This change updates the type of portable chat options from Float to
Double. Affected options include:
- frequencyPenalty
- presencePenalty
- temperature
- topP
The motivation for this change is to simplify coding. In Java, Float
values require an "f" suffix (e.g., 0.5f), while Double values don't
need any suffix. This makes Double easier to type and reduces
potential errors from forgetting the "f" suffix.
APIs, tests, and documentation have been updated to reflect this
change.
Fixes gh-712
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
- Precompute all embeddings using a BatchingStrategy before inserting into the vector store
This optimization improves efficiency when adding multiple documents
Related to #1261
Implement function call capability for MiniMax model and add unit tests based on new tool classes.
Address most scenarios, but note limitations in complex English contexts
with multiple function calls. Weather query example: may stop
prematurely when querying multiple locations due to single-location
parameter limit. This behavior stems from model performance constraints.
Streaming function calling is not passing tests, will be address seperately.
Resolves#1077
Implement function call capability for the Moonshot model. Include unit
tests to verify the new functionality. This feature addresses the
requirements outlined in issue #1058.
fix: MiniMax function call
review
This commit introduces the OpenAiModerationModel and related classes:
- Add OpenAiModerationModel for content moderation
- Create OpenAiModerationOptions for configuration
- Implement OpenAiModerationProperties for Spring Boot setup
- Add integration tests in OpenAiModerationModelIT
- Add documentation
Co-authored-by: hemeda3 <hemeda3@users.noreply.github.com>
- Based on the pattern established in other vector store support implementations,
added a builder class as an inner class of the GemfireVectorStoreConfig class which
is also moved as an inner class to GemfireVectorStore.
Based on the original PR: https://github.com/spring-projects/spring-ai/pull/1168
* 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>
Remove the requirement of MongoDB vector store auto-configuration only after `MongoDataAutoConfiguration`.
This enables the `MongoDBAtlasVectorStoreAutoConfiguration` to properly provide custom Mongo conversions.
In `MongoDBAtlasVectorStoreIT` and `MongoDbVectorStoreObservationIT` tests, properly provision the `MongoTemplate`
with custom conversions as these tests are not relying on auto-configuration.
- When embedding documents, allow batching the documents using some criteria.
- `BatchingStrategy` interface with a `TokenCountBatchingStrategy` implementation that uses
the openai max input token size of 8191 as the default.
- Add a default method in EmbeddingModel to embed document using this new batching strategy.
- Change `MilvusVectorStore` to make use of this new batching API.
- Adding unit tests for `TokenCountBatchingStrategy`.
- Adding openai integration test to call the embed API that uses batching.
Resolves https://github.com/spring-projects/spring-ai/issues/1214
Other vector stores will be updated seperately
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
- Implement observable chat responses in DefaultChatClient
- Add ChatClientObservationContext and related classes for metrics
- Update ChatClient and builder methods to support ObservationRegistry
- Enhance RequestResponseAdvisor with getName() method
- Add ChatClient streaming observability support
- Introduce ChatClientObservationDocumentation for metric key names
- Create DefaultChatClientObservationConvention for implementing conventions
- Add ChatClientInputContentObservationFilter for optional input content logging
- Update ChatClientAutoConfiguration to include new observation components
- Extend ChatClientBuilderProperties with observation configuration options
- Add unit tests for new observation classes and configurations
- Update AiOperationType and AiProvider enums with new values
- Implement safeguards and warnings for sensitive data in observations
Resolves#1206
* Surface more configuration APIs to ChatOptions
* Use abstraction in Observations directly instead of dedicated implementation
* Simplify metadata config in observations for defined models
* Improve merging of runtime and default options in OpenAI
* Fix missing option in Mistral AI
Relates to gh-1148
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
- Added support for OpenAI's structured outputs feature, which allows specifying a JSON schema for the model to match
- Introduced new record to configure the desired response format
- Added support for configuring the response format via application properties or the chat options builder
- Extend teh BeanOutputConverter to help generate JSON schema from a target domain object and convert the response.
- Added comprehensive tests to cover the new response format functionality
Resolves#1196
- Add org-id and project-id properties with unified merging logic
- Update autoconfig and docs for all OpenAI models
- Introduce OpenAiChatOptions#httpHeaders option
- Add integration test for httpHeaders and update docs
Resolves#1141
* Observation APIs for chat, embedding and image models
* Conventions based on OpenTelemetry Semantic Conventions for GenAI
* Instrumentation for OpenAI chat, embedding, and image models
* Autoconfiguration for observability for OpenAI
Fixes gh-953
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
The autoconfiguration adds the FunctionCallbacks directly to the model's ChatOptions,
which results in the FunctionCallback being included in the request each time it is called.
The modification registers the container's FunctionCallback directly to the model's functionCallbackRegister
using the parent AbstractToolCallSupport constsructor.
Replace the handleFunctionCallbackConfigurations by simplified runtimeFunctionCallbackConfigurations.
Co-authored-by Christian Tzolov <ctzolov@vmware.com>
* Fixing auto configured beans where they are missing `@ConditionalOnMissingBean`
* Add `matchIfMissing` on `@ConditionalOnProperty` where it is missing with value `true`
Resolves https://github.com/spring-projects/spring-ai/issues/868
- extend the OllamaApi with Tool, ToolCalls and Function and add Tool Role.
- make OllamaChatModel extend the AbstractToolCallSupport.
- extend the OllamaChatModel to convert the Spring AI messages to OllamaApi messages, including Tools.
- OllamaOptions implements FunctionCallingOptions.
- add OllamaApiToolFunctionCallIT for testing function calling.
- patch the AbstractToolCallSupport#isToolCall to take set of stop resons.
- add FunctionCallbackInPromptIT and FunctionCallbackWrapperIT function calling auto-configuration tests.
- extend OllamAutoConfiguration to support function calling registration.
- add function call tests to OllamChatAutoConfigurationIT.
- add OllamaWithOpenAiChatModelIT to OpenAi that uses the OpenAI API to call Ollama.
- move buildToolCallConversation and handleToolCalls to parent AbstractToolCallSupport.
- update Docs. add Ollama function call docs.
- update Ollama diagrams.
Resolves#720
- Breaking changes: Classes from the org.springframework.ai.openai.metadata.audio.transcription package have been moved to the org.springframework.ai.audio.transcription package.
- The AzureOpenAiAudioTranscriptionModel has been added to the auto-configuration.
- The spring.ai.azure.openai.audio.transcription prefix was introduced for properties.
- Introduces options properties which cover all of them (see: AzureOpenAiAudioTranscriptionOptions).
- fix missing MutableResponseMetadata
- add docs
- adjust code to updated ResponseMetadata design
- add test to AzureOpenAiAutoConfiguration
- add missing AzureOpenAiAudioTranscriptionModel tests
* Change default schema initialization of vector stores from `true` to `false.`
Users need to explicitly opt-in for schema initialization by setting the
`initialize-schema` property on the corresponding vector store.
* Update integration tests
* Update docs
Fixes#907
Currently, `RedisVectorStoreAutoConfiguration` creates its own
configuration to connect with Redis. This commit reuse
`RedisAutoConfiguration` from spring boot project. It's limited
to Jedis.
* Remove inheritance from HashMap
* No more subclasses per model provider
* Builder class for ChatResponse
* Fix the AbstractResponseMetadata#AI_METADATA_STRING parameter order
* ChatResponseMetadata ignore Null values.
- provides a flexible schema, can be combined with a vector store, and supports time-to-live rows.
- fix initialize-schema docs and so that it actually works.
- move CommonVectorStoreProperties to .vectorstore. package
- add CassandraAutoConfiguration to the AutoConfiguration.imports
- re-assign the model on withSystemInstruction return.
- deprecate the gemini-pro-vision model and replace the default model to gemin-pro-1.5
- update docs.
- improve system message handling implementation.
Resolves#1030
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
