This commit introduces a new boolean option 'maskSensitiveInfo' for
the MiniMax API model support in Spring AI. This feature allows users
to control whether sensitive information in the output is masked.
Relevant unit tests have been added to ensure proper functionality.
Resolves: #1216
* 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.
Enhance JsonReader to detect and process both JSON objects and arrays at
the root level. This update introduces type checking for the JSON root node
using JsonNode and leverages the Stream API to handle JSON arrays.
- Refactor parsing logic to support both JSON arrays and objects
- Rename variable: resourceArray to arrayResource for clarity
- Convert for-loop to Stream API for processing JSON keys
- Add additional tests to ensure proper array handling
Handle the case where AnthropicChatResponse content is empty to prevent
an out of bounds exception. This ensures that the model behaves correctly
even when no content is returned in the response.
- 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
The withRateLimit call is redundant as rateLimit is already handled conditionally elsewhere. Its removal helps simplify the code and improves maintainability.
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
- 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>
- Add `TEXT` to the supported ResponseFormat types
- Add JsonSchema record for structured output configuration
- Update OpenAI chat documentation with details on Structured Outputs
- Clarify usage of JSON_SCHEMA response format in properties and code examples
- Update Structured Output Converter docs to mention OpenAI Structured Outputs
- 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
- Updated OpenAiChatModel, OpenAiApi, and OpenAiStreamFunctionCallingHelper to include the `refusal` field in metadata.
- Adjusted constructors and methods to handle the new `refusal` attribute.
- Modified related tests to account for the new `refusal` field.
- Add the refusal field value to the Spring AI AssistantMessage metadata
Resolves#1178
Integrated Micrometer's Observation into the OpenAiChatModel#stream reactive chain.
Included changes:
- Added ability to aggregate streaming responses for use in Observation metadata.
- Improved error handling and logging for chat response processing.
- Updated unit tests to include new observation logic and subscribe to Flux responses.
- Refined validation of observations in both normal and streaming chat operations.
- Disabled retry for streaming which used RetryTemplate - should use .retryWhen operator as the next step.
- Added an integration test.
Resolves#1190
Co-authored-by Christian Tzolov <ctzolov@vmware.com>
* Add model and dimensions to option abstraction
* Use abstraction in Observations directly instead of dedicated implementation
* Clean-up the merge of runtime and default embedding options in OpenAI
Relates to #gh-1148
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
* Add style to option abstraction
* Use abstraction in Observations directly instead of dedicated implementation
* Clean-up the merge of runtime and default image options in OpenAI and Stability AI
Related to #1148
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
