- 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
Though the property names are different, for example 'spring.ai.openai.api-key' and 'spring.ai.openai.embedding-api-key,
by introducing an additional property namespace, 'spring.ai.openai.embedding.*', this enables the configuration of embeddings
to be properly encapsulated and related in Spring Boot fashion.
Closes#102
* Apply consistent treatment to the organization of the source code.
* Fix Logger statements missing message placeholder formatting.
* Simplify logic using Java 17 syntax where applicable.
Closes#101
- 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.
* Clean up README.md files in Milvus, PGvector, and Pinecone modules.
* Apply consistent treatment of 'model' when used as an AI concept, e.g. AI model or Embedding model.
* Apply consistent treatment of 'vector store' and 'vector database' references.
* Simplify sentence structures.
Closes#79
- Extend the VectorStore with similaritySearch using metadata filters using internal DSL and external DSL using Antlr
- Metdata support for Pinecone, Milvus, and pgvector vector stores
- PGVectorStore uses explict ::jsonpath casting for the pgvector filter expression to avoid injections
- Add unit tests for the filter converters, parser and DSL.
- Add ITs for the 3 vector stores
Resolves: #75
* Fixes misspelling in concepts.adco, 'Prompts' section.
* Uses plural form of AI Models in aiclient.adoc.
* Fixes several grammatical mistakes in vectordbs.adoc.
- 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.
