- 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>
Fixes: #938
Issue link: https://github.com/spring-projects/spring-ai/issues/938
This commit integrates Couchbase as a vector store option in Spring AI, providing:
- CouchbaseSearchVectorStore implementation with vector similarity search capabilities
- Support for metadata filtering with SQL++ expression conversion
- Spring Boot auto-configuration and starter module for easy integration
- Comprehensive documentation covering setup, configuration, and usage examples
- Integration tests using TestContainers with Couchbase 7.6
The implementation supports configuring dimensions, similarity functions (dot_product/l2_norm),
and optimization strategies (recall/latency). Schema initialization is now opt-in via
the initializeSchema property. Documentation includes both auto-configuration and
manual configuration instructions, along with property configuration details.
Signed-off-by: Abhiraj <abhiraj.official15@gmail.com>
co-authored-by: Laurent Doguin <laurent.doguin@gmail.com>
- Update ToolCallback import to use relative package path
- Remove qodana.yaml file from the project
Signed-off-by: gongzhongqiang <gongzhongqiang@apache.org>
This change ensures that @Tool annotated methods can be properly discovered even when the tool objects are wrapped
in Spring AOP proxies, which is common when using aspects or other proxy-based features.
- Enhance MethodToolCallbackProvider to properly handle AOP proxied tool objects by detecting proxies
and retrieving their target classes when scanning for @Tool annotated methods.
- Add test suite in MethodToolCallbackProviderAopTests.java to verify AOP proxy handling
Resolves#2356
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Adding `equals` and `hashCode` methods for proper object comparison.
- Implementing a deep `copy()` method, creating new instances of mutable collections (List, Set, Map, Metadata) to prevent shared state.
- Adding `AzureOpenAiChatOptionsTests` to verify `copy()`, builders, setters, and default values.
Signed-off-by: Alexandros Pappas <apappascs@gmail.com>
- Reorganize sections to focus on Milestones vs Snapshots instead of specific versions
- Add Central Portal Snapshots repository configuration for both Maven and Gradle
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Fix Embedding equals() using Arrays.equals() for embedding array comparison
- Fix Embedding hashCode() using Arrays.hashCode() for proper array hashing
- Add final modifiers to fields in Embedding classes
Signed-off-by: Soby Chacko <soby.chacko@broadcom.com>
- Add null check for isError() method in SyncMcpToolCallback
- Implement consistent error handling in AsyncMcpToolCallback to match SyncMcpToolCallback behavior
- Throw IllegalStateException with error content when tool calls fail
Resolves#2447
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
Co-authored-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Add SseHttpClientTransportAutoConfigurationTests to verify conditional bean creation for HTTP client transports
- Add SseWebFluxTransportAutoConfigurationTests to verify conditional bean creation for WebFlux client transports
- Test both auto-configurations with various conditions including presence/absence of WebFluxSseClientTransport and MCP client enabled/disabled states
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
Existing example displays a typo in the code example for Runtime Options:
ChatResponse response = chatModel.call(
new Prompt(
"Generate the names of 5 famous pirates.",
OpenAiChatOptions.builder()
.model("gpt-4-o")
.temperature(0.4)
.build()
));
Using gpt-4-0 generates this error:
org.springframework.ai.retry.NonTransientAiException: 404 - {
"error": {
"message": "The model `gpt-4-o` does not exist or you do not have access to it.",
"type": "invalid_request_error",
"param": null,
"code": "model_not_found"
}
}
Changing the model String in the example to "gpt-4o" resolves this.
Replace direct dependencies with ObjectProvider pattern in SSE transport auto-configurations.
- Uses ObjectProvider for ObjectMapper and WebClient.Builder dependencies
- Removes redundant @ConditionalOnMissingBean methods
- Provides fallback instantiation when dependencies are not available
Resolves#2440
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Add prefixedToolName utility method to ensure consistent tool name formatting
- E nforce alphanumeric, underscore, and hyphen characters only in tool names
- Limit tool names to 64 characters maximum
- Use original tool name in actual calls while using formatted names in definitions
- Add error handling for tool call responses in SyncMcpToolCallback
- Update tests to reflect the changes
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Remove deprecations from models, vector stores and usage
- Deprecations from FunctionCallback and ObservationContext/Convention will be in a separate PR
Models updates
- Remove AbstractToolCallSupport from the models which use ToolCallingManager
- Remove deprecated constructors and their usage
- Remove FunctionCallbackResolver and FunctionCallbacks usage in the models
- Add back deprecations for VectorStoreChatMemoryAdvisor until builder is fixed
- Update OpenAiPaymentTransactionIT to use ToolCallbackResolver in config
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
Enhances MCP tool naming by prefixing tool names with their client + transport name
to prevent conflicts when multiple MCP clients expose tools with the same name.
- Modifies client info in McpClientAutoConfiguration to include transport name
- Updates SyncMcpToolCallback and AsyncMcpToolCallback to prefix tool names with client + transport name
- Adds test coverage for tools with identical names but different client info
Resolves#2393
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Implement the new ToolCallback.call(String, ToolContext) method in both Sync and Async MCP tool callbacks.
- Since MCP tools don't support tool context, the implementation ignores the context parameter and
delegates to the existing call(String) method. Added test to verify the behavior.
Resolves#2378
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
When streaming happens on an event loop, due to the fact that currently
tools can only be synchronous and blocking, every tool call needs to be
offloaded to a blocking-safe Scheduler.
Resolves#2341
Signed-off-by: Dariusz Jędrzejczyk <dariusz.jedrzejczyk@broadcom.com>
When using the RetrievalAugmentationAdvisor with the VectorStoreDocumentRetriever, it’s now possible to provide a filter expression at request-time as an advisor context variable with key VectorStoreDocumentRetriever.FILTER_EXPRESSION.
Fixes gh-1776
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
This commit introduces the `JsoupDocumentReader` and `JsoupDocumentReaderConfig` classes, which provide functionality to read and parse HTML documents using the JSoup library.
The reader supports:
- Extracting text from specific HTML elements using CSS selectors.
- Extracting all text from the body of the document.
- Grouping text by element.
- Extracting metadata, including the document title, meta tags, and link URLs.
- Reading from various resource types (files, URLs, byte arrays).
- Configurable character encoding, selector, separator, and metadata extraction.
This new reader enhances Spring AI's ability to process web content and other HTML-based data sources.
Signed-off-by: Alexandros Pappas <apappascs@gmail.com>
- Make MCP client initialization blocking with .block() call
- Upgrade MCP SDK version from 0.7.0 to 0.8.0-SNAPSHOT
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
