Resolves#1628
In Azure Vector Store, the embedding field needs to be retrievable
for proper functionality. While SearchField class doesn't have a
'retrievable' property, the 'hidden' field serves the same purpose.
The fix sets hidden=false on the embedding field to make it retrievable.
Link: https://github.com/spring-projects/spring-ai/issues/1628
- Update GenerateResponse content schema type to array at openapi.json
- Use CompatGenerateRequest instead of GenerateRequest for the TextGenerationInference API Request
Signed-off-by: jitokim <pigberger70@gmail.com>
- Adviser name is moved from hight to low cardinality key values.
- Updated tests in to reflect these changes, ensuring the new low cardinality key is correctly captured.
Resolves#1716
Due to changes in Chroma v0.5.13 (chroma-core/chroma#2880), the delete
operation no longer returns values. This impacts our ChromaVectorStore's delete
functionality.
Now we need to check the HTTP status code instead to properly verify if the
delete operation succeeded.
Test suite has been updated.
Resolves#1529
Query Analysis
* Introduce Query Analysis Module
* Define QueryTransformer API and TranslationQueryTransformer implementation
* Define QueryExpander API and MultiQueryExpander implementation
* Support QueryTransformer in RetrievalAugmentationAdvisor (support for QueryExpander will be in the next PR together with the needed DocumentFuser API).
Improvements
* Refine Retrieval and Augmentation Modules for increased robustness
* Expand test coverage for both modules
* Define clone() method for ChatClient.Builder
Tests
* Introduce “spring-ai-integration-tests” for full-fledged integration tests
* Add integration tests for RAG modules
* Add integration tests for RAG advisor
Query Analysis
* Introduce Query Analysis Module
* Define QueryTransformer API and TranslationQueryTransformer implementation
* Define QueryExpander API and MultiQueryExpander implementation
* Support QueryTransformer in RetrievalAugmentationAdvisor (support for QueryExpander will be in the next PR together with the needed DocumentFuser API).
Improvements
* Refine Retrieval and Augmentation Modules for increased robustness
* Expand test coverage for both modules
* Define clone() method for ChatClient.Builder
Tests
* Introduce “spring-ai-integration-tests” for full-fledged integration tests
* Add integration tests for RAG modules
* Add integration tests for RAG advisor
Relates to #gh-1603
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
The change addresses initialization issues when ChromaVectorStore is used outside
Spring context, particularly in scenarios where collections are created manually
before store instantiation. Previously, collection ID wasn't properly populated
when afterPropertiesSet() wasn't called by Spring container.
- Add builder pattern to ChromaVectorStore for better initialization control
- Add initialization flag to prevent multiple collection creation calls
- Add integration tests for builder pattern usage scenarios
- Add spring-ai-transformers dependency for testing
- Remove unused constants (SIMILARITY_THRESHOLD_ALL, DEFAULT_TOP_K)
Collection ID is now properly set regardless of whether the store is managed by
Spring or created manually, solving the 404 Not Found errors during document
insertion.
Fixes: #1240
- Add maven properties for all vector stores such as
skip.vectorstore.azure-cosmos-db to control IT test execution
- Chroma and PGVector IT tests are enabled by default
- Docker Compose and Testcontainers module ITs are skipped by default
- Add parallel job to run docker-compose and testcontainers ITs
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 default values to KotlinRequest data class to fix Jackson deserialization
- Replace hardcoded model name with OllamaModel.LLAMA3_2 for consistency
- Extract common test setup into companion objects for Ollama tests
- Replace var with val using when expression in weather service
- Clean up imports and remove unused annotations
- Standardize property access using Kotlin conventions
This change simplifies how we manage Ollama containers in tests by moving
from manual toggles to environment variables for better control. Instead of
scattered container configuration, we now have:
- OLLAMA_WITH_REUSE: Toggle reuse of existing containers between tests
- OLLAMA_TESTS_ENABLED: Control test execution globally
The motivation is to make tests more reliable and easier to maintain.
Previously, developers had to modify code to run tests locally vs CI. Now
they can control this via environment variables.
We also introduce thread-safe API access and consistent default settings
across all test classes, removing duplicated configuration and potential
resource leaks.
This makes the test infrastructure more maintainable and provides clearer
separation between local development and CI environments.
Checkstyle fixes.
Make buildOllamaApiWithModel in BaseOllamaIT public and the related test changes.
- 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.
- Verify that the BeanOutputConverter converts with the right order as specified in the JSON schema
Update the BeanOutputConverterTest's test case to tweak the order for validation
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
The changes include:
- Refactor the emitting of next, error, and complete events in the Bedrock stream handling to use a default EmitFailureHandler that retries for 10 seconds before failing.
This helps improve the error resilience of the stream processing.
- Disable a couple of integration tests related to the COHERE_COMMAND_V14 model, as that model version is no longer supported.
- Adjust some configuration options in the Jurassic2 chat model integration test.
Also resolves#1679
XFY Spark and possibly other vendors implement the OpenAI interface but return
tool_calls as a single object rather than an array. Add @JsonFormat annotation
to handle both array and single object responses in ChatCompletionMessage,
making Spring AI more broadly compatible with OpenAI API implementations.
Only ChatCompletionMessage.java required modification to support this alternate
format while maintaining backward compatibility with the standard OpenAI API
response structure.
Co-authored-by: jito <pigberger70@gmail.com>
This commit rename the thresholdFactor parameter to reservePercentage to better
reflect its purpose in the TokenCountBatchingStrategy class. It also adds
validation for input parameters including maxInputTokenCount > 0 and
reservePercentage between 0 and 1 for safer initialization.
The change ensures proper parameter validation on TokenCountBatchingStrategy
creation to prevent potential runtime errors from invalid inputs.
- Extracts ResponseFormat from being a nested record in OpenAiApi to
a dedicated class with builder pattern support.
- Resolve the issue with constructor bindings for the Boog property
binding.
- Re-enables previously disabled response format integration tests.
- Add checkstyle changes
- Add schema field in ResponseFormat and set jsonSchema via the setter for schema,
this way schema set via a Boot property also sets the correct JsonSchema
- Add default constructors in ResponseFormat and JsonSchema
Resolves#1681
Adds Oracle Cloud Infrastructure (OCI) Generative AI's Cohere chat model support
to expand Spring AI's cloud provider capabilities. This allows developers to use
OCI's managed Cohere models through both dedicated and on-demand serving modes.
The integration provides auto-configuration for simple setup while allowing full
customization of model parameters through OCICohereChatOptions. Teams can now
use OCI's Cohere models alongside other providers in Spring AI applications.
This change complements the existing OCI embedding support, offering a complete
set of GenAI capabilities for Oracle Cloud users.
Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
- Fix parameter access syntax in setNodeVectorProperty DDL statement
- Define DEFAULT_TRANSACTION_SIZE for the hardcoded batch size in
the delete query
Fixes#1623
Signed-off-by: jitokim <pigberger70@gmail.com>
Integrates Oracle Coherence 24.09+ as a vector store backend for Spring AI. The
implementation provides:
- Support for vector similarity search with configurable distance metrics (Cosine,
Inner Product, L2)
- Multiple indexing options including HNSW and Binary Quantization indexes
- Filter expression evaluation for metadata-based filtering
- Vector normalization capabilities
- Comprehensive test coverage including integration tests
The implementation includes:
- CoherenceVectorStore main implementation
- CoherenceFilterExpressionConverter for Spring AI to Coherence filter conversion
- Auto-configuration support via spring-boot-starter
- Documentation and usage examples
Requires Oracle Coherence 24.09 or later.
Changes the @ConditionalOnClass condition from RetryTemplate to RetryUtils to
ensure proper conditional loading of retry functionality. This prevents the
auto-configuration from being enabled when spring-retry is present but
spring-ai-retry is not, which was causing issues with modules like
spring-ai-azure-openai that don't include spring-ai-retry.
Replace traditional switch statements with Java 14 switch expressions across
vector store filter converters and related components. This change improves
code quality in our filter expression handling for Azure, Milvus, Redis,
Typesense and Weaviate implementations.
The switch expressions eliminate fall-through behavior, enforce exhaustive
pattern matching at compile time, and provide a more direct way to return
values. This makes the filter conversion logic more robust and maintainable.
- Milvus requires "not in" operator syntax instead of "nin" for filtering.
- See https://milvus.io/docs/boolean.md
- Updated test and added additional test coverage
