- 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
* 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>
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>
