- 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
The system text advise prompt is only joined with the value of the system text if it's non-null and non-empty.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Adds @EnabledIfEnvironmentVariable annotation to integration tests that
use OpenAI embeddings. Tests will be skipped if OPENAI_API_KEY is not set,
making the build process more reliable for contributors who don't have
access to OpenAI services.
ObjectMapper instantiation is costly, so unless its usage
is one-shot, it is better to create a reusable instance
for upcoming usage.
Also, before this commit, serialization of most Kotlin
classes was not supported due to the lack of proper
Jackson KotlinModule detection.
This commit:
- Avoids per invocation ObjectMapper instantiation when
relevant
- Automatically detects and enables well-known Jackson
modules including the Kotlin one
- Removes org.springframework.ai.vectorstore.JsonUtils
which looks not needed anymore
More optimizations are possible like reusing more
ObjectMapper instances, but this could introduce more breaking
changes so this commit intends to be a good first step.
Kotlin tests will be provided in a follow-up commit.
Additional changes:
- Update ModelOptionsUtils to use JacksonUtils.instantiateAvailableModules()
- Add missing license headers
- Add missing author Javadoc comments
* 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>
This commit introduces a major overhaul of the advisor system in Spring AI,
improving modularity, type safety, and consistency
Core Changes:
- Replace RequestAdvisor and ResponseAdvisor with CallAroundAdvisor and StreamAroundAdvisor
- Introduce AdvisedRequest and AdvisedResponse classes for better encapsulation
- Deprecate RequestResponseAdvisor in favor of new advisor types
- Remove AdvisorObservableHelper class
Advisor Implementation Updates:
- Update AbstractChatMemoryAdvisor, MessageChatMemoryAdvisor, PromptChatMemoryAdvisor,
QuestionAnswerAdvisor, SafeGuardAroundAdvisor, SimpleLoggerAdvisor, and
VectorStoreChatMemoryAdvisor to implement new advisor interfaces
- Remove CacheAroundAdvisor (functionality likely moved elsewhere)
- Make CallAroundAdvisor and StreamAroundAdvisor extend Ordered interface
Client and Chain Management:
- Modify DefaultChatClient to use new advisor chain approach
- Refactor DefaultAroundAdvisorChain for better ordering and observation
- Implement builder pattern for advisor chain construction in DefaultChatClient
- Separate call and stream advisors in DefaultAroundAdvisorChain
Observation and Context Handling:
- Update observation conventions and context handling in advisors
- Add order field to AdvisorObservationContext
- Modify DefaultAdvisorObservationConvention to include order in high cardinality key values
Testing and Integration:
- Refactor ChatClientAdvisorTests and add new AdvisorsTests
- Update integration tests to reflect new advisor structure
- Enhance AdvisorsTests to verify correct advisor execution order
New Features:
- Generalize the Protect From Blocking functionality across all advisors
- Add (experimental) Re2 advisor to enhance reasoning capabilities of LLMs
- Add disabled Re2 test in OpenAiChatClientIT
Documentation:
- Add Advisors documentation
- Enhance advisors documentation with order explanation and Re2 example
Advisor Ordering:
- Introduce Advisor constants for precedence ordering
- Update AbstractChatMemoryAdvisor to use new precedence constant
- Improve advisor ordering and management in DefaultAroundAdvisorChain.Builder
- Remove redundant reordering logic from DefaultAroundAdvisorChain
These changes aim to provide a more flexible and powerful advisor system,
allowing for easier implementation of complex AI-driven interactions
Co-authored-by: Dariusz Jędrzejczyk <dariusz.jedrzejczyk@broadcom.com>
Resolves https://github.com/spring-projects/spring-ai/issues/1199
- Implement configurable maxDocumentBatchSize to prevent insert timeouts
when adding large numbers of documents
- Update PgVectorStore to process document inserts in controlled batches
- Add maxDocumentBatchSize property to PgVectorStoreProperties
- Update PgVectorStoreAutoConfiguration to use the new batching property
- Add tests to verify batching behavior and performance
This change addresses the issue of PgVectorStore inserts timing out due to
large document volumes. By introducing configurable batching, users can now
control the insert process to avoid timeouts while maintaining performance
and reducing memory overhead for large-scale document additions.
- Precompute all embeddings using a BatchingStrategy before inserting into the vector store
This optimization improves efficiency when adding multiple documents
Related to #1261
* Consolidate usage of “db.collection.name” attribute to track table name, collection name, index name, document name, or whatever concept a vector database uses to store data. Removed “db.index” that was use sometimes instead of “db.collection.name”. This usage is in line with the OpenTelemetry Semantic Conventions.
* Configure query response content to be included as a “span event” instead of a “span attribute” if the backend system supports that, similar to how we do for the model observations.
* Structure vector store observation attributes in dedicated enums, including one for the Spring AI Kinds to avoid hard-coding the same value in a lot of places. This follows the OpenTelemetry Semantic Conventions as much as possible. Also, adopt Spring usual non-null-by-default strategy as much as possible.
* Align vector store conventions to the chat model ones, and follow alphabetical order for values. This is particularly useful for the convention classes, for which the Micrometer performance of exporting telemetry data improves when key values are added already sorted to the context.
* Fix flaky test in Mistral AI.
* Improve Qdrant integration tests.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
PgVectorFilterExpressionConverter was generating incorrect SQL for
IN and NOT IN filters with PostgreSQL JSON data types. This caused
BadSqlGrammarException errors when executing queries.
This change modifies the converter to generate correct SQL syntax
for these operations, ensuring compatibility with PostgreSQL's JSON
handling capabilities.
Why:
- Improves query reliability for PgVector stores
- Enables more complex filtering operations on JSON data
- Eliminates unexpected errors in query execution
Fixes#1179
Implementation:
- Introduce AbstractObservationVectorStore with instrumentation for add, delete, and similaritySearch methods
- Create VectorStoreObservationContext to capture operation details
- Implement DefaultVectorStoreObservationConvention for naming and tagging
- Add VectorStoreObservationDocumentation for defining observation keys
- Create VectorStoreObservationAutoConfiguration for auto-configuring observations
- Add VectorStoreObservationProperties to control optional observation content filters
- Update VectorStore interface with getName() method
- Modify PgVectorStore and SimpleVectorStore to extend AbstractObservationVectorStore
- Add vector_store Spring AI kind
Filters:
- Implement VectorStoreQueryResponseObservationFilter
- Add VectorStoreDeleteRequestContentObservationFilter and VectorStoreAddRequestContentObservationFilter
Enhancements:
- Update PgVectorStoreAutoConfiguration to support observations
- Add observation support to PgVectorStore's Builder
- Add VectorStoreObservationContext.Operation enum with ADD, DELETE, and QUERY options
Tests:
- Add tests for VectorStore context, convention, and filters
- Add VectorStoreObservationAutoConfiguration tests
- Add PgVectorObservationIT
Resolves#1205
This change allows users to specify custom names, facilitating management of multiple vector databases within a single database instance.
Key changes:
- Implement configurable schema, table, and index names for PgVectorStore
- Add properties to set custom schema and table names
- Introduce optional schema/table & field validation for custom configurations
- Include additional tests for new configurations and existing deployments
Additional improvements:
- Rename properties to schemaName, tableName, and schemaValidation
- Update pgvector documentation with new properties
- Add schema/table name tests to PgVectorStoreAutoConfigurationIT and PgVectorStorePropertiesTests
- Create standalone PgVectorSchemaValidator class for schema/table validation
- Add missing 'CREATE SCHEMA IF NOT EXISTS' when initializeSchema=true
- Remove redundant code and classes
Resolves#747
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
* Rename the ModelClient class hierarchy into Model:
- Rename ModelClient into Model. Update all code and doc references.
- Rename ChatClient to ChatModel. Update all ChatClient suffixes and chatClient fields and variables in code and doc.
- Rename EmbeddingClient into EmbeddingModel. Update the XxxEmbeddingClient class and variable suffixes and embeddingClient variables and fields in code and docs.
- Rename ImageClient into ImageModel.
- Rename SpeechClient into SpeechModel.
- Rename TranscriptionClient into TranscriptionModel.
- Update all javadocs and antora pages. Update the related diagrams.
* Create fluent API in ChatClient interface that now includes streaming support
* Add OpenAI FunctionCallbackWrapper2IT auto-config tests.
* Add ChatClientTest mockito testing.
* Add ChatModel#getDefaultOptions(), and remove @FunctionalInterface
* ChatModel enums extend the new ModelDescription interface.
* Implement fromOptions copy method in every ChatOptions implementation.
* Extend ChatClient to use the model default options if not provided explicitly.
* Update readme to provide guidance on how to adapt to breaking changes.
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
Co-authored-by: Mark Pollack <mpollack@vmware.com>
- Resolve an issue where a new index keeps getting created during application start up.
- Solution is is to create an index only if an index on the embedding column does not exist.
- Add missing index name.
