- add concurrency to store.add(..) (bc embeddingClient is slow)
- CassandraVectorStoreAutoConfiguration uses CassandraAutoConfiguration
- driver profiles for production stability+performance,
- small cleanups and naming fixes,
- main doc tidy-up
- astradb compatibility (protocol V4)
– don't create embeddings again for documents that already have them
similar to https://github.com/spring-projects/spring-ai/pull/413
The filter expression within the document handling logic has been updated to enhance clarity and ensure accurate author filtering. Previously, the `in` function mistakenly included names as direct arguments, potentially leading to ambiguity in filter application. This has been corrected by explicitly specifying the `author` field, ensuring the filter accurately applies to author names only. This change improves the maintainability and readability of the code, particularly in sections dealing with article filtering based on authorship and type.
- Correct the filter expression by explicitly specifying the `author` field.
- Ensure accurate and unambiguous author filtering in article management.
The CassandraVectorStore is for managing and querying vector data in an Apache Cassandra db.
It offers functionalities like adding, deleting, and performing similarity searches on documents.
The store utilizes CQL to index and search vector data. It allows for custom metadata fields in
the documents to be stored alongside the vector and content data.
This class requires a CassandraVectorStoreConfig configuration object for initialization, which
includes settings like connection details, index name, field names, etc. It also requires an
EmbeddingClient to convert documents into embeddings before storing them.
A schema matching the configuration is automatically created if it doesn't exist. Missing columns
and indexes in existing tables will also be automatically created. Disable this with the disallowSchemaCreation.
This class is designed to work with brand new tables that it creates for you, or on top of existing
Cassandra tables. The latter is appropriate when wanting to keep data in place, creating embeddings
next to it, and performing vector similarity searches in-situ.
Instances of this class are not dynamic against server-side schema changes. If you change the schema
server-side you need a new CassandraVectorStore instance.
- Add auto-configure with tests.
- reformat code style
- Change field terminology to column (as appropriate for cassandra and cql)
- Add doc page with an advanced example.
- Add the dependencies to Spring AI BOM
– add to `AutoConfiguration.imports`
- Add @since annotation
- Fix javadoc issue
- Streamline the adoc content and layout
The filter expression within the document handling logic has been updated to enhance clarity and ensure accurate author filtering. Previously, the `in` function mistakenly included names as direct arguments, potentially leading to ambiguity in filter application. This has been corrected by explicitly specifying the `author` field, ensuring the filter accurately applies to author names only. This change improves the maintainability and readability of the code, particularly in sections dealing with article filtering based on authorship and type.
- Correct the filter expression by explicitly specifying the `author` field.
- Ensure accurate and unambiguous author filtering in article management.
- Implement a HanaCloudVectorStore and tests
- Implement Autoconfiguraiton + properties
- Add boot starter
- Update BOM with vector store and boot dependencies.
- Add antora docuementation
- added junit for HanaCloudVectorStoreProperties.java and documentation
to create a BTP trial account and provision an instance for SAP Hana Cloud db
- updated license, formatting and javadoc
- IT for HanaCloudVectorStoreAutoConfiguration
- IT for HanaCloudVectorStoreAutoConfiguration
Additional
- add @AutoConfiguration(after = { JpaRepositoriesAutoConfiguration.class })
- update the handa docs structure.
