The MongoDBAtlasVectorStore implementation has been enhanced with a builder
pattern to provide a more flexible and type-safe way to configure the vector
store. This change improves the developer experience by making the API more
intuitive and less error-prone.
The old constructors and configuration classes have been deprecated in favor
of the builder pattern. This aligns with Spring's best practices for
configuration APIs.
Additionally, the package has been refactored to
org.springframework.ai.vectorstore.mongodb.atlas to avoid having
multiple vector store modules share the same package name.
Documentation has been updated to reflect these changes and provide
examples of using the new builder pattern.review
The changes introduce a fluent builder pattern for ElasticsearchVectorStore
configuration, making it easier to create and customize instances with
optional parameters. All Elasticsearch-related classes are moved to a
dedicated elasticsearch package for better organization.
Key changes:
* Add ElasticsearchVectorStore.builder() with comprehensive options
* Move classes to org.springframework.ai.vectorstore.elasticsearch package
* Deprecate old constructors in favor of builder pattern
* Add support for configurable batching strategies
* Enhance documentation with usage examples and best practices
- Move PgVectorStore and related classes to org.springframework.ai.pg.vectorstore package
- Update builder pattern to use more idiomatic method names (e.g. withSchemaName -> schemaName)
- Deprecate existing constructors and old Builder class in favor of new static builder() method
- Update tests to reflect the new builder style usage
- Update docs
The commit adds a note to the bedrock-converse.adoc document that lists the supported models for the Converse API, including:
Amazon Titan, Amazon Nova, AI21 Labs, Anthropic Claude, Cohere Command, Meta Llama, Mistral AI
- Add BedrockMediaFormat class to handle media format conversions for documents, images and videos
- Enhance Media class with builder pattern and comprehensive format constants
- Refactor BedrockProxyChatModel to support multimodal content handling
- Add integration tests for PDF, image and video processing
- Add unit tests for Media and BedrockMediaFormat classes
- Upgrade AWS SDK version from 2.26.7 to 2.29.29
- Remove redundant aws.sdk.version property in favor of awssdk.version
Documentation updates for Bedrock Converse API
- Added multimodal support documentation (images, video, documents)
- Added deprecation notices for existing Bedrock model implementations
- Updated feature comparison table
- Added warning notes about transitioning to Converse API
- Implement streaming tool call support in OllamaApi and OllamaChatModel
- Add OllamaApiHelper to manage merging of streaming chat response chunks
- Remove @Disabled annotations for streaming function call tests
- Update documentation to reflect new streaming function call capabilities
- Add a new default constructor for ChatResponse
- Update Ollama chat documentation to clarify streaming support requirements
- Deprecated withContent(), withImages(), and withToolCalls() methods
- Replaced with content(), images(), and toolCalls() methods
Add token and duration aggregation for Ollama chat responses
- Modify OllamaChatModel to support accumulating tokens and durations across multiple responses
- Update ChatResponse metadata generation to aggregate usage and duration metrics
- Add tests to verify metadata aggregation behavior
Refactor Ollama duration fields and tests
- Replace Duration fields in OllamaApi.ChatResponse with Long to represent durations in nanoseconds, ensuring precision and compatibility.
- Update methods to convert Long nanoseconds to Duration objects (getTotalDuration, getLoadDuration, getEvalDuration, getPromptEvalDuration).
- Adjust merge logic in OllamaApiHelper to sum Long values for duration fields.
- Modify test cases in OllamaChatModelTests to align with Long duration representation and Duration.ofNanos conversions.
- Add new test class OllamaDurationFieldsTests to validate JSON deserialization and Duration conversion for duration fields.
Resolves#1847
Related to #1800Resolves#1796
Related to #1307
test: Update OllamaWithOpenAiChatModelIT integration tests
- Remove @Disabled annotation for streamFunctionCallTest
- Add inputType for function callback in stream function call test
- Introduced new options for audio output modalities in ChatCompletionRequest
- Added AudioParameters configuration for voice and audio format selection
- Enhanced OpenAiChatModel to handle audio generation and embedding
- Updated AssistantMessage and Media classes to support audio media
- Added integration tests for audio output functionality
- Implemented support for text and audio multi-modal responses
- Updated Spring AI's chat model comparison table to clarify OpenAI's input/output modalities
- Added new configuration properties for audio output:
* spring.ai.openai.chat.options.output-modalities
* spring.ai.openai.chat.options.output-audio
- Extended documentation to explain audio output generation with the gpt-4o-audio-preview model
- Updated Spring Boot configuration metadata to support new audio-related properties
- Included auto-configuration integration test for chat model with audio response generation
Resolves#1841
Extracts shared function callback builder functionality into DefaultCommonCallbackInvokingSpec
base class, reducing code duplication across builder implementations.
Makes FunctionInvokingSpec and MethodInvokingSpec extend CommonCallbackInvokingSpec for better
code organization. Also fixes function/description builder order in Anthropic tests.
- Introduced a common base class for function callback builders to centralize shared logic
- Standardized the order of method chaining for function and description in multiple AI model test classes
- Refactored test cases across various AI model integrations
- Corrected builder method order from .description().function() to .function().description()
and .description().method() to .method().description()
- Updated multiple test files to consistently use .function() before .description()
- Updated documentation examples to reflect new builder method order
- Modified DefaultFunctionCallbackResolver to maintain new builder method order
- Updated DefaultChatClient and ChatClient test classes to reflect new builder pattern
- Simplified callback specification by removing parent spec reference
- Removed cascading getter logic for description, schema type, and other properties
- Minor adjustments to function callback builder and invoking specs
- Introduced `PerplexityWithOpenAiChatModelIT` integration test for Perplexity AI with OpenAI Chat Model.
- Includes various test cases for role-based prompts, streaming responses, token usage validation, and output converters.
- Added tests for function calls and metadata validation.
- Updated Antora navigation (`nav.adoc`) to include Perplexity AI documentation link.
- Enhanced chat model comparison documentation to highlight Perplexity AI integration.
- Added a dedicated `perplexity-chat.adoc` page under `spring-ai-docs` to provide detailed documentation for integrating Perplexity AI.
- Covers API prerequisites, auto-configuration, and runtime options.
- Explains configuration properties such as `spring.ai.openai.base-url`, `spring.ai.openai.chat.model`, and `spring.ai.openai.chat.options.*`.
- Provides examples for environment variable setup and runtime overrides.
- Highlights limitations like lack of multimodal support and explicit function calling.
- Includes a sample Spring Boot controller demonstrating integration usage.
- Links to Perplexity documentation for further reference.
- Updated `org.springframework.boot.autoconfigure.AutoConfiguration.imports` to include MariaDB vector store auto-configuration
- Created MariaDB Vector Store autoconfiguration integration tests (`MariaDbStoreAutoConfigurationIT`)
- Added MariaDB store properties configuration and tests (`MariaDbStorePropertiesTests`)
- Introduced new Maven modules:
- `spring-ai-mariadb-store`: Core MariaDB vector store implementation
- `spring-ai-starter-mariadb-store`: Spring Boot starter for MariaDB vector store
- Added `MariaDBFilterExpressionConverter` to support JSON-based metadata filtering in MariaDB
- Implemented filter expression conversion for MariaDB vector store queries
- Added README.md with documentation link for MariaDB Vector Store
- Updated project dependencies to include MariaDB JDBC driver and test containers
- Configured integration testing with TestContainers for MariaDB
- Added observability support for MariaDB vector store operations
- Added support for audio input and output in OpenAI Chat Completion API
- Introduced new audio-related parameters, enums, and record types
- Updated ChatCompletionMessage, ChatCompletionChunk, and related classes
- Added new AudioParameters, AudioOutput, and InputAudio record types
- Implemented method to handle audio media content conversion
- Included new model enum for GPT-4o audio preview
- Extended existing API classes to accommodate audio modalities
- Modified usage tracking and metadata classes to handle audio-specific token details
- Improved ModelOptionsUtils with additional JSON utility methods
Tests:
- Updated test classes to validate audio input and output functionality
- Added integration tests for multimodal audio input with streaming and non-streaming methods
- Created parameterized tests for audio-enabled models
- Enhanced OpenAI API integration tests to cover audio-related scenarios
Docs:
- Updated documentation in spring-ai-docs to explain audio multimodality support
Resolves#1560
- Enhance AnthropicChatModel to support PDF and document content types
- Introduce getContentBlockTypeByMedia method for flexible media type handling
- Update ContentBlock handling to dynamically determine content type for media
- Add multimodal PDF support test case for Claude 3.5 Sonnet
- Update documentation to reflect PDF and multimodal capabilities
- Modify comparison chart to show PDF support for Anthropic Claude
Fixese #1819
review
- Add test case for PDF document summarization using Gemini multimodal capabilities
- Update documentation to reflect PDF support in model comparison table
- Add PDF format to multimodal capabilities documentation
- Move bedrock-converse.adoc from api/ to api/chat/ directory
- Add Bedrock Converse to chat models comparison table
- Fix table entry formatting for OCI GenAI/Cohere width values
The move aligns with the documentation structure where chat-related content
is organized under the api/chat/ directory.
* Remove redundant section headers
* Improve builder pattern documentation with hierarchical structure overview
* Add cross-references between related sections
* Restructure function and method invoking sections for better organization
* Adjust heading levels for better document hierarchy
* Move schema generation and common pitfalls under appropriate sections
- Add cross-references to tool context documentation section
- Enhance BiFunction section title and description
- Improve readability and consistency of function callback documentation
- Added Jackson annotations for JSON property handling in Chroma API request/response records.
Use @JsonInclude(JsonInclude.Include.NON_NULL) to ignore empty fields such as null where.
Use @JsonProperty(XYZ) to provide a strong contract with the Cohere API.
- Updated the Docker image for running Chroma locally and in tests to version 0.5.20.
- Enhanced methods with non-null assertions for improved code safety.
- Added a simple search test to verify document similarity search functionality.
Fixes#1749
Fixes: #1753https://github.com/spring-projects/spring-ai/issues/1753
- Add character-based truncation (max 2048 chars) for Cohere embedding requests
- Support both START and END truncation strategies
- Add unit tests verifying truncation behavior for both strategies
Truncation is applied before sending requests to Bedrock API to avoid
ValidationException when text exceeds maximum length. The END strategy
(default) keeps the first 2048 characters while START keeps the last
2048 characters.
Implemented a new configuration property 'useKeylessAuth' to toggle
between API key and Azure default credential authentication.
Added necessary dependencies and updated tests to reflect the new changes.
co-authored-by: mattgotteiner <mattgotteiner@users.noreply.github.com>
- Fix the embedding dimension configuration for opensearch client indices mapping
The dimension config is obtained by the underlying embedding model's dimension
- Updated OpenSearch mapping JSON to include dynamic embedding dimension.
- Changed default mapping for OpenSearch vector store to use generic dimension placeholder.
- Fixed OpenSearchVectorStoreAutoConfiguration to fallback to new default mapping if no custom mapping is provided.
- Added test for verifying mapping configuration with OpenSearch vector store.
- Added test dependencies for Ollama integration.
- Refactored OpenSearch integration tests to use new mapping and dimension logic.
Add tests
- Verify the mappingJson field is correctly set - verify the override works fine
- Add integration tests with Ollama embedding model
Resolves#1589
* Add property `spring.ai.vectorstore.opensearch.aws.domain-name`
* Require `AwsCredentialsProvider` to enable `AwsOpenSearchConfiguration`
* Add Testcontainers Service Connection support
* Add Docker Compose Service Connection support
Add support for no-argument Supplier and single-argument Consumer function
callbacks in the Spring AI core module. This enhancement allows:
- Registration of Supplier<O> callbacks with no input (Void) type
- Registration of Consumer<I> callbacks with no output (Void) type
- Support for Kotlin Function0 (equivalent to Java Supplier)
- Handle empty properties for Void input types in schema generation
- Enhance FunctionCallback builder to support Supplier/Consumer patterns
Additional changes:
- Add test coverage for both Supplier and Consumer callbacks in various scenarios
- Enhance TypeResolverHelper to support Consumer input type resolution
- Support lambda-style function declarations for improved ergonomics
- Add test cases for void input/output handling in OpenAI chat model
- Include examples of function calls without return values
- Add support for parameterless functions through Supplier interface
Add comprehensive documentation for the FunctionCallback API:
- Overview of the interface and its key methods
- Builder pattern usage with function and method invocation approaches
- Examples for different function types (Function, BiFunction, Supplier, Consumer)
- Best practices and common pitfalls
- Schema generation and customization options
Resolves#1718 , #1277 , #1118, #860
Improves function calling documentation with clearer organization and examples:
- Reorganizes content into server-side and client-side registration sections
- Adds detailed examples for both function-invoking and method-invoking approaches
- Enhances tool context documentation with diagrams and usage examples
- Updates function-calling diagrams to reflect current implementation
Introduces a simplified, type-safe builder pattern for function callbacks to
improve developer experience and code reliability. The new hierarchical API
separates concerns between direct function invocation and method reflection,
while providing better compile-time safety.
This change deprecates the older FunctionCallbackWrapper in favor of a more
intuitive FunctionCallback.Builder that better handles generic types via
ParameterizedTypeReference. It also adds automatic function description
generation as a fallback when none is provided, though explicit descriptions
are still recommended.
The update standardizes function callback handling across all AI model
implementations (OpenAI, Ollama, Minimax, etc.) and improves response
handling with configurable converters.
Core API Enhancements:
- New Builder Interface: Replaced FunctionCallbackWrapper.builder() with
FunctionCallback.builder(), introducing a hierarchical approach that improves
customization and type safety.
- Specialized Builders: Introduced FunctionInvokerBuilder for direct Function/BiFunction
implementations and MethodInvokerBuilder for reflection-based invocations.
- Generic Type Support: Added ParameterizedTypeReference for better handling of generic parameters.
- Unified Method Definition: Merged method() and argumentTypes() into a single method() call
for simplicity and type safety.
- Automatic Descriptions: Implemented auto-generation of function descriptions, with warnings
to encourage explicit descriptions.
- Configurable Response Converters: Enhanced response handling with support for custom
converters, reducing unnecessary JSON conversions.
Architecture Improvements:
- Established common Builder interface for shared properties
- Separated function object handling from constructor
- Added method-specific configuration (name, arg types, target)
- Added JSON schema generation support for ResolvableType
- Moved to standardized schema types across AI providers
- Set OPEN_API_SCHEMA as default for Vertex AI Gemini
Builder Pattern Standardization:
- Standardized builder method ordering across implementations
- Moved function() call after description() for consistency
- Improved function callback configuration with unified patterns
- Enhanced error handling and validation in DefaultFunctionCallbackBuilder
Deprecations:
- FunctionCallbackWrapper.Builder replaced by DefaultFunctionCallbackBuilder
- Removed CustomizedTypeReference in favor of ParameterizedTypeReference
- Deprecated older ChatClient API methods for function handling
Testing & Documentation:
- Updated all AI model implementations (OpenAI, Ollama, Minimax, Moonshot, ZhiPuAI)
- Added comprehensive integration tests for static/instance methods
- Added integration tests for auto-generated descriptions
- Updated documentation to reflect new builder pattern usage
- Added Kotlin extension for inputType() support
Co-authored-by: Sébastien Deleuze <sebastien.deleuze@broadcom.com>
This change enables more flexible integration between Spring AI and LLM function
calling capabilities while maintaining type safety and ease of use.
- Add new MethodFunctionCallback class to support method invocation via reflection
- Supports both static and non-static method calls
- Handles multiple parameter types including primitives, objects, collections
- Supports empty parameters and empty response
- Auto-generates JSON schema from method parameters
- Special handling for ToolContext parameters
- Builder pattern for easy configuration
- Add comprehensive unit tests for MethodFunctionCallback
- Add integration tests for MethodFunctionCallback with both Anthropic and OpenAI clients
- Add jackson-module-jsonSchema dependency
- Modify FunctionCallback to check for empty tool context
Testing coverage includes:
- Static method invocation scenarios
- Non-static method calls with various parameter types
- Void return type methods
- Complex parameter types (enums, records, lists)
- Tool context handling
- Error cases and validation
Add MethodFunctionCallback reference docs
- Add migration note recommending transition from InvokeModel to Converse API
- Update model configuration to remove default model ID
- Add AWS session token configuration for temporary credentials
- Improve documentation links and descriptions
- Add support for proper Kotlin functions handling by adapting
`kotlin.jvm.functions.Function1` to `java.util.function.Function` and
`kotlin.jvm.functions.Function2` to `java.util.function.BiFunction`.
- Removes the dependency on Spring Cloud Function and
`net.jodah:typetools` which are replaced by leveraging
Spring Framework `ResolvableType` capabilities.
- Add a Kotlin extension function for
`FunctionCallbackWrapper.Builder.withInputType` allowing to
specify `withInputType<T>()` instead of `withInputType(T::class.java)`.
- Add Kotlin documentation.
Fixes: #1335https://github.com/spring-projects/spring-ai/issues/1335
Add support for maintaining JSON property order in generated schemas using
@JsonPropertyOrder. Users can now control the order of properties in their
JSON schemas by annotating their classes/records with @JsonPropertyOrder.
- Add JacksonOption.RESPECT_JSONPROPERTY_ORDER to BeanOutputConverter
- Add test to verify schema property ordering
- Update documentation with property ordering example
