This commit introduces a new Markdown document reader with several
key features and improvements:
* Add support for text with various formatting elements
* Implement handling for horizontal rules and hard line breaks
* Add functionality for inline and block code sections
* Incorporate blockquote handling
* Support ordered and unordered lists
* Introduce additional metadata capabilities
* Include JavaDocs
Update ETL documentation to reflect these new features and usage.
Fixes#105
- Breaking changes: Classes from the org.springframework.ai.openai.metadata.audio.transcription package have been moved to the org.springframework.ai.audio.transcription package.
- The AzureOpenAiAudioTranscriptionModel has been added to the auto-configuration.
- The spring.ai.azure.openai.audio.transcription prefix was introduced for properties.
- Introduces options properties which cover all of them (see: AzureOpenAiAudioTranscriptionOptions).
- fix missing MutableResponseMetadata
- add docs
- adjust code to updated ResponseMetadata design
- add test to AzureOpenAiAutoConfiguration
- add missing AzureOpenAiAudioTranscriptionModel tests
- 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
- add new spring-ai-vertex-ai-embedding project.
- add VertexAiTextEmbeddingModel and VertexAiMultimodalEmbeddingMode with related options configuration classes.
- add ITs
- add auto-configuraiton and boot starters.
- register to BOM.
- add documentation.
- add multimodal embedding documentation
- extend the Embedding metdata so that it can keep references to the source document's data, Id, mediatype
Resolves#1013
Related to #1009
- upadate bedrockruntime version to 2.26.7 and align it with aws sdk dependecy.
- minor ITs configuration, consistency and statbility fixes
Use the EnvironmentVariableCredentialsProvider instead of the Profile credential provider.
- update the Bedrock getting started documentation.
- update the Genemi Prerequisites docs.
Currently, in order to use an OpenSearch instance provided by AWS,
additional steps are needed. This commit introduces the required
configuration.
Add new starter and update docs
- 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>
- 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
- 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.
* Added Spring Boot Starter for Spring AI Hugging Face
* Updated documentation with instructions using the starter dependency
* Fixed naming inconsistencies in the docs for Hugging Face
Fixes gh-838
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
- update the Gemini function calling ITs to include a system message with dedicated calling instructions.
- fix a type with few Gemini ChatModel enum names.
- re-enable all Gemini ITs.
* Tests have been updated to use the "gpt-4-turbo" model instead of the "gpt-4-turbo-preview".
* String comparisons of temperature have been adjusted to match the format changes from model reponses
- In ITs rename property spring.ai.azure.openai.chat.options.model to spring.ai.azure.openai.chat.options.deployment-name.
- Resolve compilation issues after the client library update.
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
