- 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
- replace RestTemplate by RestClient but default to SimpleClientHttpRequestFactory as Chroma seems to have issues with HTTP2.
- update the documentation.
- update the ghcr.io/chroma-core/chroma version to 0.5.0. Fix the withBasicAuthCredentials.
Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
- implement OpensSearchVectorStore
- add opensearch auto-configuration and boot starter
- add documentation for OpenSearch VectorStore
- add bom dependecies
- align with to new Spirng AI API
- autoconfigure setup
- add post bean initialization and create method
- add embedding field
- create collection add nested field options
- add typesense tests
- use embedding variable instead of word vec
- check in runtime the number of documents in the collection
- add typesense expression converter
- add filter tests. add update document test and search with threshold test
- distance threshold and add distance key into metadata
- add typesesne boot starter
- add typesense docs
- add client properties in autoconfigure
- add embedding dimension method
- add typesense vector store autoconfiguration tests
- add docs to nav.adoc and vectorsdb.adoc.
- fix module name.
- move the expression converter to the typesense project.
* Move ChatClient and related classes into the chat.client package
* Move ChatModel and related class into the chat.model package
* Smaller refactorings to remove DSM cycles
* Update README.md
* 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>
Add a default collection name similar to other vector store
implementations. Currently, when using starters, qdrant requires
a collection name. Otherwise, it fails.
Currently, `username` is added as a password in `BasicAuthenticationInterceptor`.
This commit fixes the issue and also make sure the integration test
setup is correct.
Currently, `QdrantClient` and `WeaviateClient` are not exposed as
beans. Having access to those would benefit to perform operations
with an already configured client.
- Deprecate QdrantVectorStoreConfig.
- Update Qdrant manual config adoc.
- Improve Qdrant adoc.
- Update Weaviate docs.
* Add ChatBot and basic DefaultChatBot
* Add streaming ChatBot support.
* Add Evaluator interface and RelevancyEvaluator implementation
* Add Content data type abstraction for Document and Message
* Renaming and package refactoring
* update .gitignore to allow node package name
* Add List<Media> to node and move ai.transformer package to ai.prompt.transformer
* Add Short/Long term memory support.
* Add mixing transformers support
Docs TBD
This also is a fix for AstraDB, which throws an exception if you try to `CREATE KEYSPACE IF NOT EXISTS …`
And use more compatible `USING 'StorageAttachedIndex'` index creation syntax.
- add index configuration and add support for ES response error handling.
- rename dims to dimension propety.
- add property javadocs
- improve the elasticsearch javadoc.
- Resolve an issue where a new index keeps getting created during application start up.
- Solution is is to create an index only if an index on the embedding column does not exist.
- Add missing index name.
- 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 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
- 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.
