Implement service connection support for MongoDB Atlas using both
TestContainers and Docker Compose. This change enables easier
integration testing and local development with MongoDB Atlas.
- Leverage TestContainers 1.20.2 which introduces MongoDBAtlasLocalContainer
- Add TestContainers support using MongoDBAtlasLocalContainer
- Implement Docker Compose configuration for MongoDB Atlas
- Create connection details factories for both TestContainers and Docker Compose
- Update dependency management for MongoDB Atlas integration
- Add integration tests for both TestContainers and Docker Compose setups
- Update documentation to include MongoDB Atlas support
This commit introduces a major overhaul of the advisor system in Spring AI,
improving modularity, type safety, and consistency
Core Changes:
- Replace RequestAdvisor and ResponseAdvisor with CallAroundAdvisor and StreamAroundAdvisor
- Introduce AdvisedRequest and AdvisedResponse classes for better encapsulation
- Deprecate RequestResponseAdvisor in favor of new advisor types
- Remove AdvisorObservableHelper class
Advisor Implementation Updates:
- Update AbstractChatMemoryAdvisor, MessageChatMemoryAdvisor, PromptChatMemoryAdvisor,
QuestionAnswerAdvisor, SafeGuardAroundAdvisor, SimpleLoggerAdvisor, and
VectorStoreChatMemoryAdvisor to implement new advisor interfaces
- Remove CacheAroundAdvisor (functionality likely moved elsewhere)
- Make CallAroundAdvisor and StreamAroundAdvisor extend Ordered interface
Client and Chain Management:
- Modify DefaultChatClient to use new advisor chain approach
- Refactor DefaultAroundAdvisorChain for better ordering and observation
- Implement builder pattern for advisor chain construction in DefaultChatClient
- Separate call and stream advisors in DefaultAroundAdvisorChain
Observation and Context Handling:
- Update observation conventions and context handling in advisors
- Add order field to AdvisorObservationContext
- Modify DefaultAdvisorObservationConvention to include order in high cardinality key values
Testing and Integration:
- Refactor ChatClientAdvisorTests and add new AdvisorsTests
- Update integration tests to reflect new advisor structure
- Enhance AdvisorsTests to verify correct advisor execution order
New Features:
- Generalize the Protect From Blocking functionality across all advisors
- Add (experimental) Re2 advisor to enhance reasoning capabilities of LLMs
- Add disabled Re2 test in OpenAiChatClientIT
Documentation:
- Add Advisors documentation
- Enhance advisors documentation with order explanation and Re2 example
Advisor Ordering:
- Introduce Advisor constants for precedence ordering
- Update AbstractChatMemoryAdvisor to use new precedence constant
- Improve advisor ordering and management in DefaultAroundAdvisorChain.Builder
- Remove redundant reordering logic from DefaultAroundAdvisorChain
These changes aim to provide a more flexible and powerful advisor system,
allowing for easier implementation of complex AI-driven interactions
Co-authored-by: Dariusz Jędrzejczyk <dariusz.jedrzejczyk@broadcom.com>
- Add new option 'maxCompletionTokens' to spring.ai.openai.chat.options
- Mark 'maxTokens' as deprecated
- Update documentation to reflect these changes in OpenAI chat configuration
Related to #1411 and #1412
This commit adds comprehensive documentation for the BatchingStrategy
in vector stores and enhances the TokenCountBatchingStrategy class.
Key changes:
- Explain batching necessity due to embedding model thresholds
- Describe BatchingStrategy interface and its purpose
- Detail TokenCountBatchingStrategy default implementation
- Provide guidance on using and customizing batching strategies
- Note pre-configured vector stores with default strategy
- Add new constructor for custom TokenCountEstimator in
TokenCountBatchingStrategy
- Implement null checks with Spring's Assert utility
- Update docs with new customization options and code examples
This commit introduces support for Oracle Cloud Infrastructure (OCI)
GenAI embedding models in Spring AI. It includes:
* New OCIEmbeddingModel class for interacting with OCI GenAI API
* Auto-configuration for easy setup and integration
* Properties for configuring OCI connection and embedding options
* Documentation updates explaining usage and configuration
* Integration tests to verify functionality
Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
Correct the OllamaEmbeddingModel initialization and options syntax in
the ollama-embeddings.adoc file. Remove unnecessary .toMap() call and
fix typo in withTruncate method name.
Update mongodb.adoc to include links to both beginner and intermediate
content for Spring AI and MongoDB integration. Add a new section
"Tutorials and Code Examples" with:
- Link to the Getting Started guide for basic integration
- Link to a detailed RAG tutorial for more advanced usage
This change provides users with a clear path from initial setup to
more complex implementations using the MongoDB Atlas Vector Store.
This commit introduces a new proxyToolCalls option for various chat
models in the Spring AI project. When enabled, it allows the client to
handle function calls externally instead of being processed internally
by Spring AI.
The change affects multiple chat model implementations, including:
AnthropicChatModel
AzureOpenAiChatModel
MiniMaxChatModel
MistralAiChatModel
MoonshotChatModel
OllamaChatModel
OpenAiChatModel
VertexAiGeminiChatModel
ZhiPuAiChatModel
The proxyToolCalls option is added to the respective chat options
classes and integrated into the AbstractToolCallSupport class for
consistent handling across different implementations.
The proxyToolCalls option can be set either programmatically via
the <ModelName>ChatOptions.builder().withProxyToolCalls() method
or the spring.ai.<model-name>.chat.options.proxy-tool-calls
application property.
Documentation for the new option is also updated in the relevant
Antora pages.
Added ITs for proxy tool calls
Remove ChatClientPromptRequestSpec and all ChatClient.prompt() overloads can how take advantage of the full fluent API. Docs updated
Resolves#1367
This commit introduces a new overloaded method prompt(String content)
to the ChatClient API. It provides a convenient way to create prompts
using only a string of user content, simplifying the process for basic
chat interactions.
Fixes gh-1286
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
This commit introduces support for the Watsonx.ai embedding model.
It includes:
- Watsonx embedding options class with tests
- Watsonx embedding model implementation
- Auto-configuration and properties for the embedding model
- Tests for the Watsonx embedding model
- Documentation for using the Watsonx embedding model
Also removed use of deprecated APIs in WatsonAIChatModel
Introduce a new property for the MongoDB vector store:
spring.ai.vectorstore.mongodb.metadata-fields-to-filter
This property accepts comma-separated values specifying which metadata
fields can be used for filtering when querying the vector store. It
ensures that metadata indexes are created if they don't already exist.
This addition enhances query performance and flexibility by allowing
users to define filterable fields in advance.
Co-authored-by: Eddú Meléndez <eddu.melendez@gmail.com>
* Improve docs for OpenAI, Mistral AI, and Ollama chat models and function calling
* Improve docs for the Chat Client
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
This change updates the type of portable chat options from Float to
Double. Affected options include:
- frequencyPenalty
- presencePenalty
- temperature
- topP
The motivation for this change is to simplify coding. In Java, Float
values require an "f" suffix (e.g., 0.5f), while Double values don't
need any suffix. This makes Double easier to type and reduces
potential errors from forgetting the "f" suffix.
APIs, tests, and documentation have been updated to reflect this
change.
Fixes gh-712
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
* Improve syntax and grammar
* Fix examples using latest APIs
* Add missing info about newer features
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
Resolve issue with AzureOpenAiChatModel processing non-public images as
byte arrays. Implement handling for both URL strings and byte arrays,
converting latter to base64 encoded data URLs. Add test case for
resource-based media data. Update documentation with sample for
classpath resources.
This commit introduces the OpenAiModerationModel and related classes:
- Add OpenAiModerationModel for content moderation
- Create OpenAiModerationOptions for configuration
- Implement OpenAiModerationProperties for Spring Boot setup
- Add integration tests in OpenAiModerationModelIT
- Add documentation
Co-authored-by: hemeda3 <hemeda3@users.noreply.github.com>
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
* Surface more configuration APIs to ChatOptions
* Use abstraction in Observations directly instead of dedicated implementation
* Simplify metadata config in observations for defined models
* Improve merging of runtime and default options in OpenAI
* Fix missing option in Mistral AI
Relates to gh-1148
Signed-off-by: Thomas Vitale <ThomasVitale@users.noreply.github.com>
- Add `TEXT` to the supported ResponseFormat types
- Add JsonSchema record for structured output configuration
- Update OpenAI chat documentation with details on Structured Outputs
- Clarify usage of JSON_SCHEMA response format in properties and code examples
- Update Structured Output Converter docs to mention OpenAI Structured Outputs
- Added support for OpenAI's structured outputs feature, which allows specifying a JSON schema for the model to match
- Introduced new record to configure the desired response format
- Added support for configuring the response format via application properties or the chat options builder
- Extend teh BeanOutputConverter to help generate JSON schema from a target domain object and convert the response.
- Added comprehensive tests to cover the new response format functionality
Resolves#1196
