- 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>
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>
- Add new Maven profile 'ci-fast-integration-tests' for running selective ITs
- Remove redundant vector store skip flags from properties section
- Update maven-failsafe-plugin to version 3.5.2
- Configure test exclusions for various components:
- Most model integration tests (Anthropic and OpenAI)
- Most vector store tests (except PgVector and Chroma)
- Most auto-configuration tests
- All test containers and docker compose tests
- AI evaluation tests
- Convert the docker-compose tests into ITs
- Convert the testcontainers tests into ITs
- Updated README.md
- Explain the new profile and also the new integration tests repo
- Describe ways to run integration tests for specific modules
- Add badge for https://github.com/spring-projects/spring-ai-integration-tests
* Add property `spring.ai.vectorstore.opensearch.aws.domain-name`
* Require `AwsCredentialsProvider` to enable `AwsOpenSearchConfiguration`
* Add Testcontainers Service Connection support
* Add Docker Compose Service Connection support
- Add maven properties for all vector stores such as
skip.vectorstore.azure-cosmos-db to control IT test execution
- Chroma and PGVector IT tests are enabled by default
- Docker Compose and Testcontainers module ITs are skipped by default
- Add parallel job to run docker-compose and testcontainers ITs
- Disable project-wide Checkstyle checks to unblock development
- Add documentation for enabling Checkstyle locally
- Fix remaining checkstyle violations in current codebase
Fixes#1669
Implement service connection support for MongoDB Atlas using both
TestContainers and Docker Compose. This change enables easier
integration testing and local development with MongoDB Atlas.
- Leverage TestContainers 1.20.2 which introduces MongoDBAtlasLocalContainer
- Add TestContainers support using MongoDBAtlasLocalContainer
- Implement Docker Compose configuration for MongoDB Atlas
- Create connection details factories for both TestContainers and Docker Compose
- Update dependency management for MongoDB Atlas integration
- Add integration tests for both TestContainers and Docker Compose setups
- Update documentation to include MongoDB Atlas support
Modify getHost method to return a properly formatted URL string. This
ensures that the Chroma client can correctly connect to the service
when using Docker Compose.
Fixes#1395
Currently, `RedisVectorStoreAutoConfiguration` creates its own
configuration to connect with Redis. This commit reuse
`RedisAutoConfiguration` from spring boot project. It's limited
to Jedis.
Currently, Docker Compose and Testcontainers support `CHROMA_SERVER_AUTHN_CREDENTIALS`,
which is provided by Chroma 0.5.x. Previous versions use `CHROMA_SERVER_AUTH_CREDENTIALS`
instead.
