- Replace class-by-class scanning with comprehensive package-level scanning to capture
all JSON-annotated classes within each model's package hierarchy.
- Update tests to verify registered types and add specific type assertions.
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Enhance McpToolUtils to handle base64-encoded images in JSON responses
- Add Base64Wrapper record to parse JSON structures containing base64 image data
- Implement image conversion in DefaultToolCallResultConverter to encode RenderedImage as base64 PNG
- Add tests for DefaultToolCallResultConverter including image conversion
- Gracefully handle unsupported JSON structure for base64 wrappers
Signed-off-by: Alexandre Roman <alexandre.roman@broadcom.com>
Implement MistralAI moderation capabilities to detect potentially harmful content.
This allows Spring AI applications to use Mistral's content moderation services
to identify and filter inappropriate content before processing
- Add MistralAiModerationApi for interacting with Mistral's moderation endpoints
- Create MistralAiModerationModel implementing the ModerationModel interface
- Add configuration properties and auto-configuration for the moderation model
- Extend Categories and CategoryScores with additional moderation categories
- Add integration tests to verify moderation functionality
Signed-off-by: Ricken Bazolo <ricken.bazolo@gmail.com>
Introduce a new ToolExecutionEligibilityChecker interface to provide a more flexible way to determine
when tool execution should be performed based on model responses. This abstraction replaces the
hardcoded logic previously scattered across the codebase.
- Adds a new ToolExecutionEligibilityChecker interface in spring-ai-core
- Integrates the checker into OpenAiChatModel with appropriate defaults
- Updates OpenAiChatAutoConfiguration to support the new interface
- Provides a default implementation that maintains backward compatibility
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
refactor: Replace ToolExecutionEligibilityChecker with ToolExecutionEligibilityPredicate
- Replacing ToolExecutionEligibilityChecker with ToolExecutionEligibilityPredicate
- Changing from Function<ChatResponse, Boolean> to BiPredicate<ChatOptions, ChatResponse>
- Adding a DefaultToolExecutionEligibilityPredicate implementation
- Updating AnthropicChatModel and OpenAiChatModel to use the new predicate
- Updating auto-configurations to inject the new predicate
- Adding comprehensive tests for the new predicate implementation
The new approach provides a cleaner and more consistent way to determine when tool execution should be performed based on both prompt options and chat responses.
Add Bedrock Converse support
add mistral support
Add ollama and vertex gemini
add ToolExecutionEligibilityPredicate docs
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Avoid overlapping package names
Changed in spring-ai-commons package from org.sf.ai.model to org.sf.ai.content
Refactor advisor module name to be spring-ai-advisors-vector-store
Moved advisors into org.springframework.ai.chat.client.advisor.vectorstore
- Created top level memory directory
- Create new module spring-ai-model-chat-memory-neo4j and moved neo4j memory classes out of the vectorstore module
Updated neo4j autoconfiguation
Extract functionality from spring-ai-core into dedicated modules:
- spring-ai-commons: Common utilities and document handling
- spring-ai-model: Core model interfaces and implementations
- spring-ai-vector-store: Vector store abstraction and implementation
This modularization creates clearer responsibility boundaries and allows
consumers to include only what they need. The restructuring will make the
codebase easier to maintain and extend as the project grows.
