- 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
Adds @EnabledIfEnvironmentVariable annotation to integration tests that
use OpenAI embeddings. Tests will be skipped if OPENAI_API_KEY is not set,
making the build process more reliable for contributors who don't have
access to OpenAI services.
* Unify image definition for vector stores in vector-store modules
* Unify image definition for vector stores in spring-ai-testcontainers module
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Vector store observations support several key-value pairs, coming from the Spring AI abstractions. Currently, whenever a value is not available (either because not configured by the user or not supported by the vector store provider), span/metrics attributes are generated anyway with value none.
That causes several issues, including an unneeded increase in time series, challenges in alerting/monitoring (especially for integer/double attributes that suddenly are populated with a string), and non-compliance with the OpenTelemetry Semantic Conventions (according to which, attributes should be excluded altogether if there's no value).
This pull request changes the conventions for vector store observations to exclude the generation of span/metrics attributes for optional values which don't have any value.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Testcontainers 1.20.2 offers `MongoDBAtlasLocalContainer`. Previous
image used during tests was pulling images in every execution. The
new image is cached locally and can be executed offline.
Apply batching when adding Documents to the following vector stores:
- Azure vector store
- Cassandra
- MongoDB Atlas
- OpenSearch
- Oracle
- Pinecone
This improves efficiency by processing multiple Documents at once instead of individually, reducing the overhead for each operation.
Related to #1261
* Consolidate usage of “db.collection.name” attribute to track table name, collection name, index name, document name, or whatever concept a vector database uses to store data. Removed “db.index” that was use sometimes instead of “db.collection.name”. This usage is in line with the OpenTelemetry Semantic Conventions.
* Configure query response content to be included as a “span event” instead of a “span attribute” if the backend system supports that, similar to how we do for the model observations.
* Structure vector store observation attributes in dedicated enums, including one for the Spring AI Kinds to avoid hard-coding the same value in a lot of places. This follows the OpenTelemetry Semantic Conventions as much as possible. Also, adopt Spring usual non-null-by-default strategy as much as possible.
* Align vector store conventions to the chat model ones, and follow alphabetical order for values. This is particularly useful for the convention classes, for which the Micrometer performance of exporting telemetry data improves when key values are added already sorted to the context.
* Fix flaky test in Mistral AI.
* Improve Qdrant integration tests.
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Remove the requirement of MongoDB vector store auto-configuration only after `MongoDataAutoConfiguration`.
This enables the `MongoDBAtlasVectorStoreAutoConfiguration` to properly provide custom Mongo conversions.
In `MongoDBAtlasVectorStoreIT` and `MongoDbVectorStoreObservationIT` tests, properly provision the `MongoTemplate`
with custom conversions as these tests are not relying on auto-configuration.
In testing the docker mongodb/atlas container doesn't throw an exception when
calling `mongoTemplate.executeCommand({"createSearchIndexes": ...});`. However,
when using the Atlas service it does and throws a `IndexAlreadyExists` exception.
Added error handling for error code 68 or error code name `IndexAlreadyExists`.
See: https://www.mongodb.com/docs/manual/reference/error-codes/Fixes#910
* Rename the ModelClient class hierarchy into Model:
- Rename ModelClient into Model. Update all code and doc references.
- Rename ChatClient to ChatModel. Update all ChatClient suffixes and chatClient fields and variables in code and doc.
- Rename EmbeddingClient into EmbeddingModel. Update the XxxEmbeddingClient class and variable suffixes and embeddingClient variables and fields in code and docs.
- Rename ImageClient into ImageModel.
- Rename SpeechClient into SpeechModel.
- Rename TranscriptionClient into TranscriptionModel.
- Update all javadocs and antora pages. Update the related diagrams.
* Create fluent API in ChatClient interface that now includes streaming support
* Add OpenAI FunctionCallbackWrapper2IT auto-config tests.
* Add ChatClientTest mockito testing.
* Add ChatModel#getDefaultOptions(), and remove @FunctionalInterface
* ChatModel enums extend the new ModelDescription interface.
* Implement fromOptions copy method in every ChatOptions implementation.
* Extend ChatClient to use the model default options if not provided explicitly.
* Update readme to provide guidance on how to adapt to breaking changes.
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
Co-authored-by: Mark Pollack <mpollack@vmware.com>
`Reuse Container` is a Testcontainers experimental feature. It requires
`testcontainers.reuse.enable=true` in `~/.testcontainers.properties` in
order to take effect but in order to avoid surprises, this commit remove it.
See https://java.testcontainers.org/features/reuse/
- Add VectorSearchAggregation used to actually preform the search
on a given collection with embeddings.
- add MongoDBVectorStore
- Add MongoDBVectorStoreIT. Integration test runs fine given...
- You have a mongo atlas cluster to connect to (local or remote)
- You have the search index "spring_ai_vector_search" setup correctly
- Need to explore getting around this
- Need to filter results using threshold
- Add postfilter for threshold values - While a post filter is not ideal,
it gets the job done. The mongo team seems to be working on having
it availible as a prefilter option, in which this implementation
can be updated to use later.
- implement filtering threshold
- fix a few sonar issues
- formatting
- use higher default num_candidates
- use builder for configuration
- add documentation and some refactor
- use consistent property in integration test
- finish implementing filter support
- add documentation to filter converter
- add vector search index auto creation
- Add to BOM.
- Fix version to 1.0.0-SN.
- Move expresion converter from core to models/mongodb.
- Fix style and license headers
