- 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>
Add repository element to the distributionManagement for Spring AI BOM
Add sonatype profile to Spring AI BOM
Add developer info to Spring AI BOM
Remove javadoc skip at spring-ai-spring-boot-testcontainers POM
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
- Remove spring-ai-parent POM as its parent
- This change is done to make Spring AI dependencies independent of Spring Boot
Signed-off-by: Ilayaperumal Gopinathan <ilayaperumal.gopinathan@broadcom.com>
* 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>
- Introduce new TemplateRenderer API providing the logic for rendering an input template.
- Update the PromptTemplate API to accept a TemplateRenderer object at construction time.
- Move ST logic to StTemplateRenderer implementation, used by default in PromptTemplate. Additionally, make start and end delimiter character configurable.
Relates to gh-2655
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.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 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>
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>
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>
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
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
- Define the version for Azure Cosmos DB
- Add dependencies for the azure-cosmos-db-store module and boot starter
- Include the artifacts in spring-ai-bom
Signed-off-by: jitokim <pigberger70@gmail.com>
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>
