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
Introduces a fluent builder API to improve configuration readability and
type safety when creating MilvusVectorStore instances. This replaces the
existing configuration object approach which was less intuitive and harder
to maintain.
The builder pattern provides better encapsulation of configuration logic
and validation, while maintaining backward compatibility through a
deprecated config class. This change makes the codebase more maintainable
and the API more discoverable for users.
Key changes:
- Replace configuration object with fluent builder pattern
- Move Milvus-related classes to dedicated milvus package
- Deprecate MilvusVectorStoreConfig in favor of builder
- Update constructor to use builder internally
- Maintain backward compatibility with deprecated config
- Add comprehensive builder methods with validation
- Deprecate existing ChatOptionsBuilder and its inner class DefaultChatOptions
- Create a new builder interface ChatOptions.Builder for building the Chat options
- Create an explicit DefaultChatOptions
- Create DefaultChatOptionBuilder which can create DefaultChatOptions
- Add javadoc for the deprecated Builder
Resolves#1875
The commit restructures the ChromaVectorStore builder pattern to use a no-args constructor
with fluent API for setting the ChromaApi.
This change:
- Makes builder creation consistent with other vector stores
- Moves ChromaApi validation to the doValidate method
- Improves builder API ergonomics
The change requires updating all builder usages to use the new .chromaApi() method instead
of passing it in the constructor.
- Fix OpenAI ChatModel's call() operation
- When toolcalling is used, calculate cumulative usage from the preceding ChatResponses
- Fix OpenAI ChatModel's stream() operation
- Make sure that cumulative usage is calculated from the ChatResponse which has a valid usage
- Use overlapping buffer to check and store the usage from the response that holds the usage.
- Add tests for both call() and stream()
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
The Document class previously allowed multiple media entries while also having a
text field, leading to ambiguity in content handling. This change enforces a
clear separation between text and media documents to prevent content type
confusion and simplify document processing.
A Document now must contain either text content or a single media entry, but
never both. This aligns with the class's primary use in ETL pipelines where
clear content type boundaries are essential for proper embedding generation and
vector database storage.
Additional architectural changes:
- Document now implements a cleaner API by removing deprecated methods
- Removed MediaContent interface implementation from Document class
- Document.getMedia() now returns a single Media object instead of Collection
- Removed EMPTY_TEXT constant in favor of proper null handling
- Constructor signatures simplified and streamlined
- Builder pattern improved to enforce single content type constraint
The breaking changes include:
- Media is now a single entry instead of a collection
- Content field renamed to text for clarity
- Removed support for mixed content types
- Simplified builder API to prevent ambiguous construction
Prefer using text-related methods over deprecated content methods to
better reflect the actual content type being handled and improve API clarity.
- 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
- Upgrade Ollama version used for IT from 0.3.14 to 0.5.1
- Fix string comparison pattern in OllamaModelManager
- Re-enable stream function calling test
- Improve multimodal test assertions
- 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
- Add null check for the Chroma vector store ObservationConvention
- Since the builder enforces non null check, the ChromaVectorStore builder in its autoconfiguration will only set the ObservationConvention if it is not null.
- Since Document's reference to its embedding is deprecated, store the embedding into OpenSearch vector store by creating an explicit OpenSearch Document type which has embedding associated with it
- Create an explicit MariaDBDocument to store the embeddings of its content
- This is because the Spring AI Document no longer holds reference to its embeddings
- Address the test case which checks just the MariaDB documents storing without their embeddings
- Re-enable the MariDB ITs
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
- PaymentStatusFunctionCallingIT in org.springframework.ai.mistralai.api.tool
is failing. Needs investigation.
See https://github.com/spring-projects/spring-ai/issues/1853
- OpenSearchVectorStoreWithOllamaIT updated to pull model if not available.
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>
