- Add missing formats "wav" and "pcm" which OpenAI supports but SpringAI does not have serializers for
Signed-off-by: gabriel duncan <gabrielduncan@Mac.attlocal.net>
When generating the JSON Schema for a tool input from a method, ToolContext is now excluded since it's not something we want the model to provide. The framework takes care of passing a value for it when actually executing the tool call.
Fixes gh-2366
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
- Fix to include the correct top level property to enable/disable image/audio models
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Remove batch strategy usage as it is taken care by the vector store parent builder
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
Add de-duplication logic for tools in the MCP server configuration, ensuring that tools
with the same name are not registered multiple times. The implementation keeps the
first occurrence of each tool name and discards duplicates.
- Modified toSyncToolSpecifications and toAsyncToolSpecification methods to de-duplicate tools
- Updated tests to verify that duplicate tools are properly filtered out
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Since the model autoconfiguration enable/disable flags are no longer used, remove them
- Currently, the model autoconfigurations can be enabled/disabled via top level Spring AI properties such as spring.ai.model.chat/embedding/image/moderation=<model-provider-name>
- Update documentation to add note section about this change
- Update note on the autoconfiguration section to point to the configuration changes
- Align the vertex ai text/multimodal keys in line with the other properties
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Rename Registration classes to Specification (SyncToolRegistration → SyncToolSpecification)
- Update transport classes to use Provider suffix (WebFluxSseServerTransport → WebFluxSseServerTransportProvider)
- Add exchange parameter to handler methods for better context passing
- Introduce McpBackwardCompatibility class to maintain backward compatibility
- Update MCP Server documentation to reflect new API patterns
- Add tests for backward compatibility
- Update mcp version to 0.8.0
- Add mcp 0.8.0 breaking change note-
The changes align with the MCP specification evolution while maintaining backward compatibility through deprecated APIs.
refactor: Extract MCP tool callback configuration into separate auto-configuration
Extracts the MCP tool callback functionality from McpClientAutoConfiguration into a
new dedicated McpToolCallbackAutoConfiguration that is disabled by default.
- Created new McpToolCallbackAutoConfiguration class that handles tool callback registration
- Made tool callbacks opt-in by requiring explicit configuration with spring.ai.mcp.client.toolcallback.enabled=true
- Removed deprecated tool callback methods from McpClientAutoConfiguration
- Updated ClientMcpTransport references to McpClientTransport to align with MCP library changes
- Added tests for the new auto-configuration and its conditions
refactor: standardize tool names to use underscores instead of hyphens
- Change separator in McpToolUtils.prefixedToolName from hyphen to underscore
- Add conversion of any remaining hyphens to underscores in formatted tool names
- Update affected tests to reflect the new naming convention
- Add comprehensive tests for McpToolUtils.prefixedToolName method
- Add integration test for payment transaction tools with Vertex AI Gemini
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Add missing jackson-module-kotlin for the kotlin tests
- Fix Function calling options to use ToolCallingChatOptions
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Update dependencies and module names in maven pom.xml files affecting couchbase vector store support
- Rename artifact from spring-ai-couchbase-store-spring-boot-starter to spring-ai-starter-vector-store-couchbase
- Update imports and related cleanup
- Update corresponding documentation references
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Update the documentation references to replace the artifact ID name change for the model starters
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Update the latest changes to autoconfigurations, starters for the Spring AI autoconfigurations and starters
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Split model autoconfigurations based on the model
- Change the autoconfiguration class into model specific autoconfigurations - chat, embedding, image etc.,
- Update/add tests based on this change
- Make sure the conditional logic to enable the model auto configuration is at the class level so that the configuration properties as well as the models are not enabled when the model is explicitly disabled. By default, the condition will allow enabling the beans if not explicitly overridden.
- Remove spring-ai-spring-boot-autoconfigure as a dedicated auto-configuration module
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Introduce top level properties to support conditional logic
- The properties will have the format like "spring.ai.vectorstore.type=<vectorstore provider>" to enable specific vectore store implementation
- By default, these will be enabled when no specific properties are set. To disable, set any value other than the provider name for example, none"
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Conditionally enable invidividual models within the provider
- Introduce top level properties to support conditional logic
- The properties will have the format like "spring.ai.model.<chat/embedding/image etc.,>=<provider>" to enable specific chat/embedding/image/audio/moderation models by the provider. By default, these will be enabled when no specific properties are set. To disable, set any value other than the provider name for example, "none"
- For the auto configurations where the provider has multiple models, split the autoconfiguration into per model auto-configuration classes. This will enable the isolated auto-configurations for each provider and its model.
- This PR addresses this for OpenAI and others will follow in subsequent PRs
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
Update the vector store starters with the new observation autoconfig dependency
Other maven configuraiton changes
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
Move vector store auto-configuration classes to dedicated modules under auto-configurations/vector-stores/:
- Creates separate modules for Cassandra, Chroma, Elasticsearch, GemFire, HanaDB, MariaDB, MongoDB Atlas, Neo4j, OpenSearch, Oracle, PGVector, and Redis vector stores
- Updates package names to follow the pattern org.springframework.ai.vectorstore.<implementation>.autoconfigure
- Renames corresponding starter modules to follow the pattern spring-ai-starter-vector-store-<implementation>
- Updates import paths in affected classes
- Relocates test resources alongside their respective implementations
- Updates imports in spring-ai-spring-boot-docker-compose and spring-ai-spring-boot-testcontainers
This change improves modularity by allowing each vector store implementation to be
independently versioned and maintained, following the migration pattern established
with previous vector store autoconfiguraiton and starters .
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
Move vector store auto-configuration classes to dedicated modules under auto-configurations/vector-stores/:
- Creates separate modules for Milvus, Pinecone, Qdrant, and Typesense vector stores
- Moves CommonVectorStoreProperties to spring-ai-core for better reusability
- Updates pom.xml dependencies to maintain proper relationships between modules
- Name the artifacts based on the pattern spring-ai-autoconfigure-vector-store-<implementation>.
For example - spring-ai-autoconfigure-vectore-store-milvus
- Package names follow the pattern org.springframework.ai.vectorstore.<implementation>.autoconfigure
- Naming the correspondinbg starter modules accordingly (spring-ai-starter-vector-store-milvus for example).
This change improves modularity by allowing each vector store implementation to be
independently versioned and maintained, continuing the migration pattern established
with previous vector stores.
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Split spring-ai-spring-boot-autoconfigure into modules
- This PR addresses the restructuring of the following spring boot autoconfigurations:
- spring-ai retry -> common
- spring-ai chat client/model/memory -> chat
- spring-ai chat/embedding/image observation -> observation
- spring-ai chat/embedding models -> models
- Update the Spring AI BOM and boot starters with the new autoconfigure modules
- Rename the autoconfiguration and starters
- The package name for the models in autoconfiguration classes will have `org.springframework.ai.model.<name>.autoconfigure`
- Both the autoconfiguration and starters will have the prefix `spring-ai-autoconfigure-model` and `spring-ai-starter-model` respectively
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
