- Remove deprecations from models, vector stores and usage
- Deprecations from FunctionCallback and ObservationContext/Convention will be in a separate PR
Models updates
- Remove AbstractToolCallSupport from the models which use ToolCallingManager
- Remove deprecated constructors and their usage
- Remove FunctionCallbackResolver and FunctionCallbacks usage in the models
- Add back deprecations for VectorStoreChatMemoryAdvisor until builder is fixed
- Update OpenAiPaymentTransactionIT to use ToolCallbackResolver in config
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
Extract common vector store delete tests to base class
This commit extracts shared delete operation tests into a reusable BaseVectorStoreTests class.
This reduces code duplication and provides a consistent test suite for delete operations across
different vector store implementations. The base class includes tests for:
Deleting by ID
Deleting by filter expressions
Deleting by string filter expressions
Most of the vector store implementation now extends this base class and inherits these
common tests while maintaining the ability to add vector store specific tests.
Adding javadoc
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Change VectorStore.delete() and related implementations to return void instead of Optional<Boolean>
- Remove unnecessary boolean return values and success status checks across all vector store implementations
- Clean up tests by removing redundant assertions
- Implementations continue using runtime exceptions for error signaling
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Spring AI's dependencies management to derive from Spring Boot 3.4.2
- Remove explicit versioning of dependencies
- Update upgrade notes for Spring Boot 3.4.2
- Fix ElasticSearch client changes with the latest dependency derived from Spring Boot 3.4.2
Adds getNativeClient API to VectorStore interface allowing access to the underlying native client implementation.
This change:
- Adds getNativeClient() default method to VectorStore interface returning Optional<T>
- Implements getNativeClient() in all vector store implementations exposing their respective native clients
- Adds integration tests verifying native client access for all implementations
Fixes: #2137
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
Add string-based filter deletion alongside the Filter.Expression-based deletion
for vector stores. This provides a more convenient API for simple filter cases
while maintaining the full flexibility of expression-based filtering.
Key changes:
- Add delete(Filter.Expression) and delete(String) methods to VectorStore
- Add default string filter implementation in VectorStore interface
- Implement filter deletion in Chroma/Elasticsearch/PgVector stores
- Add integration tests for both filter APIs across implementations
This extends vector store deletion capabilities while maintaining consistent
behavior across implementations.
Moving BatchingStrategy configuration from individual vector store implementations
to the base AbstractVectorStoreBuilder to reduce code duplication and provide consistent
batching behavior across all vector stores.
The default TokenCountBatchingStrategy is now set in the base builder class.
* Remove use of Document.getContext method from spring-ai-core, use getText
* Remove deprecated ChatOptionsBuilder class
* Remove deprecated FunctionCallingOptionsBuilder class
- Rename all specific builder inner classes (PineconeBuilder, MongoDBBuilder, etc.)
to simply Builder for consistency across vector store implementations
- Update code references to use the new standardized Builder class names
The change establishes a consistent naming convention for builder classes
across the vector store implementations, improving code uniformity.
- Replace Document.getContent() with getText() across all vector store implementations
- Fix incorrect package declarations in package-info.java files
- Make builder constructors private and implement proper builder patterns
- Add @Nullable annotations for better null safety
- Simplify PineconeVectorStore builder API by requiring essential parameters in factory method
- Make static Map fields final
- Clean up code and improve JavaDoc documentation
The changes focus on making the vector store APIs more consistent,
type-safe, and maintainable while following best practices for
builder patterns and null safety.
This commit refactors the builder pattern implementation across all VectorStore
implementations to make the EmbeddingModel a required constructor parameter
rather than an optional builder method. Key changes include:
- Move embeddingModel from being a builder method to a required constructor parameter
- Make embeddingModel final in AbstractVectorStoreBuilder
- Remove redundant validate() methods since EmbeddingModel validation now happens
in constructor
- Update all VectorStore builder instantiations to pass EmbeddingModel in builder
creation
- Add @Nullable annotations to appropriate methods in VectorStore interface
This change improves the API design by:
1. Enforcing that EmbeddingModel is provided at builder creation time
2. Removing the possibility of forgotten EmbeddingModel configuration
3. Simplifying the builder implementation by moving validation to construction
4. Making the dependency on EmbeddingModel more explicit in the API
Breaking Changes:
- VectorStore builders must now be created with an EmbeddingModel parameter
- The embeddingModel() builder method has been removed from all implementations
This commit deprecates builder methods with the "with" prefix in the VectorStoreObservationContext class
and introduces new methods without the prefix for a cleaner API. For example:
withCollectionName() → collectionName()
withDimensions() → dimensions()
withNamespace() → namespace()
The old methods are marked as deprecated for maintaining backward compatibility.
All vector store implementations have been updated to use the new method names.
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
This refactoring introduces a consistent builder pattern across vector store
implementations to standardize configuration and initialization, while also
moving ChromaVectorStore to a dedicated chroma package.
Key changes:
- Add VectorStore.Builder interface and AbstractVectorStoreBuilder to establish
a common builder hierarchy
- Move ChromaVectorStore and related classes from vectorstore to
org.springframework.ai.chroma.vectorstore package
- Migrate ChromaVectorStore to builder pattern as the first implementation
- Add null-safety annotations and parameter validation
- Deprecate direct constructors in favor of builder API
- Update all tests and documentation to reflect new structure
The builder pattern provides several benefits:
- Consistent configuration across all vector store implementations
- Better validation of required parameters
- More flexible initialization order
- Clearer separation of concerns between configuration and usage
- Improved discoverability of options through method chaining
- Since the Document object's reference to the `embedding` is deprecated and will be removed, the VectorStore implementations require a way to store the embedding of the corresponding Document objects
- One way to fix this is, to have the EmbeddingModel#embed to return the embeddings in the same order as that of the Documents passed to it.
- Since both the Document and embedding collections use the List object, their iteration operation will make sure to keep them in line with the same order.
- A fix is required to preserve the order when batching strategy is applied.
- Updated the Javadoc for BatchingStrategy
- Fixed the Document List order in TokenCountBatchingStrategy
- Refactored the vector store implementations to update this change
Resolves #GH-1826
Document
* Introduced “score” attribute in Document API. It stores the similarity score.
* Consolidate “distance” metadata for Documents. It stores the distance measurement.
* Adopted prefix-less naming convention in Document.Builder and deprecated old methods.
* Deprecated the many overloaded Document constructors in favour of Document.Builder.
Vector Stores
* Every vector store implementation now configures a “score” attribute with the similarity score of the Document embedding. It also includes the “distance” metadata with the distance measurement.
* Fixed error in Elasticsearch where distance and similarity were mixed up.
* Added missing integration tests for SimpleVectorStore.
* The Azure Vector Store and HanaDB Vector Store do not include those measurements because the product documentation do not include information about how the similarity score is returned, and without access to the cloud products I could not verify that via debugging.
* Improved tests to actually assert the result of the similarity search based on the returned score.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
- Add new Maven profile 'ci-fast-integration-tests' for running selective ITs
- Remove redundant vector store skip flags from properties section
- Update maven-failsafe-plugin to version 3.5.2
- Configure test exclusions for various components:
- Most model integration tests (Anthropic and OpenAI)
- Most vector store tests (except PgVector and Chroma)
- Most auto-configuration tests
- All test containers and docker compose tests
- AI evaluation tests
- Convert the docker-compose tests into ITs
- Convert the testcontainers tests into ITs
- Updated README.md
- Explain the new profile and also the new integration tests repo
- Describe ways to run integration tests for specific modules
- Add badge for https://github.com/spring-projects/spring-ai-integration-tests
- 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
- Disable project-wide Checkstyle checks to unblock development
- Add documentation for enabling Checkstyle locally
- Fix remaining checkstyle violations in current codebase
Fixes#1669
* Unify image definition for vector stores in vector-store modules
* Unify image definition for vector stores in spring-ai-testcontainers module
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Vector store observations support several key-value pairs, coming from the Spring AI abstractions. Currently, whenever a value is not available (either because not configured by the user or not supported by the vector store provider), span/metrics attributes are generated anyway with value none.
That causes several issues, including an unneeded increase in time series, challenges in alerting/monitoring (especially for integer/double attributes that suddenly are populated with a string), and non-compliance with the OpenTelemetry Semantic Conventions (according to which, attributes should be excluded altogether if there's no value).
This pull request changes the conventions for vector store observations to exclude the generation of span/metrics attributes for optional values which don't have any value.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Apply batching when adding Documents to the following vector stores:
- Chroma
- ElasticSearch
- Neo4j
- Qdrant
- Redis
- Typesense
- Weaviate
This improves efficiency by processing multiple Documents at once instead of individually, reducing the overhead for each operation.
Related to #1261
