- 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
- 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
The text-embedding-3-small has the same dimensions as previous text-embedding-ada-002.
The text-embedding-3-large has higher dimensionality not supported by some Vector Stores.
To make the Neo4j module more future-proof, this commit
replaces the old vector index creation syntax with the new style.
Also, the new pattern is in line with the standard Neo4j index creation
and supports the _IF NOT EXISTS_ clause to run idempotent.
This allows us to remove the preceding call to check if the index exists.
As a consequent, the module will require Neo4j to be at least on version 5.15.
At the moment, it is not possible to configure SpringAI
to use an existing index in the database.
This commit enables the user to provide the index name
for auto configuration or builder usage.
Moved up one package level the following classes
* org.springframework.ai.huggingface.client.HuggingfaceChatClient
* org.springframework.ai.openai.client.OpenAiChatClient and org.springframework.ai.openai.embedding.OpenAiEmbeddingClient
* org.springframework.ai.vertex.generation.VertexAiChatClient and org.springframework.ai.vertex.embedding.VertexAiEmbeddingClient
Fixes#211
- 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
