- 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 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.
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>
Pre-Retrieval:
* Consolidated naming and documentation
Retrieval:
* Consolidated naming and documentation
* Introduced DocumentJoiner sub-module and CompositionDocumentJoiner operator
Post-Retrieval:
* Introduced main interfaces for sub-modules. Implementation waiting for missing features in Document APIs
Orchestration:
* Introduced QueryRouter sub-module and AllDocumentRetrieversQueryRouter operator
Generation:
* Consolidated naming and documentation
Advisor:
* Introduced BaseAdvisor to reduce boilerplate when implementing Advisors
* Extended RetrievalAugmentationAdvisor to include the new sub-modules
Relates to #gh-1603
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>
