* 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.
- Implement ElasticsearchVectoSotore and IT.
- Add ElasticsearchAiSearchFilterExpressionConverter.
- Add dependency to BOM and module to parent pom.
- Fix ElasticsearchVectorStoreIT FilterExpression with
Date type requires the use of epoch milliseconds.
- Add license formatting.
`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
