- Rename the artifact ID of the chat memory repository artifacts:
- `spring-ai-model-chat-memory-jdbc` -> `spring-ai-model-chat-memory-repository-jdbc`
- `spring-ai-model-chat-memory-cassandra` -> `spring-ai-model-chat-memory-repository-cassandra`
- `spring-ai-model-chat-memory-neo4j` -> `spring-ai-model-chat-memory-repository-neo4j`
- Rename the package names to include "repository". Example: org.springframework.ai.chat.memory.repository.jdbc.JdbcChatMemoryRepository
- This package renaming also requires to change the default schema location for the jdbc repository to include "repository"
- Update the docs
- Update the artifact IDs in the parent POM, BOM, autoconfiguration and starters
- Update upgrade notes and docs to describe the changes
Fix JdbcChatMemoryRepositoryPostgresqlIT
- Make sure to set the dialect via datasource
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Rename chat memory repository autoconfiguration modules to include the "repository" suffix:
- spring-ai-autoconfigure-model-chat-memory-cassandra -> spring-ai-autoconfigure-model-chat-memory-repository-cassandra
- spring-ai-autoconfigure-model-chat-memory-jdbc -> spring-ai-autoconfigure-model-chat-memory-repository-jdbc
- spring-ai-autoconfigure-model-chat-memory-neo4j -> spring-ai-autoconfigure-model-chat-memory-repository-neo4j
- Update Spring Boot starter modules to match the new naming convention.
- Rename packages to include `.repository.` for improved clarity and consistency.
- Rename configuration and related classes to use the `ChatMemoryRepository` suffix.
- Update Spring AI BOM and parent POM files to reference the new artifact names.
- Update all imports, references, and configuration to use the new package and class names.
BREAKING CHANGE:
These changes require users to update their dependencies, imports, and configuration to use the new artifact, package, and class names. See the upgrade notes for migration instructions.
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Add remaining deepseek modules
- Fix build order to put autoconfig modules after model modules to fix javadoc build
Signed-off-by: GR <gr@fastball.dev>
* ChatMemory will become a generic interface to implement different memory management strategies. It’s been moved from the “”spring-ai-client-chat” package to “spring-ai-model” package while retaining the same package, so it’s transparent to users.
* A MessageWindowChatMemory has been introduced to provide support for a chat memory that keeps at most N messages in the memory.
* A ChatMemoryRepository interface has been introduced to support different storage strategies for the chat memory. It’s meant to be used as part of a ChatMemory implementation. This is different than before, where the storage-specific implementation was directly tied to the ChatMemory. This design is familiar to Spring users since it’s used already in the ecosystem. The goal was to use a programming model similar to Spring Session and Spring Data.
* The JdbcChatMemory has been supersed by JdbcChatMemoryRepository.
* A ChatMemory bean is auto-configured for you whenever using one of the Spring AI Model starters. By default, it uses the MessageWindowChatMemory implementation and stores the conversation history in memory. If a different repository is already configured (e.g., Cassandra, JDBC, or Neo4j), Spring AI will use that instead.
* First-class documentation has been introduced to describe the ChatMemory API and related features.
* All the changes introduced in this PR are backward-compatible.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.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>
- 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>
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>
Core Architecture Changes:
- Split MCP into dedicated client/server modules
- Created separate starters: spring-ai-starter-mcp-webmvc and spring-ai-starter-mcp-webflux
- Removed property-based transport configuration in favor of auto-configuration
- Added support for multiple transport types (STDIO, WebMVC, WebFlux)
Client Improvements:
- Added support for both synchronous and asynchronous MCP clients
- Fixed client auto-configuration issues
- Added root change notification property to common properties
Configuration Enhancements:
- Improved configuration properties organization and validation
- Added ConditionalOnMissingBean for WebMvc/WebFlux configurations
- Enhanced lifecycle management and customization support
Testing and Documentation:
- Added comprehensive integration tests for McpClientAutoConfiguration
- Updated McpServerAutoConfigurationIT
- Added extensive JavaDoc documentation
- Improved MCP client/server starter documentation
- Added documentation for common utilities
- Updated navigation for new MCP documentation sections
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Spring AI's dependencies management to derive from Spring Boot 3.4.2
- Remove explicit versioning of dependencies
- Update upgrade notes for Spring Boot 3.4.2
- Fix ElasticSearch client changes with the latest dependency derived from Spring Boot 3.4.2
Adds comprehensive Model Context Protocol (MCP) integration to Spring AI, including:
Core Features:
- MCP client implementation with Spring AI tool calling capabilities
- Spring-friendly abstractions for MCP clients and servers
- Both synchronous and asynchronous MCP server operation modes
- Add MCP client autoconfiguration with support for STDIO, WebMVC and WebFlux transports
- Auto-configuration for MCP server components
- Spring Boot starter (spring-ai-starter-mcp) with WebFlux and WebMVC support
- MCP dependency management with BOM
- Add close() method to McpToolCallback for proper resource cleanup
- Add initialize flag to control MCP client initialization
- Add comprehensive integration tests and documentation for MCP client configuration
Technical Improvements:
- Split WebMvc and WebFlux configurations into separate auto-configuration classes
- Server type configurable via 'spring.ai.mcp.server.type' property (SYNC/ASYNC)
- Comprehensive test coverage including McpServerAutoConfigurationIT
- Utility classes for converting between Spring AI tools and MCP tools
- MCP SDK version management in parent pom
Reorganize MCP tool utilities and client configuration
- Rename ToolUtils to McpToolUtils for better MCP-specific naming
- Rename McpToolCallbackProvider to SyncMcpToolCallbackProvider
- Add utility methods for handling tool callbacks in McpToolUtils
- Extract client configuration logic into new McpClientDefinitions class
- Add tool callback support to ChatClient interface and implementations
- Remove redundant integration test
Introduce MCP client customization support
- Add McpSyncClientCustomizer interface for customizing MCP sync clients
- Replace McpClientDefinitions with McpSyncClientConfigurer
- Refactor MCP client initialization to support customization
- Remove redundant close() method from McpToolCallback
- Fix conditional class dependencies in WebMvc/Flux configurations
Add MCP AOT hints
Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>
- Updated `org.springframework.boot.autoconfigure.AutoConfiguration.imports` to include MariaDB vector store auto-configuration
- Created MariaDB Vector Store autoconfiguration integration tests (`MariaDbStoreAutoConfigurationIT`)
- Added MariaDB store properties configuration and tests (`MariaDbStorePropertiesTests`)
- Introduced new Maven modules:
- `spring-ai-mariadb-store`: Core MariaDB vector store implementation
- `spring-ai-starter-mariadb-store`: Spring Boot starter for MariaDB vector store
- Added `MariaDBFilterExpressionConverter` to support JSON-based metadata filtering in MariaDB
- Implemented filter expression conversion for MariaDB vector store queries
- Added README.md with documentation link for MariaDB Vector Store
- Updated project dependencies to include MariaDB JDBC driver and test containers
- Configured integration testing with TestContainers for MariaDB
- Added observability support for MariaDB vector store operations
Integrates Oracle Coherence 24.09+ as a vector store backend for Spring AI. The
implementation provides:
- Support for vector similarity search with configurable distance metrics (Cosine,
Inner Product, L2)
- Multiple indexing options including HNSW and Binary Quantization indexes
- Filter expression evaluation for metadata-based filtering
- Vector normalization capabilities
- Comprehensive test coverage including integration tests
The implementation includes:
- CoherenceVectorStore main implementation
- CoherenceFilterExpressionConverter for Spring AI to Coherence filter conversion
- Auto-configuration support via spring-boot-starter
- Documentation and usage examples
Requires Oracle Coherence 24.09 or later.
Introduces support for Amazon Bedrock Converse API through a new BedrockProxyChatModel
implementation. This enables integration with Bedrock's conversation models with features
including:
- Support for sync/async chat completions
- Stream response handling
- Tool/function calling capabilities
- System message support
- Image input support
- Observation and metrics integration
- Configurable model parameters and AWS credentials
Adds core support classes:
- BedrockUsage: Implements Usage interface for token tracking
- ConverseApiUtils: Utility class for handling Bedrock API responses including:
- Tool use event aggregation and processing
- Chat response transformation from stream outputs
- Model options conversion
- Support for metadata aggregation
- URLValidator: Utility for URL validation and normalization with support for:
- Basic and strict URL validation
- URL normalization
- Multimodal input handling
- Enhanced FunctionCallingOptionsBuilder with merge capabilities for both ChatOptions
and FunctionCallingOptions
- Added BEDROCK_CONVERSE to AiProvider enum for metrics tracking
- Extended AWS credentials support with session token capability
- Added configurable session token property to BedrockAwsConnectionProperties
Adds new auto-configuration support:
- BedrockConverseProxyChatAutoConfiguration for automatic setup of the Bedrock Converse chat model
- BedrockConverseProxyChatProperties for configuration including:
- Model selection (defaults to Claude 3 Sonnet)
- Timeout settings (defaults to 5 minutes)
- Temperature and token control
- Top-K and Top-P sampling parameters
- Integration with existing BedrockAwsConnectionConfiguration for AWS credentials
Updates to testing infrastructure:
- Adds comprehensive test suite for Bedrock Converse properties and auto-configuration
- Integration tests for chat completion and streaming scenarios
- Property validation tests for configuration options
- Temporarily disabled other Bedrock tests due to AWS quota limitations
- Added ObjectMapper configuration for proper JSON handling
Added new spring-ai-bedrock-converse-spring-boot-starter module
Updates module configuration in parent POM and BOM to include new bedrock-converse
modules and starters. Adds necessary auto-configuration imports for seamless integration
with Spring Boot applications.
Unrelated changes:
- Disabled several Bedrock model tests (Jurassic2, Llama, Titan) due to AWS quota limitations
- Disabled PaLM2 tests due to API decommissioning by Google
Resolves#809, #802
Add docs and fix configs
- Move timeout configuration from chat properties to connection properties
- Add comprehensive documentation for Bedrock Converse API usage and configuration
- Update tests to reflect configuration changes
Co-authored-by: maxjiang153 <maxjiang153@users.noreply.github.com>
Standardize AWS credential handling in integration tests
- Improve how we manage AWS credentials across our integration test
suite and ensures consistent test configuration. We're replacing individual
environment variable checks with @RequiresAwsCredentials
annotation and standardizing the use of BedrockTestUtils for context creation
in tests
We also align all AWS regions to US_EAST_1 for consistency and add missing
dependency versioning for Oracle Free.
These changes make our AWS tests more easier to maintain.
Key changes:
- Replace @EnabledIfEnvironmentVariable with @RequiresAwsCredentials
- Standardize context creation via BedrockTestUtils
- Set AWS region to US_EAST_1
- Add Oracle Free dependency version in pom.xml
- Implement core vector store module for CosmosDB integration
- Add Spring Boot auto-configuration capabilities
- Integrate batch processing strategy for optimized operations
- Include comprehensive tests for core and auto-config modules
- Add reference docs for the CosmosDB vector store support
This commit introduces support for Oracle Cloud Infrastructure (OCI)
GenAI embedding models in Spring AI. It includes:
* New OCIEmbeddingModel class for interacting with OCI GenAI API
* Auto-configuration for easy setup and integration
* Properties for configuring OCI connection and embedding options
* Documentation updates explaining usage and configuration
* Integration tests to verify functionality
Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
- add new spring-ai-vertex-ai-embedding project.
- add VertexAiTextEmbeddingModel and VertexAiMultimodalEmbeddingMode with related options configuration classes.
- add ITs
- add auto-configuraiton and boot starters.
- register to BOM.
- add documentation.
- add multimodal embedding documentation
- extend the Embedding metdata so that it can keep references to the source document's data, Id, mediatype
Resolves#1013
Related to #1009
