8 listed as stable for the prior line |
| 2.1.x | Not stated in the cited compatibility statement | 2.1.0-M1 listed as preview |
Do not infer that every example on a documentation page uses the newest patch release: the Getting Started examples show a Spring AI BOM version of 2.0.0 while the page identifies 2.0.1 as stable. Confirm the BOM and artifact versions recommended for the release you are actually using.
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What Spring AI contributes to a Spring application
Spring AI supplies portable APIs for common AI application work: chat, image generation, audio transcription, text-to-speech, embeddings, and vector stores. It also includes a fluent ChatClient, advisors for recurring interaction patterns, tool calling, Model Context Protocol (MCP) integration, Spring Boot auto-configuration and starters, and building blocks for preparing data for retrieval-augmented generation (RAG).
The value is a familiar Spring integration layer and a shared shape for common operations—not a promise that every provider behaves identically. The chosen provider and model determine available capabilities, and Spring AI permits access to provider-specific features when an application needs them. Check a model’s supported modalities, options, and deployment constraints before designing around a capability.
Use ChatClient for ordinary model interactions
For an application that needs to send a prompt and receive a response, begin with the Spring AI chat abstraction and its fluent ChatClient API. It keeps the interaction in the Spring ecosystem and can be used for synchronous responses or streaming, depending on the application’s needs and the selected provider’s support.
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A useful design boundary is to keep application behavior outside the prompt-building code. Put model selection and provider configuration in application configuration; put prompt construction and response handling in a service; and keep validation, authorization, persistence, and other business rules in ordinary application components. That separation makes it easier to test the parts that should remain deterministic and to change providers without treating prompt text as the whole application.
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- Choose synchronous interaction when the caller needs a complete response before proceeding.
- Choose streaming when the user interface benefits from displaying generated output as it arrives, and verify support through the selected model integration.
- Check model-specific features such as modality or provider options instead of assuming that an abstraction makes them universal.
Ground answers in application data with RAG
RAG gives a model relevant external context at answer time. In Spring AI’s documented QuestionAnswerAdvisor flow, the application searches a vector store for documents related to the user’s question and adds those results to the user text as context for the model. The flow assumes the documents have already been loaded into a VectorStore.
Prepare and ingest the source material
Start with the information the application is allowed to use: for example, approved help content or internal reference documents. Prepare it as retrievable documents, create embeddings as required by the chosen integration, and load the documents and associated data into a vector store. Spring AI’s ETL building blocks can support data-loading workflows. The quality and currency of this material matter: retrieval cannot supply information that was never included or correctly prepared.
Retrieve relevant records for each question
At query time, search the vector store for records related to the user’s question. Spring AI’s VectorStore API provides similarity search and portable SQL-like metadata filters. Similarity search helps find semantically related material; filters can narrow retrieval using metadata when the application needs boundaries such as a content category or tenant.
If a retrieval-only component should not be able to change the index, the read-only VectorStoreRetriever interface can express that narrower access. Select the actual vector-store provider and its filtering behavior for the application’s deployment and data needs.
Supply retrieved context and assess the answer
For a small, straightforward question-and-answer flow, use QuestionAnswerAdvisor; its documented dependency is spring-ai-vector-store-advisor. For a more composable retrieval pipeline, Spring AI provides RetrievalAugmentationAdvisor through the spring-ai-rag dependency. Choose between them based on how much control the application needs over retrieval and augmentation rather than treating either as a guarantee of answer quality.
RAG is a way to provide context, not a factuality guarantee. Retrieval can return irrelevant, incomplete, or stale records, and the model can still produce an answer that is not supported by the retrieved text. Evaluate both retrieval quality and generated answers against representative questions; where correctness matters, design appropriate review, abstention, or escalation behavior.
Keep tool execution under application control
Tools let a model request an operation, such as looking up information or initiating an application action. Spring AI supports declarative methods annotated with @Tool as well as programmatic method and function callbacks. In either case, the model can request a tool and provide arguments; application code performs the operation and returns its result to the model. The model does not receive direct access to the implementation behind the tool.
This division is important for security and correctness. Treat tool arguments as untrusted input: validate them, enforce authorization in application code, and apply the same safeguards you would use for a normal API request. For operations with side effects, decide explicitly which requests may proceed, whether confirmation is needed, and how failures are reported. A tool declaration is an interface for a model to request work, not permission to bypass application policy.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11ToolContext lets the application pass internal data, such as a tenant or user identifier, to a tool method at invocation time without sending that data to the model. Use this pattern where private application context is required for execution but should not be included in model-visible prompt content.
Account for the Spring AI 2.0 tool loop
In Spring AI 2.0, the documented ChatClient tool loop is organized through ToolCallingAdvisor. A caller using the lower-level ChatModel API can drive the tool cycle itself; do not assume the older 1.x behavior in which a ChatModel caller automatically receives the complete tool loop. Consult the version-specific tool-calling reference for configuration and exact behavior.
Plan upgrades from Spring AI 1.1.x deliberately
The 2.0 upgrade notes record changes that can affect dependencies and behavior. For example, spring-ai-advisors-vector-store was renamed to spring-ai-vector-store-advisor, starter naming changed, and optional tool-search advisor support was added.
The documented starter patterns are spring-ai-starter-model-{model} for model starters and spring-ai-starter-vector-store-{store} for vector-store starters. These patterns are useful when identifying the right artifact family, but they are not a substitute for checking the complete upgrade notes and the artifact names for a specific release. A 2.0 dependency choice should not be presented as a drop-in migration for a 1.x project.
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- Compatibility: align the Spring AI line with the Spring Boot version before selecting integrations.
- Provider and model: select for the capabilities and deployment the application needs, then verify provider-specific behavior.
- Interaction style: use synchronous calls for request-response work or streaming where incremental output is useful and supported.
- Retrieval design: use a simple question-answer advisor for a direct flow or a modular RAG advisor when retrieval needs more composition.
- Vector store: choose based on the application’s storage, similarity-search, metadata-filtering, and access-control requirements.
- Tool authority: determine which operations the model may request, while keeping validation and execution in trusted application code.
Further learning
For a book-length companion to the Spring documentation, Manning’s Spring AI in Action by Craig Walls is written for Java developers familiar with Spring and Spring Boot. Its publisher describes coverage including RAG, tools, chat memory, image and voice generation, observability, security, and agents. Treat a book as structured learning alongside the release-specific documentation, particularly for version-sensitive configuration.
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