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
Apply batching when adding Documents to the following vector stores:
- Chroma
- ElasticSearch
- Neo4j
- Qdrant
- Redis
- Typesense
- Weaviate
This improves efficiency by processing multiple Documents at once instead of individually, reducing the overhead for each operation.
Related to #1261
- Precompute all embeddings using a BatchingStrategy before inserting into the vector store
This optimization improves efficiency when adding multiple documents
Related to #1261
- Based on the pattern established in other vector store support implementations,
added a builder class as an inner class of the GemfireVectorStoreConfig class which
is also moved as an inner class to GemfireVectorStore.
Based on the original PR: https://github.com/spring-projects/spring-ai/pull/1168
* 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.
- When embedding documents, allow batching the documents using some criteria.
- `BatchingStrategy` interface with a `TokenCountBatchingStrategy` implementation that uses
the openai max input token size of 8191 as the default.
- Add a default method in EmbeddingModel to embed document using this new batching strategy.
- Change `MilvusVectorStore` to make use of this new batching API.
- Adding unit tests for `TokenCountBatchingStrategy`.
- Adding openai integration test to call the embed API that uses batching.
Resolves https://github.com/spring-projects/spring-ai/issues/1214
Other vector stores will be updated seperately
PgVectorFilterExpressionConverter was generating incorrect SQL for
IN and NOT IN filters with PostgreSQL JSON data types. This caused
BadSqlGrammarException errors when executing queries.
This change modifies the converter to generate correct SQL syntax
for these operations, ensuring compatibility with PostgreSQL's JSON
handling capabilities.
Why:
- Improves query reliability for PgVector stores
- Enables more complex filtering operations on JSON data
- Eliminates unexpected errors in query execution
Fixes#1179
Implementation:
- Introduce AbstractObservationVectorStore with instrumentation for add, delete, and similaritySearch methods
- Create VectorStoreObservationContext to capture operation details
- Implement DefaultVectorStoreObservationConvention for naming and tagging
- Add VectorStoreObservationDocumentation for defining observation keys
- Create VectorStoreObservationAutoConfiguration for auto-configuring observations
- Add VectorStoreObservationProperties to control optional observation content filters
- Update VectorStore interface with getName() method
- Modify PgVectorStore and SimpleVectorStore to extend AbstractObservationVectorStore
- Add vector_store Spring AI kind
Filters:
- Implement VectorStoreQueryResponseObservationFilter
- Add VectorStoreDeleteRequestContentObservationFilter and VectorStoreAddRequestContentObservationFilter
Enhancements:
- Update PgVectorStoreAutoConfiguration to support observations
- Add observation support to PgVectorStore's Builder
- Add VectorStoreObservationContext.Operation enum with ADD, DELETE, and QUERY options
Tests:
- Add tests for VectorStore context, convention, and filters
- Add VectorStoreObservationAutoConfiguration tests
- Add PgVectorObservationIT
Resolves#1205
* Change default schema initialization of vector stores from `true` to `false.`
Users need to explicitly opt-in for schema initialization by setting the
`initialize-schema` property on the corresponding vector store.
* Update integration tests
* Update docs
Fixes#907
Currently, `RedisVectorStoreAutoConfiguration` creates its own
configuration to connect with Redis. This commit reuse
`RedisAutoConfiguration` from spring boot project. It's limited
to Jedis.
- provides a flexible schema, can be combined with a vector store, and supports time-to-live rows.
- fix initialize-schema docs and so that it actually works.
- move CommonVectorStoreProperties to .vectorstore. package
- add CassandraAutoConfiguration to the AutoConfiguration.imports
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
This change allows users to specify custom names, facilitating management of multiple vector databases within a single database instance.
Key changes:
- Implement configurable schema, table, and index names for PgVectorStore
- Add properties to set custom schema and table names
- Introduce optional schema/table & field validation for custom configurations
- Include additional tests for new configurations and existing deployments
Additional improvements:
- Rename properties to schemaName, tableName, and schemaValidation
- Update pgvector documentation with new properties
- Add schema/table name tests to PgVectorStoreAutoConfigurationIT and PgVectorStorePropertiesTests
- Create standalone PgVectorSchemaValidator class for schema/table validation
- Add missing 'CREATE SCHEMA IF NOT EXISTS' when initializeSchema=true
- Remove redundant code and classes
Resolves#747
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
- Adds spring boot auto-configuration support for GemFireVectorStore
- Adds integration test GemFireVectorStoreAutoConfigurationIT
- Includes gemfire-testcontainers in integration tests
- Adds unit test GemFireVectorStorePropertiesTests
- Refactors GemFireVectorStore.java extracting GemFireVectorStoreConfig.java
- Renames spring-ai-gemfire to spring-ai-gemfire-store
- Adds GemFireConnectionDetails
- Adds GemFireVectorStoreProperties with default values
- Remove gemfire-release-repo maven repository
Co-authored-by: Louis Jacome <louis.jacome@broadcom.com>
Co-authored-by: Jason Huyn <jason.huynh@broadcom.com>
- Add `withContentFieldName` and `withDistanceMetadataFieldName` to Pinecone Config Builder
- Add `spring.ai.vectorstore.pinecone.contentFieldName` and `spring.ai.vectorstore.pinecone.distanceMetadataFieldName`
properties to the auto-config
- Default content field name: "document_content", distance field: "distance"
- Update tests and docs
Resolves#882
- knn instead of script_score, removed initialization
- only using normalized similarities, adjusted unit test
- making l2norm's distances consistent with others
- update dependency version and docs
- upate autoconfigure ITs
