Upgrade Notes

Upgrading to 1.0.0-SNAPSHOT

Overview

The 1.0.0-SNAPSHOT version includes significant changes to artifact IDs, package names, and module structure. This section provides guidance specific to using the SNAPSHOT version.

Add Snapshot Repositories

To use the 1.0.0-SNAPSHOT version, you need to add the snapshot repositories to your build file. For detailed instructions, refer to the Snapshots - Add Snapshot Repositories section in the Getting Started guide.

Update Dependency Management

Update your Spring AI BOM version to 1.0.0-SNAPSHOT in your build configuration. For detailed instructions on configuring dependency management, refer to the Dependency Management section in the Getting Started guide.

Artifact ID, Package, and Module Changes

The 1.0.0-SNAPSHOT includes changes to artifact IDs, package names, and module structure.

For details, refer to: - Common Artifact ID Changes - Common Package Changes - Common Module Structure

Automating upgrading using AI

You can automate the upgrade process to 1.0.0-SNAPSHOT using the Claude Code CLI tool with a provided prompt. The prompt will guide the AI to perform the following tasks:

Update the Spring AI BOM version to 1.0.0-SNAPSHOT
Ensure all required repositories exist in your build configuration
Update Spring AI artifact IDs according to the new naming patterns

To use this automation:

Download the Claude Code CLI tool
Copy the prompt from the update-to-snapshot.txt file
Paste the prompt into the Claude Code CLI
The AI will analyze your project and make the necessary changes

This approach can save time and reduce the chance of errors when upgrading multiple projects or complex codebases.

Upgrading to 1.0.0-M8

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.

Chat Client

The ChatClient has been enhanced to solve some inconsistencies or unwanted behavior whenever user and system prompts were not rendered before using them in an advisor. The new behavior ensures that the user and system prompts are always rendered before executing the chain of advisors. As part of this enhancement, the AdvisedRequest and AdvisedResponse APIs have been deprecated, replaced by ChatClientRequest and ChatClientResponse. Advisors now act on a fully built Prompt object included in a ChatClientRequest instead of the destructured format used in AdvisedRequest, guaranteeing consistency and completeness.

For example, if you had a custom advisor that modified the request prompt in the before method, you would refactor it as follows:

// --- Before (using AdvisedRequest) ---
@Override
public AdvisedRequest before(AdvisedRequest advisedRequest) {
    // Access original user text and parameters directly from AdvisedRequest
    String originalUserText = new PromptTemplate(advisedRequest.userText(), advisedRequest.userParams()).render();

    // ... retrieve documents, create augmented prompt text ...
    List<Document> retrievedDocuments = ...;
    String augmentedPromptText = ...; // create augmented text from originalUserText and retrievedDocuments

    // Copy existing context and add advisor-specific data
    Map<String, Object> context = new HashMap<>(advisedRequest.adviseContext());
    context.put("retrievedDocuments", retrievedDocuments); // Example key

    // Use the AdvisedRequest builder pattern to return the modified request
    return AdvisedRequest.from(advisedRequest)
        .userText(augmentedPromptText) // Set the augmented user text
        .adviseContext(context)        // Set the updated context
        .build();
}

// --- After (using ChatClientRequest) ---
@Override
public ChatClientRequest before(ChatClientRequest chatClientRequest, AdvisorChain chain) {
    String originalUserText = chatClientRequest.prompt().getUserMessage().getText(); // Access prompt directly

    // ... retrieve documents ...
    List<Document> retrievedDocuments = ...;
    String augmentedQueryText = ...; // create augmented text

    // Initialize context with existing data and add advisor-specific data
    Map<String, Object> context = new HashMap<>(chatClientRequest.context()); (1)
    context.put("retrievedDocuments", retrievedDocuments); // Example key
    context.put("originalUserQuery", originalUserText);   // Example key

    // Use immutable operations
    return chatClientRequest.mutate()
        .prompt(chatClientRequest.prompt()
            .augmentUserMessage(augmentedQueryText) (2)
        )
        .context(context) (3)
        .build();
}

1	Initialize the context map with data from the incoming request (`chatClientRequest.context()`) to preserve context from previous advisors, then add new data.
2	Use methods like `prompt.augmentUserMessage()` to modify the prompt content safely.
3	Pass the updated context map. This map becomes part of the `ChatClientRequest` and is accessible later via `ChatClientResponse.responseContext()` in the `after` method. The chain of advisors can populate the execution context with useful data. For example, when performing retrieval augmented generation, the retrieved documents can be added to the context. It’s now possible to return a `ChatClientResponse` object from a `ChatClient` call, which contains the execution context. So, besides the `content()` and `chatResponse()` methods, you can now terminate a `ChatClient` call with `chatClientResponse()` which gives you access to both the `ChatResponse` and the execution context.

In addition to directly replacing the user message text with augmentUserMessage(String), you can provide a function to modify the existing UserMessage more granularly:

Prompt originalPrompt = new Prompt(new UserMessage("Tell me about Large Language Models."));

// Example: Append context or modify properties using a Function
Prompt augmentedPrompt = originalPrompt.augmentUserMessage(userMessage ->
    userMessage.mutate()
        .text(userMessage.getText() + "\n\nFocus on their applications in software development.")
        // .media(...) // Potentially add/modify media
        // .metadata(...) // Potentially add/modify metadata
        .build()
);

// 'augmentedPrompt' now contains the modified UserMessage

This approach offers more control when you need to conditionally change parts of the UserMessage or work with its media and metadata, rather than just replacing the text content.

The overloaded tools methods in the ChatClient prompt builder API have been renamed for clarity and to avoid ambiguity in method dispatching based on argument types.
ChatClient.PromptRequestSpec#tools(String… toolNames) has been renamed to ChatClient.PromptRequestSpec#toolNames(String… toolNames). Use this method to specify the names of tool functions (registered elsewhere, e.g., via @Bean definitions with @Description) that the model is allowed to call.
ChatClient.PromptRequestSpec#tools(ToolCallback… toolCallbacks) has been renamed to ChatClient.PromptRequestSpec#toolCallbacks(ToolCallback… toolCallbacks). Use this method to provide inline ToolCallback instances, which include the function implementation, name, description, and input type definition.

This change addresses potential confusion where the Java compiler might not select the intended overload based on the provided arguments.

Prompt Templating and Advisors

Several classes and methods related to prompt creation and advisor customization have been deprecated in favor of more flexible approaches using the builder pattern and the TemplateRenderer interface. See PromptTemplate for details on the new API.

PromptTemplate Deprecations

The PromptTemplate class has deprecated several constructors and methods related to the older templateFormat enum and direct variable injection:

Constructors: PromptTemplate(String template, Map<String, Object> variables) and PromptTemplate(Resource resource, Map<String, Object> variables) are deprecated.
Fields: template and templateFormat are deprecated.
Methods: getTemplateFormat(), getInputVariables(), and validate(Map<String, Object> model) are deprecated.

Migration: Use the PromptTemplate.builder() pattern to create instances. Provide the template string via .template() and optionally configure a custom TemplateRenderer via .renderer(). Pass variables using .variables().

// Before (Deprecated)
PromptTemplate oldTemplate = new PromptTemplate("Hello {name}", Map.of("name", "World"));
String oldRendered = oldTemplate.render(); // Variables passed at construction

// After (Using Builder)
PromptTemplate newTemplate = PromptTemplate.builder()
    .template("Hello {name}")
    .variables(Map.of("name", "World")) // Variables passed during builder configuration
    .build();
Prompt prompt = newTemplate.create(); // Create prompt using baked-in variables
String newRendered = prompt.getContents(); // Or use newTemplate.render()

QuestionAnswerAdvisor Deprecations

The QuestionAnswerAdvisor has deprecated constructors and builder methods that relied on a simple userTextAdvise string:

Constructors taking a userTextAdvise String argument are deprecated.
Builder method: userTextAdvise(String userTextAdvise) is deprecated.

Migration: Use the .promptTemplate(PromptTemplate promptTemplate) builder method to provide a fully configured PromptTemplate object for customizing how retrieved context is merged.

// Before (Deprecated)
QuestionAnswerAdvisor oldAdvisor = QuestionAnswerAdvisor.builder(vectorStore)
    .userTextAdvise("Context: {question_answer_context} Question: {question}") // Simple string
    .build();

// After (Using PromptTemplate)
PromptTemplate customTemplate = PromptTemplate.builder()
    .template("Context: {question_answer_context} Question: {question}")
    .build();

QuestionAnswerAdvisor newAdvisor = QuestionAnswerAdvisor.builder(vectorStore)
    .promptTemplate(customTemplate) // Provide PromptTemplate object
    .build();

Chat Memory

A ChatMemory bean is auto-configured for you whenever using one of the Spring AI Model starters. By default, it uses the MessageWindowChatMemory implementation and stores the conversation history in memory.
The ChatMemory API has been enhanced to support a more flexible and extensible way of managing conversation history. The storage mechanism has been decoupled from the ChatMemory interface and is now handled by a new ChatMemoryRepository interface. The ChatMemory API now can be used to implement different memory strategies without being tied to a specific storage mechanism. By default, Spring AI provides a MessageWindowChatMemory implementation that maintains a window of messages up to a specified maximum size.
The get(String conversationId, int lastN) method in ChatMemory has been deprecated in favour of using MessageWindowChatMemory when it’s needed to keep messages in memory up to a certain limit. The get(String conversationId) method is now the preferred way to retrieve messages from the memory whereas the specific implementation of ChatMemory can decide the strategy for filtering, processing, and returning messages.
The JdbcChatMemory has been deprecated in favour of using JdbcChatMemoryRepository together with a ChatMemory implementation such MessageWindowChatMemory. If you were relying on an auto-configured JdbcChatMemory bean, you can replace that by auto-wiring a ChatMemory bean that is auto-configured to use the JdbcChatMemoryRepository internally for storing messages whenever the related dependency is in the classpath.
The spring.ai.chat.memory.jdbc.initialize-schema property has been deprecated in favor of spring.ai.chat.memory.repository.jdbc.initialize-schema.
Refer to the new Chat Memory documentation for more details on the new API and how to use it.
The MessageWindowChatMemory.get(String conversationId, int lastN) method is deprecated. The windowing size is now managed internally based on the configuration provided during instantiation, so only get(String conversationId) should be used.

Prompt Templating

The PromptTemplate API has been redesigned to support a more flexible and extensible way of templating prompts, relying on a new TemplateRenderer API. As part of this change, the getInputVariables() and validate() methods have been deprecated and will throw an UnsupportedOperationException if called. Any logic specific to a template engine should be available through the TemplateRenderer API.

Class Package Refactoring

Several classes have been moved to different modules and packages for better organization:

Evaluation classes moved:
- org.springframework.ai.evaluation.FactCheckingEvaluator moved to org.springframework.ai.chat.evaluation package within spring-ai-client-chat.
- org.springframework.ai.evaluation.RelevancyEvaluator moved to org.springframework.ai.chat.evaluation package within spring-ai-client-chat.
- org.springframework.ai.evaluation.EvaluationRequest, EvaluationResponse, and Evaluator moved from spring-ai-client-chat to spring-ai-commons under the org.springframework.ai.evaluation package.
Output converter classes moved:
- Classes within org.springframework.ai.converter (e.g., BeanOutputConverter, ListOutputConverter, MapOutputConverter, StructuredOutputConverter, etc.) moved from spring-ai-client-chat to spring-ai-model.
Transformer classes moved:
- org.springframework.ai.chat.transformer.KeywordMetadataEnricher moved to org.springframework.ai.model.transformer.KeywordMetadataEnricher in spring-ai-model.
- org.springframework.ai.chat.transformer.SummaryMetadataEnricher moved to org.springframework.ai.model.transformer.SummaryMetadataEnricher in spring-ai-model.
Utility classes moved:
- org.springframework.ai.util.PromptAssert moved from spring-ai-client-chat to org.springframework.ai.rag.util.PromptAssert in spring-ai-rag.

Please update your imports accordingly.

Observability

Changes to the spring.ai.client observation:
- The spring.ai.chat.client.tool.function.names and spring.ai.chat.client.tool.function.callbacks attributes have been deprecated, replaced by a new spring.ai.chat.client.tool.names attribute that includes the names of all the tools passed to a ChatClient, regardless of the underlying mechanism used to define them.
- The spring.ai.chat.client.advisor.params attribute has been deprecated and will not have a replacement. The reason is that there is a risk to expose sensitive information or break the instrumentation since the entries in the advisor context are used to pass arbitrary Java objects between advisors and are not necessarily serializable. The conversation ID that was previously exported here is now available via the dedicated spring.ai.chat.client.conversation.id attribute. If you need to export some of the other parameters in the advisor context to the observability system, you can do so by defining an ObservationFilter and making an explicit decision on which parameters to export. For inspiration, you can refer to the ChatClientPromptContentObservationFilter.
- The content of a prompt as specified via a ChatClient API was included optionally in the spring.ai.client observation, broken down in a few attributes: spring.ai.chat.client.user.text, spring.ai.chat.client.user.params, spring.ai.chat.client.system.text, spring.ai.chat.client.system.params. All those attributes are now deprecated, replaced by a single gen_ai.prompt attribute that contains all the messages in the prompt, solving the problem affecting the deprecated attributes where part of the prompt was not included in the observation, and aligning with the observations used in the ChatModel API. This new attribute can be enabled via the spring.ai.chat.observations.include-prompt configuration property, whereas the previous spring.ai.chat.observations.include-input configuration property is deprecated.
Changes to the spring.ai.advisor observation:
- The spring.ai.advisor.type attribute has been deprecated. In previous releases, the Advisor API was categorized based on the type of advisor (before, after, around). That distinction doesn’t apply anymore meaning that all Advisors are now of the same type (around).

Retrieval Augmented Generation

The DocumentPostProcessor API has been introduced to implement post-retrieval components in a Modular RAG architecture, superseding the DocumentCompressor, DocumentRanker, DocumentSelector APIs that are now deprecated.

Upgrading to 1.0.0-M7

Overview of Changes

Spring AI 1.0.0-M7 is the last milestone release before the RC1 and GA releases. It introduces several important changes to artifact IDs, package names, and module structure that will be maintained in the final release.

Artifact ID, Package, and Module Changes

The 1.0.0-M7 includes the same structural changes as 1.0.0-SNAPSHOT.

For details, refer to: - Common Artifact ID Changes - Common Package Changes - Common Module Structure

MCP Java SDK Upgrade to 0.9.0

Spring AI 1.0.0-M7 now uses MCP Java SDK version 0.9.0, which includes significant changes from previous versions. If you’re using MCP in your applications, you’ll need to update your code to accommodate these changes.

Key changes include:

Interface Renaming

ClientMcpTransport → McpClientTransport
ServerMcpTransport → McpServerTransport
DefaultMcpSession → McpClientSession or McpServerSession
All *Registration classes → *Specification classes

Server Creation Changes

Use McpServerTransportProvider instead of ServerMcpTransport

// Before
ServerMcpTransport transport = new WebFluxSseServerTransport(objectMapper, "/mcp/message");
var server = McpServer.sync(transport)
    .serverInfo("my-server", "1.0.0")
    .build();

// After
McpServerTransportProvider transportProvider = new WebFluxSseServerTransportProvider(objectMapper, "/mcp/message");
var server = McpServer.sync(transportProvider)
    .serverInfo("my-server", "1.0.0")
    .build();

Handler Signature Changes

All handlers now receive an exchange parameter as their first argument:

// Before
.tool(calculatorTool, args -> new CallToolResult("Result: " + calculate(args)))

// After
.tool(calculatorTool, (exchange, args) -> new CallToolResult("Result: " + calculate(args)))

Client Interaction via Exchange

Methods previously available on the server are now accessed through the exchange object:

// Before
ClientCapabilities capabilities = server.getClientCapabilities();
CreateMessageResult result = server.createMessage(new CreateMessageRequest(...));

// After
ClientCapabilities capabilities = exchange.getClientCapabilities();
CreateMessageResult result = exchange.createMessage(new CreateMessageRequest(...));

Roots Change Handlers

// Before
.rootsChangeConsumers(List.of(
    roots -> System.out.println("Roots changed: " + roots)
))

// After
.rootsChangeHandlers(List.of(
    (exchange, roots) -> System.out.println("Roots changed: " + roots)
))

For a complete guide to migrating MCP code, refer to the MCP Migration Guide.

Enabling/Disabling Model Auto-Configuration

The previous configuration properties for enabling/disabling model auto-configuration have been removed:

spring.ai.<provider>.chat.enabled
spring.ai.<provider>.embedding.enabled
spring.ai.<provider>.image.enabled
spring.ai.<provider>.moderation.enabled

By default, if a model provider (e.g., OpenAI, Ollama) is found on the classpath, its corresponding auto-configuration for relevant model types (chat, embedding, etc.) is enabled. If multiple providers for the same model type are present (e.g., both spring-ai-openai-spring-boot-starter and spring-ai-ollama-spring-boot-starter), you can use the following properties to select which provider’s auto-configuration should be active, effectively disabling the others for that specific model type.

To disable auto-configuration for a specific model type entirely, even if only one provider is present, set the corresponding property to a value that does not match any provider on the classpath (e.g., none or disabled).

You can refer to the SpringAIModels enumeration for a list of well-known provider values.

spring.ai.model.audio.speech=<model-provider|none>
spring.ai.model.audio.transcription=<model-provider|none>
spring.ai.model.chat=<model-provider|none>
spring.ai.model.embedding=<model-provider|none>
spring.ai.model.embedding.multimodal=<model-provider|none>
spring.ai.model.embedding.text=<model-provider|none>
spring.ai.model.image=<model-provider|none>
spring.ai.model.moderation=<model-provider|none>

Automating upgrading using AI

You can automate the upgrade process to 1.0.0-M7 using the Claude Code CLI tool with a provided prompt:

Download the Claude Code CLI tool
Copy the prompt from the update-to-m7.txt file
Paste the prompt into the Claude Code CLI
The AI will analyze your project and make the necessary changes

The automated upgrade prompt currently handles artifact ID changes, package relocations, and module structure changes, but does not yet include automatic changes for upgrading to MCP 0.9.0. If you’re using MCP, you’ll need to manually update your code following the guidance in the MCP Java SDK Upgrade section.

Common Changes Across Versions

Artifact ID Changes

The naming pattern for Spring AI starter artifacts has changed. You’ll need to update your dependencies according to the following patterns:

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}

Examples

Maven
Gradle

<!-- BEFORE -->
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

<!-- AFTER -->
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>

// BEFORE
implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
implementation 'org.springframework.ai:spring-ai-redis-store-spring-boot-starter'

// AFTER
implementation 'org.springframework.ai:spring-ai-starter-model-openai'
implementation 'org.springframework.ai:spring-ai-starter-vector-store-redis'

Changes to Spring AI Autoconfiguration Artifacts

The Spring AI autoconfiguration has changed from a single monolithic artifact to individual autoconfiguration artifacts per model, vector store, and other components. This change was made to minimize the impact of different versions of dependent libraries conflicting, such as Google Protocol Buffers, Google RPC, and others. By separating autoconfiguration into component-specific artifacts, you can avoid pulling in unnecessary dependencies and reduce the risk of version conflicts in your application.

The original monolithic artifact is no longer available:

<!-- NO LONGER AVAILABLE -->
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-spring-boot-autoconfigure</artifactId>
    <version>${project.version}</version>
</dependency>

Instead, each component now has its own autoconfiguration artifact following these patterns:

Model autoconfiguration: spring-ai-autoconfigure-model-{model}
Vector Store autoconfiguration: spring-ai-autoconfigure-vector-store-{store}
MCP autoconfiguration: spring-ai-autoconfigure-mcp-{type}

Examples of New Autoconfiguration Artifacts

Models
Vector Stores
MCP

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-model-openai</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-model-anthropic</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-model-vertex-ai</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-vector-store-redis</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-vector-store-pgvector</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-vector-store-chroma</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-mcp-client</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-autoconfigure-mcp-server</artifactId>
</dependency>

In most cases, you won’t need to explicitly add these autoconfiguration dependencies. They are included transitively when using the corresponding starter dependencies.

Package Name Changes

Your IDE should assist with refactoring to the new package locations.

KeywordMetadataEnricher and SummaryMetadataEnricher have moved from org.springframework.ai.transformer to org.springframework.ai.chat.transformer.
Content, MediaContent, and Media have moved from org.springframework.ai.model to org.springframework.ai.content.

Module Structure

The project has undergone significant changes to its 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.

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

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

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

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

spring-ai-advisors-vector-store

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

spring-ai-model-chat-memory-cassandra

Apache Cassandra persistence for ChatMemory: - CassandraChatMemory implementation - Type-safe CQL with Cassandra’s QueryBuilder ==== spring-ai-model-chat-memory-neo4j

Neo4j graph database persistence for chat conversations.

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

Dependency Structure

The dependency hierarchy can be summarized as:

spring-ai-commons (foundation)
spring-ai-model (depends on commons)
spring-ai-vector-store and spring-ai-client-chat (both depend on model)
spring-ai-advisors-vector-store and spring-ai-rag (depend on both client-chat and vector-store)
spring-ai-model-chat-memory-* modules (depend on client-chat)

ToolContext Changes

The ToolContext class has been enhanced to support both explicit and implicit tool resolution. Tools can now be:

Explicitly Included: Tools that are explicitly requested in the prompt and included in the call to the model.
Implicitly Available: Tools that are made available for runtime dynamic resolution, but never included in any call to the model unless explicitly requested.

Starting with 1.0.0-M7, tools are only included in the call to the model if they are explicitly requested in the prompt or explicitly included in the call.

Additionally, the ToolContext class has now been marked as final and cannot be extended anymore. It was never supposed to be subclassed. You can add all the contextual data you need when instantiating a ToolContext, in the form of a Map<String, Object>. For more information, check the [documentation](docs.spring.io/spring-ai/reference/api/tools.html#_tool_context).

Upgrading to 1.0.0-M6

Changes to Usage Interface and DefaultUsage Implementation

The Usage interface and its default implementation DefaultUsage have undergone the following changes:

Method Rename:
- getGenerationTokens() is now getCompletionTokens()
Type Changes:
- All token count fields in DefaultUsage changed from Long to Integer:
  - promptTokens
  - completionTokens (formerly generationTokens)
  - totalTokens

Required Actions

Replace all calls to getGenerationTokens() with getCompletionTokens()
Update DefaultUsage constructor calls:

// Old (M5)
new DefaultUsage(Long promptTokens, Long generationTokens, Long totalTokens)

// New (M6)
new DefaultUsage(Integer promptTokens, Integer completionTokens, Integer totalTokens)

For more information on handling Usage, refer here

JSON Ser/Deser changes

While M6 maintains backward compatibility for JSON deserialization of the generationTokens field, this field will be removed in M7. Any persisted JSON documents using the old field name should be updated to use completionTokens.

Example of the new JSON format:

{
  "promptTokens": 100,
  "completionTokens": 50,
  "totalTokens": 150
}

Changes to usage of FunctionCallingOptions for tool calling

Each ChatModel instance, at construction time, accepts an optional ChatOptions or FunctionCallingOptions instance that can be used to configure default tools used for calling the model.

Before 1.0.0-M6:

any tool passed via the functions() method of the default FunctionCallingOptions instance was included in each call to the model from that ChatModel instance, possibly overwritten by runtime options.
any tool passed via the functionCallbacks() method of the default FunctionCallingOptions instance was only made available for runtime dynamic resolution (see Tool Resolution), but never included in any call to the model unless explicitly requested.

Starting 1.0.0-M6:

any tool passed via the functions() method or the functionCallbacks() of the default FunctionCallingOptions instance is now handled in the same way: it is included in each call to the model from that ChatModel instance, possibly overwritten by runtime options. With that, there is consistency in the way tools are included in calls to the model and prevents any confusion due to a difference in behavior between functionCallbacks() and all the other options.

If you want to make a tool available for runtime dynamic resolution and include it in a chat request to the model only when explicitly requested, you can use one of the strategies described in Tool Resolution.

1.0.0-M6 introduced new APIs for handling tool calling. Backward compatibility is maintained for the old APIs across all scenarios, except the one described above. The old APIs are still available, but they are deprecated and will be removed in 1.0.0-M7.

Removal of deprecated Amazon Bedrock chat models

Starting 1.0.0-M6, Spring AI transitioned to using Amazon Bedrock’s Converse API for all Chat conversation implementations in Spring AI. All the Amazon Bedrock Chat models are removed except the Embedding models for Cohere and Titan.

Refer to Bedrock Converse documentation for using the chat models.

Changes to use Spring Boot 3.4.2 for dependency management

Spring AI updates to use Spring Boot 3.4.2 for the dependency management. You can refer here for the dependencies managed by Spring Boot 3.4.2

Required Actions

If you are upgrading to Spring Boot 3.4.2, please make sure to refer to this documentation for the changes required to configure the REST Client. Notably, if you don’t have an HTTP client library on the classpath, this will likely result in the use of JdkClientHttpRequestFactory where SimpleClientHttpRequestFactory would have been used previously. To switch to use SimpleClientHttpRequestFactory, you need to set spring.http.client.factory=simple.
If you are using a different version of Spring Boot (say Spring Boot 3.3.x) and need a specific version of a dependency, you can override it in your build configuration.

Vector Store API changes

In version 1.0.0-M6, the delete method in the VectorStore interface has been modified to be a void operation instead of returning an Optional<Boolean>. If your code previously checked the return value of the delete operation, you’ll need to remove this check. The operation now throws an exception if the deletion fails, providing more direct error handling.

Before 1.0.0-M6:

Optional<Boolean> result = vectorStore.delete(ids);
if (result.isPresent() && result.get()) {
    // handle successful deletion
}

In 1.0.0-M6 and later:

vectorStore.delete(ids);
// deletion successful if no exception is thrown

Upgrading to 1.0.0.M5

Vector Builders have been refactored for consistency.
Current VectorStore implementation constructors have been deprecated, use the builder pattern.
VectorStore implementation packages have been moved into unique package names, avoiding conflicts across artifact. For example org.springframework.ai.vectorstore to org.springframework.ai.pgvector.vectorstore.

Upgrading to 1.0.0.RC3

The type of the portable chat options (frequencyPenalty, presencePenalty, temperature, topP) has been changed from Float to Double.

Upgrading to 1.0.0.M2

The configuration prefix for the Chroma Vector Store has been changes from spring.ai.vectorstore.chroma.store to spring.ai.vectorstore.chroma in order to align with the naming conventions of other vector stores.
The default value of the initialize-schema property on vector stores capable of initializing a schema is now set to false. This implies that the applications now need to explicitly opt-in for schema initialization on supported vector stores, if the schema is expected to be created at application startup. Not all vector stores support this property. See the corresponding vector store documentation for more details. The following are the vector stores that currently don’t support the initialize-schema property.
1. Hana
2. Pinecone
3. Weaviate
In Bedrock Jurassic 2, the chat options countPenalty, frequencyPenalty, and presencePenalty have been renamed to countPenaltyOptions, frequencyPenaltyOptions, and presencePenaltyOptions. Furthermore, the type of the chat option stopSequences have been changed from String[] to List<String>.
In Azure OpenAI, the type of the chat options frequencyPenalty and presencePenalty has been changed from Double to Float, consistently with all the other implementations.

Upgrading to 1.0.0.M1

On our march to release 1.0.0 M1 we have made several breaking changes. Apologies, it is for the best!

ChatClient changes

A major change was made that took the 'old' ChatClient and moved the functionality into ChatModel. The 'new' ChatClient now takes an instance of ChatModel. This was done to support a fluent API for creating and executing prompts in a style similar to other client classes in the Spring ecosystem, such as RestClient, WebClient, and JdbcClient. Refer to the [JavaDoc](docs.spring.io/spring-ai/docs/api) for more information on the Fluent API, proper reference documentation is coming shortly.

We renamed the 'old' ModelClient to Model and renamed implementing classes, for example ImageClient was renamed to ImageModel. The Model implementation represents the portability layer that converts between the Spring AI API and the underlying AI Model API.

Adapting to the changes

The ChatClient class is now in the package org.springframework.ai.chat.client

Approach 1

Now, instead of getting an Autoconfigured ChatClient instance, you will get a ChatModel instance. The call method signatures after renaming remain the same. To adapt your code should refactor your code to change the use of the type ChatClient to ChatModel Here is an example of existing code before the change

@RestController
public class OldSimpleAiController {

    private final ChatClient chatClient;

    public OldSimpleAiController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @GetMapping("/ai/simple")
    Map<String, String> completion(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", this.chatClient.call(message));
    }
}

Now after the changes this will be

@RestController
public class SimpleAiController {

    private final ChatModel chatModel;

    public SimpleAiController(ChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/simple")
    Map<String, String> completion(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", this.chatModel.call(message));
    }
}

The renaming also applies to the classes * StreamingChatClient → StreamingChatModel * EmbeddingClient → EmbeddingModel * ImageClient → ImageModel * SpeechClient → SpeechModel * and similar for other <XYZ>Client classes

Approach 2

In this approach you will use the new fluent API available on the 'new' ChatClient

Here is an example of existing code before the change

@RestController
class OldSimpleAiController {

    ChatClient chatClient;

    OldSimpleAiController(ChatClient chatClient) {
        this.chatClient = chatClient;
	}

	@GetMapping("/ai/simple")
	Map<String, String> completion(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
		return Map.of(
                "generation",
				this.chatClient.call(message)
        );
	}
}

Now after the changes this will be

@RestController
class SimpleAiController {

    private final ChatClient chatClient;

    SimpleAiController(ChatClient.Builder builder) {
      this.chatClient = builder.build();
    }

    @GetMapping("/ai/simple")
    Map<String, String> completion(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of(
                "generation",
				this.chatClient.prompt().user(message).call().content()
        );
    }
}

The ChatModel instance is made available to you through autoconfiguration.

Approach 3

There is a tag in the GitHub repository called [v1.0.0-SNAPSHOT-before-chatclient-changes](github.com/spring-projects/spring-ai/tree/v1.0.0-SNAPSHOT-before-chatclient-changes) that you can check out and do a local build to avoid updating any of your code until you are ready to migrate your code base.

git checkout tags/v1.0.0-SNAPSHOT-before-chatclient-changes

./mvnw clean install -DskipTests

Artifact name changes

Renamed POM artifact names: - spring-ai-qdrant → spring-ai-qdrant-store - spring-ai-cassandra → spring-ai-cassandra-store - spring-ai-pinecone → spring-ai-pinecone-store - spring-ai-redis → spring-ai-redis-store - spring-ai-qdrant → spring-ai-qdrant-store - spring-ai-gemfire → spring-ai-gemfire-store - spring-ai-azure-vector-store-spring-boot-starter → spring-ai-azure-store-spring-boot-starter - spring-ai-redis-spring-boot-starter → spring-ai-starter-vector-store-redis

Upgrading to 0.8.1

Former spring-ai-vertex-ai has been renamed to spring-ai-vertex-ai-palm2 and spring-ai-vertex-ai-spring-boot-starter has been renamed to spring-ai-vertex-ai-palm2-spring-boot-starter.

So, you need to change the dependency from

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-vertex-ai</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-vertex-ai-palm2</artifactId>
</dependency>

and the related Boot starter for the Palm2 model has changed from

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-vertex-ai-spring-boot-starter</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-vertex-ai-palm2-spring-boot-starter</artifactId>
</dependency>

Renamed Classes (01.03.2024)
- VertexAiApi → VertexAiPalm2Api
- VertexAiClientChat → VertexAiPalm2ChatClient
- VertexAiEmbeddingClient → VertexAiPalm2EmbeddingClient
- VertexAiChatOptions → VertexAiPalm2ChatOptions

Upgrading to 0.8.0

January 24, 2024 Update

Moving the prompt and messages and metadata packages to subpackages of org.springframework.ai.chat
New functionality is text to image clients. Classes are OpenAiImageModel and StabilityAiImageModel. See the integration tests for usage, docs are coming soon.
A new package model that contains interfaces and base classes to support creating AI Model Clients for any input/output data type combination. At the moment, the chat and image model packages implement this. We will be updating the embedding package to this new model soon.
A new "portable options" design pattern. We wanted to provide as much portability in the ModelCall as possible across different chat based AI Models. There is a common set of generation options and then those that are specific to a model provider. A sort of "duck typing" approach is used. ModelOptions in the model package is a marker interface indicating implementations of this class will provide the options for a model. See ImageOptions, a subinterface that defines portable options across all text→image ImageModel implementations. Then StabilityAiImageOptions and OpenAiImageOptions provide the options specific to each model provider. All options classes are created via a fluent API builder, all can be passed into the portable ImageModel API. These option data types are used in autoconfiguration/configuration properties for the ImageModel implementations.

January 13, 2024 Update

The following OpenAi Autoconfiguration chat properties have changed

from spring.ai.openai.model to spring.ai.openai.chat.options.model.
from spring.ai.openai.temperature to spring.ai.openai.chat.options.temperature.

Find updated documentation about the OpenAi properties: docs.spring.io/spring-ai/reference/api/chat/openai-chat.html

December 27, 2023 Update

Merge SimplePersistentVectorStore and InMemoryVectorStore into SimpleVectorStore * Replace InMemoryVectorStore with SimpleVectorStore

December 20, 2023 Update

Refactor the Ollama client and related classes and package names

Replace the org.springframework.ai.ollama.client.OllamaClient by org.springframework.ai.ollama.OllamaModelCall.
The OllamaChatClient method signatures have changed.
Rename the org.springframework.ai.autoconfigure.ollama.OllamaProperties into org.springframework.ai.model.ollama.autoconfigure.OllamaChatProperties and change the suffix to: spring.ai.ollama.chat. Some of the properties have changed as well.

December 19, 2023 Update

Renaming of AiClient and related classes and package names

Rename AiClient to ChatClient
Rename AiResponse to ChatResponse
Rename AiStreamClient to StreamingChatClient
Rename package org.sf.ai.client to org.sf.ai.chat

Rename artifact ID of

transformers-embedding to spring-ai-transformers

Moved Maven modules from top-level directory and embedding-clients subdirectory to all be under a single models directory.

December 1, 2023

We are transitioning the project’s Group ID:

FROM: org.springframework.experimental.ai
TO: org.springframework.ai

Artifacts will still be hosted in the snapshot repository as shown below.

The main branch will move to the version 0.8.0-SNAPSHOT. It will be unstable for a week or two. Please use the 0.7.1-SNAPSHOT if you don’t want to be on the bleeding edge.

You can access 0.7.1-SNAPSHOT artifacts as before and still access 0.7.1-SNAPSHOT Documentation.

0.7.1-SNAPSHOT Dependencies

Azure OpenAI

<dependency>
    <groupId>org.springframework.experimental.ai</groupId>
    <artifactId>spring-ai-azure-openai-spring-boot-starter</artifactId>
    <version>0.7.1-SNAPSHOT</version>
</dependency>

OpenAI

<dependency>
    <groupId>org.springframework.experimental.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
    <version>0.7.1-SNAPSHOT</version>
</dependency>