- Establish a new "spring-ai-retry" project, implementing a default HTTP error handler,
RetryTemplate, and handling both Transient and Non-Transient Exceptions.
- Streamline existing clients (e.g., OpenAI and MistralAI) to utilize "spring-ai-retry."
- Integrate retry auto-configuration with customizable properties, extending it to OpenAI and MistralAI Auto-Configs.
- Allow configuration of RetryTemplate and ResponseErrorHandler for various clients, including OpenAIChatClient,
OpenAiEmbeddingClient, OpenAiAudioTranscriptionCline, OpenAiImageClient, MistralAiChatClient, and MistralAiEmbeddingClient.
- Add tests for default RestTemplate and ResponseErrorHandler configurations in OpenAI and MistralAI.
- Introduce new retry auto-config properties: "onClientErrors" and "onHttpCodes".
- Implement tests for retry auto-config properties.
- Generate missing license headers.
- Implement QdrantVectorStore.
Uses a custom parser for converting Spring AI metadata(Map<String, Object>) to Qdrant GRPC payload.
- Implement Qdrant Expression Filter support.
Uses a custom parser for converting Spring AI filters to Qdrant-compatible GRPC filters.
- Add ITs using testcontainers.
- Add antora docs adrant.adoc.
- Add Qdrant vector store auto-configuraton and boot starter.
Additional (review) change:
- Fix poms parent to 0.8.1-SNAPSHOT.
- Rename ObjectFactory into QdrantObjectFactor.
- Rename ValueFactory into QdrantValueFactory.
- Move the org.springframework.ai.vectorstore package into org.springframework.ai.vectorstore.qdrant.
- Add missing Autoconfigure definition.
- Add missing license and JavaDocs.
- Minor code style improvmentes.
- Move the qdrant version to the main pom
- Add QdrantVectorStoreAutoConfigurationIT
- Remove guava dependency
- Improve gdrant.adoc conent and structure.
- Remove the grpc-protobuf dependency
Resolves#331
- Extend the Spring AI Message with getMediaData() : List<MediaData>
MediaData is a pair of MimeType and data of type Object.
Message#getContent() return text only.
- VertexAI Gemini Support
- implement VertexAiGeiminChatClient for ChatClient and StreamingChat client and support for MediaData content.
add IT tests for Chat, Streaming and Multimodality
- add Auto-configuration + ITs
- add Gemini Spring Boot starter.
- add clients and boot starters to the Spring AI BOM.
- add Anotra documentation for the Gemini chat client.
- update gemini to latest 26.33.0 BOM.
- add vertex ai gemini dependencies to the BOM.
- add Vertex AI Gemini API Function Calling support
- add Gemini API Function Calling Streaming support
- add vertex ai gemini function calling documentation
- factor out the Function Calling functionality into common abstraction used by OpenAI, Azure and Gemini.
- group the VertexAI documentation under a common parent
- add PortableFunctionCallingOption that implements FunctionCallingOptions and ChatOptions and provide builder for it.
- remove some deprecated code.
- allow authorization with GoogleCredentials form json file.
- add AOT support for VertexAI Gemini.
- move legacy Vertex AI into VertexAI PaLM2.
- better handling for empty chat responses.
- update the Gemini version to latest 26.33.0. This required lifting the protobuf-java to 3.25.2 as well.
- fix a bug for handling System messages with Gemini.
- Implement Azure OpenAI Function Calling
Uses the same the common abstractions used by OpenAI and Gemini: FunctionCallingOptions and AbstractFunctionCallSupport
- Implement MistrealAiApi as a REST client for the Mistral REST API.
- Add MistralAiChatClient implementing ChatClient and StreamingChatClient.
Add MistralAiChatOptions implementing ChatOptions.
- Add MistralAiEmbeddingClient impelementing EmbeddingClient.
Add MistralAiEmbeddingOptions implementing EmbeddingOptions.
- Add Unit and IT tests for api and clients.
- Add mistral auto-configuration and boot strarter
- update auto-config property for Mistral AI embedding client
- update auto-config property for Mistral AI embedding client
Additional, review changes:
- Add missing license headers.
- Fix the intialization of defaul options.
- Code re-formatting to improve the readabitliy.
- Many improvements
- Add missing sine annotations
- Remove stream field from MistralAiChatOptions. This handled internally.
- Add MistralAiApi ChatModel and EmbeddingModel enums.
- Add MistralChatClientIT for testeing chat, stream , parsers...
- Add MistralAiApiIT tests
- Refactor and streamline the MistralAiAutoConfiguration.
- Add MistralAiAutoConfigurationIT
- Add postgremaddmbedding adoc page.
- Auto-configuration:
- add missing boot-starter.
- refactor autoconf class and properties to accomodate the PostgresMlEmbeddingOptions.
- PostgesMlEmbeddingClient
- Add the (default) options field and remove old fields.
- Implement default and request options merging.
- Add tests for options and merging.
- Remove redundant code.
- Code style fixes.
* An abstract API for AI model clients
* Providing portable client request options while still allowing vendor specific options when required. Implemented only for StabilityAI/OpenAI ImageClient
* Support for text->image for openai and stabilityai.
Partial fix for #27 : Text To Image and Fixes#266 and Fixes#261
- Implement a native client (OllamaApi) to leverage chat/streaming and embedding endpoints.
- Add a OllamaChatClient implementing the ChatClinet and StreamingChatClinet interfaces.
- Add a OllamaEmbedding clinent that impl. the EmbeddingClinet interface.
- Add AutoConfiguraitons with properties for the chat and the embedding clients.
- Add unit and ITs for the OllamaApi, OllamaChatClient, OllamaEmbeddingClient, and related auto-configuraitons.
- Remove the old ollama impl. classes and tests.
- minor fixes to the bedrok test methods names.
- Add spring-ai-bedrock project with support for Cohere, Llama2, Ai21 Jurassic 2, Titan and Anthropic LLM models.
- Add native API clients for CohereChat CohereEmbedding , Llama2Chat, JurassicChat, TitanChat and TitanEmbedding models, supporting both single shot and streaming completions (for the models that allows it)
- Add ITs tests for the native API clients.
- Implement Chat (AiClient) and ChatStreaming (AiStreamingClient) and EmbeddingClients (according to the models’ support for those) for Cohere, Llama2, and Anthropica. Titan and Jurassic2 are WIP.
- Add ITs for the ChatClient, ChatStreamingClient and EmbeddingClient implementations.
- Add Spring Boot Auto-configurations with flexible properties for the Llama2, Anthropic and Cohere modes + ITs
- Add Spring Boot Starter configurations for all Bedrock models.
- Add README documentations for all models.
- Add BedrockAi APIs AOT hints
- Add Ai21Jurassic2ChatBedrockApi, TitanEmbeddingBedrockApi, TitanChatBedrockApi
- Add TitanChatBedrockApi
Resolves#66
- Added autoconfiguration for Redis vector store
- Added spring boot starter for Redis vector store
- Supports portable metadata filter expressions
Fixes#11
- Introducing a new OpenAiApi native client for OpenAI API and get rid of the theokanning library.
Amongst others the OpenAiApi allows:
- easy base-url configuration (e.g. TAS-AI)
- Flux response for streaming OpenAI results.
- Exposes the http headers containing important metadata
- Pure Spring ecosystem, making it easier for Graal VM
- Define a new AiStreamClient interface returning Flux<AiResponse>
- Refactor OpenAiClient and to use the new OpenAiApi and implement the AiStreamClient.
- Use spring-retry to improve the OpenAI EmbeddingClient stability on 503 error.
- Remove the OpenAI http header interceptor as the OpenAiApi returns ResponseEntity<T> that provides direct access to the headers.
- Refactor the metadata headers and usage extraction.
- Remove redundant and obsolete classes.
- Fix dependency issue with Pinecone, netty-codec-http2 and Spring Boot 3.2
- Add Vertex AI Autoconfigurations for chat and embedding clients.
- Factor out the embeding client dimensions() computation into an abstract parent AbstractEmbeddingClient.
- Add ITs
- Vertex dos.
- Rename Generation#text to content and info to properties.
- Make Generation extend the AbstractMessage and default to ASSISTANT message type.
- Add vevertex-ai project with native api client for generation and embedding.
- Add unit and IT tests for the vertex-ai native client.
- Add AiClient and EmbeddingClient implementation for the the Vertex AI along with ITs.
- Add Weaviate Vector Store implementation.
- Implement a converter of portable Filter.Expressions into native, Weaviate GraphQL Were expressions.
- Support for Weaviater schema auto-registration of filtarable metadata fields.
- Add auto-configration, spring properties and tests.
- WeaviateVectorStore ITs.
- Add README.md
Resolves#100
* Define GenerationMetadata property in AiResponse.
* Add OpenAI implementations of AiMetadata, RateLimit and Usage interfaces.
* Add REST Assured JsonPath dependency to spring-ai-openai module.
* Add OkHttp dependency to spring-ai-openai module.
* Add OkHttp Interceptor to parse OpenAI rate limit metadata from HTTP headers.
* Add OkHttp MockWebServer dependency to spring-ai-openai module, test scope
* Add Jakarta Servlet API dependency to spring-ai-openai module, test scope
* Add Spring Web MVC dependency to spring-ai-open-ai module., test scope
* Define OpenAI API response headers in an Enum.
* Add OpenAI test configuration using mock objects.
* Add integration test to assert successful extraction of OpenAI API response metadata.
* Include Spring Boot auto-configuration for (conditional) OpenAI metadata collection.
* Edit documentation and include information on AI metadata collected by Spring AI.
* Provide AI metadata implementation for Microsoft Azure OpenAI Service.
* Capture optional PromptMetadata in AiResponse.
* Define metadata for an AI generation choice.
* Capture AI choice metadata in Generation.
* Integrate ChoiceMetadata into AiResponse returned by OpenAI.
Fixes#98
- Use Azure AI Search end point to implement the VectorStore interface.
- Add ITs and README.
- Create/update vector index on after properties set.
- Add boot auto-configuration and ITs.
- Add boot starter for the vector store.
Resolves: #82
- Implement ChromaApi client, based on Chroma REST API.
- Implement ChromaVectorStore, including support for filter expression conversion.
- Common VectorStoreUtil class to share to/from Float/Double list/array convertion as well as Json/Map convertions.
- Add ITs including for Basic Auth and Token autheticatios.
- Add ChromaApi security support for BasicAuth and Token.
- Fix an issue with Text filter expression parser, related to double-quoted identifiers.
- Add Chroma README.md.
- Add Chroma boot autoconfiguration
Resolves# #86
- Collapses all VectorStore similiaritySearch methdos into one with SearchRequest builder.
- Fix all affected code and tests.
- Bump the project version to 0.7.1.
- Add tests
- Add autoconfigurations for milvus, pinecone and pgvecor stores.
- Improve and unify the VectorStore ITs.
- Make use of TrasformersEmbeddingClient for auto-configurations ITs.
- Based on the official pinecone java library.
Later expects that indices are created externally via Ops.
- Map Document metadata to and from Pinecone's internal Struct.
Later converts the metadata into pinecone json format.
- Add integration tests and README.
- EmbeddingClient implementation that computes, locally, sentence embeddings with SBERT transformers.
- Uses pre-trained transformer models, serialized into Open Neural Network Exchange (ONNX) format.
- Deep Java Library and the Microsoft ONNX Java Runtime are used to run
the ONNX models and compute the embeddings efficiently.
- Add default tokenizer.json and model.onnx for sentence-transformers/all-MiniLM-L6-v2.
- Add, configurable resource caching service to allow caching
remote (http/https) resources to the local FS.
- README.md provides information on how to serialize ONNX models.
- add Git LFS configuration for large onnx model files.
- Provides a rudimentary text extractions for multitude of document formats,
including PDF, Word Doc/Docx PowerPoint ppt/pptx and many more.
- Generates a single Document for the extracted text.
- No pre or post processing and cleansing for the text.
- Move the ExtractedTextFormatter from pdf reader to the core reader to enable reusability. Improve the tika reader
