All Classes and Interfaces
Class
Description
Abstract class for the Bedrock API.
Encapsulates the metrics about the model invocation.
Abstract
StructuredOutputConverter implementation that uses a pre-configured
DefaultConversionService to convert the LLM output into the desired type
format.Abstract implementation of the
EmbeddingModel interface that provides
dimensions calculation caching.AbstractFilterExpressionConverter is an abstract class that implements the
FilterExpressionConverter interface.
The AbstractMessage class is an abstract implementation of the Message interface.
Abstract
StructuredOutputConverter implementation that uses a pre-configured
MessageConverter to convert the LLM output into the desired type format.Abstract base class for
VectorStore implementations that provides observation
capabilities.Abstract base builder implementing common builder functionality for
VectorStore.Parent advisor interface for all advisors.
Defines the context for executing a chain of advisors as part of processing a chat
request.
Context used to store metadata for chat client advisors.
Builder for
AdvisorObservationContext.Interface for an
ObservationConvention for chat client advisors.AI Advisor observation documentation.
High cardinality key names.
Low cardinality key names.
Utilities to work with advisors.
Collection of attribute keys used in AI observations (spans, metrics, events).
Collection of metric attributes used in AI observations.
Enumeration of metric names used in AI observations.
Metadata associated with an AI operation (e.g.
Builder for
AiOperationMetadata.Types of operations performed by AI systems.
Collection of systems providing AI functionality.
Utility methods for creating native runtime hints.
Types of tokens produced and consumed in an AI operation.
AllOfGenerateResponseDetails
AllOfStreamResponseDetails
The Anthropic API client.
Input messages.
Chat completion request object.
Metadata about the request.
Configuration for the model's thinking mode.
Chat completion response object.
Check the Models
overview and model
comparison for additional details and options.
The content block of the message.
The source of the media content.
The ContentBlock type.
Content block delta event.
JSON content block delta.
Signature content block delta.
Text content block delta.
Thinking content block delta.
Content block start event.
Text content block.
Thinking content block.
Tool use content block.
Content block stop event.
Error event.
Error body.
The event type of the streamed chunk.
Message delta event.
Message delta usage.
Message start event.
Message stop event.
Ping event.
The role of the author of this message.
The thinking type.
Tool description.
Special event used to aggregate multiple tool use events into a single event with
list of aggregated ContentBlockToolUse.
Usage statistics.
Auto-configuration for Anthropic Chat Model.The
ChatModel implementation for the Anthropic service.The options to be used when sending a chat request to the Anthropic API.
Anthropic Chat autoconfiguration properties.
Anthropic API connection properties.
RateLimit implementation for OpenAI.The AnthropicRuntimeHints class is responsible for registering runtime hints for
Anthropic API classes.
Some model providers API leverage short-lived api keys which must be renewed at regular
intervals using another credential.
Lets the generative know the content was generated as a response to the user.
Implementation of
ToolCallback that adapts MCP tools to Spring AI's tool
interface with asynchronous execution support.Implementation of
ToolCallbackProvider that discovers and provides MCP tools
asynchronously from one or more MCP servers.Represents a response returned by the AI.
Metadata associated with an audio transcription result.
Options for audio transcription.
Represents an audio transcription prompt for an AI model.
A response containing an audio transcription result.
Metadata associated with an audio transcription response.
Converts
Filter.Expression into Azure Search OData filter syntax.Auto-configuration for Azure OpenAI.AzureOpenAI audio transcription client implementation for backed by
OpenAIClient.Options for audio transcription using Azure Open AI.
Structured response of the transcribed audio.
Segment of the transcribed text and its corresponding details.
Extracted word and it's corresponding timestamps.
Configuration properties for Azure OpenAI audio transcription.
Audio transcription metadata implementation for AzureOpenAI.
Auto-configuration for Azure OpenAI.ChatModel implementation for Microsoft Azure AI backed by
OpenAIClient.Builder to construct
AzureOpenAiChatModel.The configuration information for a chat completions request.
Azure OpenAI Client Builder configuration.
Callback interface that can be implemented by beans wishing to customize the
OpenAIClientBuilder whilst retaining the default auto-configuration.Auto-configuration for Azure OpenAI.Azure Open AI Embedding Model implementation.
The configuration information for the embedding requests.
Auto-configuration for Azure OpenAI.Represents the metadata for image generation using Azure OpenAI.
ImageModel implementation for Microsoft Azure AI backed by
OpenAIClient.The configuration information for a image generation request.
Configuration properties for Azure OpenAI image generation options.
Represents metadata associated with an image response from the Azure OpenAI image
model.
Utility enumeration for representing the response format that may be requested from the
Azure OpenAI model.
JSON schema object that describes the format of the JSON object.
RuntimeHintsRegistrar for Azure OpenAI.Uses Azure Cognitive Search as a backing vector store.
Builder class for creating
AzureVectorStore instances.Auto-configuration for Azure Vector Store.Configuration properties for Azure Vector Store.
Base advisor that implements common aspects of the
CallAdvisor and
StreamAdvisor, reducing the boilerplate code needed to implement an advisor.A base interface for advisor chains that can be used to chain multiple advisors
together, both for call and stream advisors.
Base interface for chat memory advisors.
Base test class for VectorStore implementations.
Contract for batching
Document objects so that the call to embed them could be
optimized.An implementation of
StructuredOutputConverter that transforms the LLM output
to a specific object type using JSON schema.Configuration for AWS connection.Configuration properties for Bedrock AWS connection.
The options to be used when sending a chat request to the Bedrock API.
Auto-configuration for Bedrock Cohere Embedding Model.EmbeddingModel implementation that uses the
Bedrock Cohere Embedding API.Options for the Bedrock Cohere embedding API.
Bedrock Cohere Embedding autoconfiguration properties.
Auto-configuration for Bedrock Converse Proxy Chat Client.Configuration properties for Bedrock Converse.
The BedrockMediaFormat class provides mappings between MIME types and their
corresponding Bedrock media formats for documents, images, and videos.
A
ChatModel implementation that uses the Amazon Bedrock Converse API to
interact with the Supported
models.The BedrockRuntimeHints class is responsible for registering runtime hints for Bedrock
AI API classes.
Auto-configuration for Bedrock Titan Embedding Model.EmbeddingModel implementation that uses the
Bedrock Titan Embedding API.Options for the Titan Embedding API.
Bedrock Titan Embedding autoconfiguration properties.
BestOfSequence
Advisor for execution flows ultimately resulting in a call to an AI model
A chain of
CallAdvisor instances orchestrating the execution of a
ChatClientRequest on the next CallAdvisor in the chain.An implementation of
ChatMemoryRepository for Apache Cassandra.Auto-configuration for CassandraChatMemoryRepository.Configuration for the Cassandra Chat Memory store.
Given a string sessionId, return the value for each primary key column.
Configuration properties for Cassandra chat memory.
The CassandraVectorStore is for managing and querying vector data in an Apache
Cassandra db.
Builder for the Cassandra vector store.
Given a string document id, return the value for each primary key column.
Given a list of primary key column values, return the document id.
Indexes are automatically created with COSINE.
Auto-configuration for Cassandra Vector Store.Configuration properties for Cassandra Vector Store.
The Categories class represents a set of categories used to classify content.
This class represents the scores for different categories of content.
Client to perform stateless requests to an AI Model, using a fluent API.
A mutable builder for creating a
ChatClient.Specification for a prompt system.
Common attributes used in
ChatClient context.Auto-configuration for ChatClient.Builder for configuring a
ChatClient.Builder.Configuration properties for the chat client builder.
Callback interface that can be used to customize a
ChatClient.Builder.Helper that for streaming chat responses, aggregate the chat response messages into a
single AssistantMessage.
Context used to store metadata for chat client workflows.
Interface for an
ObservationConvention for chat client workflows.Documented conventions for chat client observations.
Handler for emitting the chat client prompt content to logs.
Represents a request processed by a
ChatClient that ultimately is used to build
a Prompt to be sent to an AI model.Represents a response returned by a
ChatClient.Represents the metadata associated with the generation of a chat response.
The contract for storing and managing the memory of chat conversations.
Auto-configuration for
ChatMemory.A repository for storing and retrieving chat messages.
A
CallAdvisor that uses a ChatModel to generate a response.Handler for emitting the chat completion content to logs.
Marker interface, to be used to store info on the model such as the current context
length.
Handler for generating metrics from chat model observations.
Context used to store metadata for chat model exchanges.
Interface for an
ObservationConvention for chat model exchanges.Documented conventions for chat model observations.
High-cardinality observation key names for chat model operations.
Low-cardinality observation key names for chat model operations.
Handler for emitting the chat prompt content to logs.
A
StreamAdvisor that uses a ChatModel to generate a streaming response.Auto-configuration for Spring AI chat model observations.
Configuration properties for chat model observations.
ModelOptions representing the common options that are portable across different
chat models.Builder for creating
ChatOptions instance.A PromptTemplate that lets you specify the role as a string should the current
implementations and their roles not suffice for your needs.
The chat completion (e.g.
Models common AI provider metadata returned in an AI response.
Single-class Chroma API implementation based on the (unofficial) Chroma REST API.
Add embeddings to the chroma data store.
Chroma embedding collection.
Request to create a new collection with the given name and metadata.
Request to create a new database
Request to create a new tenant
Chroma database.
Request to delete embedding from a collection.
Single query embedding response.
Object containing the get embedding results.
Get embeddings from a collection.
Request to get the nResults nearest neighbor embeddings for provided
queryEmbeddings.
A QueryResponse object containing the query results.
Chroma tenant.
Common value constants for Chroma api.
Configuration properties for Chroma API client.
Connection details for a Chroma service.
Converts
Filter.Expression into Chroma metadata filter expression format.ChromaVectorStore is a concrete implementation of the VectorStore
interface.Auto-configuration for Chroma Vector Store.Configuration properties for Chroma Vector Store.
Cohere Embedding API.
Cohere Embedding model ids.
The Cohere Embed model request.
Cohere Embedding API input types.
Specifies how the API handles inputs longer than the maximum token length.
Cohere Embedding response.
Converts Spring AI
Filter.Expression into Coherence Filter.
Integration of Coherence Coherence 24.09+ as a Vector Store.
Builder class for creating
CoherenceVectorStore instances.Common properties for vector stores.
CompatGenerateRequest
Uses a large language model to compress a conversation history and a follow-up query
into a standalone query that captures the essence of the conversation.
Combines documents retrieved based on multiple queries and from multiple data sources
by concatenating them into a single collection of documents.
Data structure that contains content and metadata.
Converts the Document text and metadata into an AI, prompt-friendly text
representation.
ContentFormatTransformer processes a list of documents by applying a content formatter
to each document.
Augments the user query with contextual data from the content of the provided
documents.
Amazon Bedrock Converse API utils.
Special event used to aggregate multiple tool use events into a single event with
list of aggregated ContentBlockToolUse.
Cosmos DB implementation.
Builder class for creating
CosmosDBVectorStore instances.Auto-configuration for CosmosDB Vector Store.Configuration properties for CosmosDB Vector Store.
Choose whether the Vector store should prioritize recall or latency when returning
similar vectors in search results.
Choose the method to calculate the similarity between the vector embedding in a Vector
Search index and the vector embedding in a Vector Search query.
Utility class for escaping curly brackets in strings
Single class implementation of the DeepSeek Chat Completion API:
https://platform.deepseek.com/api-docs/api/create-chat-completion
Represents a chat completion response returned by model, based on the provided
input.
Chat completion choice.
Represents a streamed chunk of a chat completion response returned by model, based
on the provided input.
Chat completion choice.
The reason the model stopped generating tokens.
Message comprising the conversation.
The function definition.
The role of the author of this message.
The relevant tool call.
Creates a model response for the given chat conversation.
Helper factory that creates a tool_choice of type 'none', 'auto' or selected function by name.
DeepSeek Chat Completion
Models
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Log probability information for the choice.
Message content tokens with log probability information.
The most likely tokens and their log probability, at this token position.
Usage statistics for the completion request.
Breakdown of tokens used in the prompt
Auto-configuration for DeepSeek Chat Model.Chat completions options for the DeepSeek chat API.
Configuration properties for DeepSeek chat client.
Parent properties for DeepSeek.
Parent properties for DeepSeek.
The DeepSeekRuntimeHints class is responsible for registering runtime hints for
DeepSeek API classes.
Helper class to support Streaming function calling.
Default implementation of the
AdvisorObservationConvention.Default implementation for the
BaseAdvisorChain.The default implementation of
ChatClient as created by the
ChatClient.Builder.build() } method.DefaultChatClientBuilder is a builder class for creating a ChatClient.
Default conventions to populate observations for chat client workflows.
Default implementation of
ChatGenerationMetadata.Default conventions to populate observations for chat model operations.
Default implementation for the
ChatOptions.Implementation of
ChatOptions.Builder to create DefaultChatOptions.Default implementation of
ContentFormatter.Default conventions to populate observations for embedding model operations.
Default conventions to populate observations for image model operations.
Default implementation of
ToolCallingChatOptions.Default implementation of
ToolCallingChatOptions.Builder.Default implementation of
ToolCallingManager.Default conventions to populate observations for tool calling operations.
A default implementation of
ToolCallResultConverter.Default implementation of
ToolDefinition.Default implementation of
ToolExecutionEligibilityPredicate that checks whether
tool execution is enabled in the prompt options and if the chat response contains tool
calls.Default implementation of
ToolExecutionExceptionProcessor.Default implementation of
ToolExecutionResult.Default implementation of
ToolMetadata.Default implementation of the
Usage interface.Default conventions to populate observations for vector store operations.
A
ToolCallbackResolver that delegates to a list of ToolCallbackResolver
instances.Details
A document is a container for the content and metadata of a document.
EmbeddingModel is a generic interface for embedding models.
Represents a request to embed a list of documents.
A component for combining documents retrieved based on multiple queries and from
multiple data sources into a single collection of documents.
Common set of metadata keys used in
Documents by DocumentReaders and
VectorStores.A component for post-processing retrieved documents based on a query, addressing
challenges such as "lost-in-the-middle", context length restrictions from the model,
and the need to reduce noise and redundancy in the retrieved information.
Component responsible for retrieving
Documents from an underlying data source,
such as a search engine, a vector store, a database, or a knowledge graph.Write a list of
Document instances.ElasticsearchAiSearchFilterExpressionConverter is a class that converts
Filter.Expression objects into Elasticsearch query string representation.
Elasticsearch-based vector store implementation using the dense_vector field type.
Auto-configuration for Elasticsearch Vector Store.Provided Elasticsearch vector option configuration.
Configuration properties for Elasticsearch Vector Store.
Represents a single embedding vector.
EmbeddingModel is a generic interface for embedding models.
Description of an embedding model.
Handler for generating metrics from embedding model observations.
Context used to store metadata for embedding model exchanges.
Interface for an
ObservationConvention for embedding model exchanges.Documented conventions for embedding model observations.
High-cardinality observation key names for embedding model operations.
Low-cardinality observation key names for embedding model operations.
Auto-configuration for Spring AI embedding model observations.
Options for embedding models.
Builder for
EmbeddingOptions.Request to embed a list of input instructions.
Embedding response object.
Common AI provider metadata returned in an embedding response.
Metadata associated with the embedding result.
Utility methods for embedding related operations.
A RateLimit implementation that returns zero for all property getters
A EmptyUsage implementation that returns zero for all property getters
An
ObservationHandler that logs errors using a Tracer.ErrorResponse
Represents an evaluation request, which includes the user's text, a list of content
data, and a chat response.
A utility to reformat extracted text content before encapsulating it in a
Document.The
Builder class is a nested static class of
ExtractedTextFormatter designed to facilitate the creation and
customization of instances of ExtractedTextFormatter.Implementation of
Evaluator used to evaluate the factual accuracy of Large
Language Model (LLM) responses against provided context.Writes the content of a list of
Documents into a file.Portable runtime generative for metadata filter expressions.
Triple that represents and filter boolean expression as
left type right.Filter expression operations.
Represents expression grouping (e.g.
String identifier representing an expression key.
Mark interface representing the supported expression types:
Filter.Key,
Filter.Value, Filter.Expression and Filter.Group.Represents expression value constant or constant array.
DSL builder for
Filter.Expression instances.Converters a generic, portable
Filter.Expression into a
VectorStore specific expression language
format.Parse a textual, vector-store agnostic, filter expression language into
Filter.Expression.Helper class providing various boolean transformation.
This class provides an empty implementation of
FiltersListener, which can be
extended to create a listener which only needs to handle a subset of the available
methods.This class provides an empty implementation of
FiltersVisitor, which can be
extended to create a visitor which only needs to handle a subset of the available
methods.This interface defines a complete listener for a parse tree produced by
FiltersParser.This interface defines a complete generic visitor for a parse tree produced by
FiltersParser.Gets or Sets FinishReason
This class extends PDFTextStripper to provide custom text extraction and formatting
capabilities for PDF pages.
Implementations of this interface provides instructions for how the output of a
language generative should be formatted.
A
ToolCallback implementation to invoke functions as tools.GemFireAiSearchFilterExpressionConverter is a class that converts Filter.Expression
objects into GemFire VectorDB query string representation.
Connection details for a GemFire service.
A VectorStore implementation backed by GemFire.
Builder class for creating
GemFireVectorStore instances.Auto-configuration for GemFire Vector Store.Configuration properties for GemFire Vector Store.
GenerateParameters
GenerateRequest
GenerateResponse
Represents a response returned by the AI.
The Generation class represents a response from a moderation process.
The SAP HANA Cloud vector engine offers multiple use cases in AI scenarios.
Builder class for creating
HanaCloudVectorStore instances.The
HanaVectorEntity is an abstract class that represents a mapped superclass
for entities that have a vector representation stored in a HANA vector repository.The
HanaVectorRepository interface provides methods for interacting with a HANA
vector repository, which allows storing and querying vector embeddings.HSQLDB-specific SQL dialect for chat memory repository.
An implementation of
ChatModel that interfaces with HuggingFace Inference
Endpoints for text generation.Configuration properties for Hugging Face chat model.
Interface for generating unique document IDs.
Context used to store metadata for image model exchanges.
Interface for an
ObservationConvention for image model exchanges.Documented conventions for image model observations.
High-cardinality observation key names for image model operations.
Low-cardinality observation key names for image model operations.
Handler for emitting image prompt content to logs.
Auto-configuration for Spring AI image model observations.
Configuration properties for image model observations.
ImageOptions represent the common options, portable across different image generation
models.
The image completion (e.g.
Represents metadata associated with an image response.
Info
An in-memory implementation of
ChatMemoryRepository.Utility methods for Jackson.
An implementation of
ChatMemoryRepository for JDBC.Abstraction for database-specific SQL for chat memory repository.
A SHA-256 based ID generator that returns the hash as a UUID.
Utilities to perform parsing operations between JSON and Java.
A class that reads JSON documents and converts them into a list of
Document
objects.Utility class for converting JSON Schema to OpenAPI schema format.
Utilities to generate JSON Schemas from Java types and method signatures.
Options for generating JSON Schemas.
Reads HTML documents and extracts text content using JSoup.
Common configuration for the
JsoupDocumentReader.Estimates the number of tokens in a given text or message using the JTokkit encoding
library.
Keyword extractor that uses generative to extract 'excerpt_keywords' metadata field.
Returns a new list of content (e.g list of messages of list of documents) that is a
subset of the input list of contents and complies with the max token size constraint.
StructuredOutputConverter implementation that uses a
DefaultConversionService to convert the LLM output into a
List instance.Utility class that provides predefined SLF4J
Marker instances used in logging
operations within the application.StructuredOutputConverter implementation that uses a pre-configured
MappingJackson2MessageConverter to convert the LLM output into a
java.util.Map<String, Object> instance.Converts
Filter.Expression into JSON metadata filter expression format.MariaDB-based vector store implementation using MariaDB's vector search capabilities.
Builder for creating instances of
MariaDBVectorStore.The representation of
Document along with its embedding.Reads the given Markdown resource and groups headers, paragraphs, or text divided by
horizontal lines (depending on the
MarkdownDocumentReaderConfig.horizontalRuleCreateDocument configuration) into
Documents.Common configuration for the
MarkdownDocumentReader.Interface for customizing asynchronous MCP client configurations.
Auto-configuration for Model Context Protocol (MCP) client support.
Record class that implements
AutoCloseable to ensure proper cleanup of MCP
clients.Common Configuration properties for the Model Context Protocol (MCP) clients shared for
all transport types.
Client types supported by the MCP client.
Represents a callback configuration for tools.
Runtime hints registrar for Model Context Protocol (MCP) schema classes.
Auto-configuration for the Model Context Protocol (MCP)
Server.Configuration properties for the Model Context Protocol (MCP) server.
Server types supported by the MCP server.
This class defines a condition met when the MCP server is enabled and the STDIO
Transport is disabled.
Configuration properties for Server-Sent Events (SSE) based MCP client connections.
Parameters for configuring an SSE connection to an MCP server.
Configuration properties for the Model Context Protocol (MCP) stdio client.
Record representing the parameters for an MCP server connection.
Configurer class for customizing MCP synchronous clients.
Interface for customizing synchronous MCP client configurations.
Utility class that provides helper methods for working with Model Context Protocol
(MCP) tools in a Spring AI environment.
Auto-configuration for MCP WebFlux Server Transport.Auto-configuration for MCP WebMvc Server Transport.The Media class represents the data and metadata of a media attachment in a message.
Builder class for Media.
Common media formats.
Utility class for merging ChatCompletions instances and their associated objects.
The Message interface represents a message that can be sent or received in a chat
application.
Helper that for streaming chat responses, aggregate the chat response messages into a
single AssistantMessage.
Memory is retrieved added as a collection of messages to the prompt
Converts a list of messages to a prompt for bedrock models.
Enumeration representing types of
Messages in a chat application.A chat memory implementation that maintains a message window of a specified size,
ensuring that the total number of messages does not exceed the specified limit.
A
ToolCallback implementation to invoke methods as tools.Converts
Filter.Expression into Milvus metadata filter expression format.A specialized
SearchRequest for Milvus vector search, extending the base
request with Milvus-specific parameters.Builder class for constructing instances of
MilvusSearchRequest.Connection details for a Milvus service client.
Parameters for Milvus client connection.
Milvus implementation of the
VectorStore
interface.Auto-configuration for Milvus Vector Store.Configuration properties for Milvus Vector Store.
Gemini supports the following MIME types:
image/gif
image/png
image/jpeg
video/mov
video/mpeg
video/mp4
video/mpg
video/avi
video/wmv
video/mpegps
video/flv
https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini
Single class implementation of the MiniMax Chat Completion API and
MiniMax Embedding API.
Represents a chat completion response returned by model, based on the provided input.
Chat completion choice.
Represents a streamed chunk of a chat completion response returned by model, based on the provided input.
Chat completion choice.
The reason the model stopped generating tokens.
Message comprising the conversation.
The function definition.
An array of content parts with a defined type.
The image content of the message.
The role of the author of this message.
The relevant tool call.
Creates a model response for the given chat conversation.
An object specifying the format that the model must output.
Helper factory that creates a tool_choice of type 'none', 'auto' or selected function by name.
MiniMax Chat Completion Models:
MiniMax Model.
List of multiple embedding responses.
MiniMax Embeddings Models:
Embeddings.
Creates an embedding vector representing the input text.
MiniMax Embeddings Types
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Log probability information for the choice.
Message content tokens with log probability information.
The most likely tokens and their log probability, at this token position.
Usage statistics for the completion request.
Common value constants for MiniMax api.
Auto-configuration for MiniMax Chat Model.MiniMaxChatOptions represents the options for performing chat completion using the
MiniMax API.
Configuration properties for MiniMax chat model.
Auto-configuration for MiniMax Embedding Model.MiniMax Embedding Model implementation.
This class represents the options for MiniMax embedding.
Configuration properties for MiniMax embedding model.
The MiniMaxRuntimeHints class is responsible for registering runtime hints for MiniMax
API classes.
Helper class to support Streaming function calling.
Single-class, Java Client library for Mistral AI platform.
Represents a chat completion response returned by model, based on the provided
input.
Chat completion choice.
Represents a streamed chunk of a chat completion response returned by model, based
on the provided input.
Chat completion choice.
The reason the model stopped generating tokens.
Message comprising the conversation.
The function definition.
An array of content parts with a defined type.
Shortcut constructor for an image content.
The role of the author of this message.
The relevant tool call.
Creates a model request for chat conversation.
An object specifying the format that the model must output.
Specifies a tool the model should use.
List of well-known Mistral chat models.
Represents an embedding vector returned by embedding endpoint.
List of multiple embedding responses.
List of well-known Mistral embedding models.
Creates an embedding vector representing the input text.
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Log probability information for the choice.
Message content tokens with log probability information.
The most likely tokens and their log probability, at this token position.
Usage statistics.
Chat
Auto-configuration for Mistral AI.Represents a Mistral AI Chat Model.
Options for the Mistral AI Chat API.
Configuration properties for Mistral AI chat.
Common properties for Mistral AI.
Embedding
Auto-configuration for Mistral AI.Provides the Mistral AI Embedding Model.
Options for the Mistral AI Embedding API.
Configuration properties for MistralAI embedding model.
Mistral AI Moderation API.
List of well-known Mistral moderation models.
Moderation
Auto-configuration for Mistral AI.Parent properties for Mistral AI.
The MistralAiRuntimeHints class is responsible for registering runtime hints for
Mistral AI API classes.
Helper class to support Streaming function calling.
The Model interface provides a generic API for invoking AI models.
Describes an AI model's basic characteristics.
Options for managing models in Ollama.
Context used when sending a request to a machine learning model and waiting for a
response from the model provider.
Interface representing the customizable options for AI model interactions.
Utility class for manipulating
ModelOptions objects.Interface representing a request to an AI model.
Interface representing the response received from an AI model.
This interface provides methods to access the main output of the AI model and the
metadata associated with this result.
Generate metrics about the model usage in the context of an AI operation.
The Moderation class represents the result of a moderation process.
An interface that represents metadata associated with the results of a moderation
generation process.
Represents a single message intended for moderation, encapsulating the text content.
The ModerationModel interface defines a generic AI model for moderation.
Represents the options for moderation.
A builder class for creating instances of ModerationOptions.
Represents a prompt for moderation containing a single message and the options for the
moderation model.
Represents a response from a moderation process, encapsulating the moderation metadata
and the generated content.
Defines the metadata associated with a moderation response, extending a base response
interface.
Represents the result of a moderation process, indicating whether content was flagged,
the categories of moderation, and detailed scores for each category.
Converts
Filter.Expression into MongoDB Atlas metadata filter expression
format.MongoDB Atlas-based vector store implementation using the Atlas Vector Search.
The representation of
Document along with its embedding.Auto-configuration for MongoDB Atlas Vector Store.Configuration properties for MongoDB Atlas Vector Store.
Uses a large language model to expand a query into multiple semantically diverse
variations to capture different perspectives, useful for retrieving additional
contextual information and increasing the chances of finding relevant results.
MySQL dialect for chat memory repository.
A named MCP client transport.
An implementation of
ChatMemoryRepository for Neo4JAuto-configuration for Neo4jChatMemoryRepository.Configuration for the Neo4j Chat Memory store.
Configuration properties for Neo4j chat memory.
Converts
Filter.Expression into Neo4j condition expression format.Neo4j-based vector store implementation using Neo4j's vector search capabilities.
An enum to configure the distance function used in the Neo4j vector index.
Auto-configuration for Neo4j Vector Store.Configuration properties for Neo4j Vector Store.
Root of the hierarchy of Model access exceptions that are considered non-transient -
where a retry of the same operation would fail unless the cause of the Exception is
corrected.
This implementation of ApiKey indicates that no API key should be used, e.g.
No-op implementation of
TemplateRenderer that returns the template unchanged.Utilities for observability.
ChatModel implementation that uses the OCI GenAI Chat API.Configuration properties for OCI Cohere chat model.
The configuration information for OCI chat requests.
Configuration properties for OCI connection.
EmbeddingModel implementation that uses the
OCI GenAI Embedding API.Configuration properties for OCI embedding model.
The configuration information for OCI embedding requests
Chat
Auto-configuration for Oracle Cloud Infrastructure
Generative AI.Embedding
Auto-configuration for Oracle Cloud Infrastructure
Generative AI.Auto-configuration for Oracle Cloud Infrastructure Generative
AI Inference Client.Java Client for the Ollama API.
Chat request object.
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Ollama chat response object.
Generate embeddings from a model.
The response object returned from the /embedding endpoint.
Chat message object.
The role of the message in the conversation.
The relevant tool call.
The function definition.
Auto-configuration for Ollama API.Common value constants for Ollama api.
Auto-configuration for Ollama Chat model.ChatModel implementation for Ollama.Ollama Chat autoconfiguration properties.
Connection details for an Ollama service.
Ollama connection autoconfiguration properties.
Auto-configuration for Ollama Chat Client.EmbeddingModel implementation for Ollama.Ollama Embedding autoconfiguration properties.
Ollama initialization configuration properties.
Helper class for common Ollama models.
Manage the lifecycle of models in Ollama.
Helper class for creating strongly-typed Ollama options.
The OllamaRuntimeHints class is responsible for registering runtime hints for Ollama AI
API classes.
Single class implementation of the
OpenAI Chat Completion
API and OpenAI
Embedding API.
Represents a chat completion response returned by model, based on the provided
input.
Chat completion choice.
Represents a streamed chunk of a chat completion response returned by model, based
on the provided input.
Chat completion choice.
The reason the model stopped generating tokens.
Message comprising the conversation.
Represents an annotation within a message, specifically for URL citations.
A URL citation when using web search.
Audio response from the model.
The function definition.
An array of content parts with a defined type.
Shortcut constructor for an image content.
Constructor for base64-encoded file
The role of the author of this message.
The relevant tool call.
Creates a model request for the given chat conversation.
Parameters for audio output.
Specifies the output audio format.
Specifies the voice type.
Helper factory that creates a tool_choice of type 'none', 'auto' or selected function by name.
This tool searches the web for relevant results to use in a response.
High level guidance for the amount of context window space to use for the
search.
Approximate location parameters for the search.
OpenAI Chat Completion Models.
Represents an embedding vector returned by embedding endpoint.
List of multiple embedding responses.
OpenAI Embeddings Models:
Embeddings.
Creates an embedding vector representing the input text.
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Log probability information for the choice.
Message content tokens with log probability information.
The most likely tokens and their log probability, at this token position.
The type of modality for the model completion.
Usage statistics for the completion request.
Breakdown of tokens used in a completion.
Breakdown of tokens used in the prompt
Thrown on 4xx client errors, such as 401 - Incorrect API key provided, 401 - You must
be a member of an organization to use the API, 429 - Rate limit reached for requests,
429 - You exceeded your current quota , please check your plan and billing details.
Common value constants for OpenAI api.
Enumeration of OpenAI API response headers.Turn audio into text or text into audio.
Builder to construct
OpenAiAudioApi instance.Request to generates audio from the input text.
The format to audio in.
Builder for the SpeechRequest.
The voice to use for synthesis.
The Transcription
Object represents a verbose json transcription response returned by model,
based on the provided input.
Segment of the transcribed text and its corresponding details.
Extracted word and it corresponding timestamps.
Request to transcribe an audio file to text.
The format of the transcript and translation outputs, in one of these options:
json, text, srt, verbose_json, or vtt.
Request to translate an audio file to English.
TTS is an AI model that converts text to natural sounding spoken text.
Whisper is a
general-purpose speech recognition model.
Auto-configuration for OpenAI.OpenAI audio speech client implementation for backed by
OpenAiAudioApi.Options for OpenAI text to audio - speech synthesis.
Configuration properties for OpenAI audio speech.
Audio speech metadata implementation for OpenAI.
Auto-configuration for OpenAI.OpenAI audio transcription client implementation for backed by
OpenAiAudioApi.OpenAI Audio Transcription Options.
Audio transcription metadata implementation for OpenAI.
Chat
Auto-configuration for OpenAI.Options for the OpenAI Chat API.
Embedding
Auto-configuration for OpenAI.Open AI Embedding Model implementation.
OpenAI Embedding Options.
OpenAI Image API.
Builder to construct
OpenAiImageApi instance.OpenAI Image API model.
Image
Auto-configuration for OpenAI.OpenAiImageModel is a class that implements the ImageModel interface.
OpenAI Image API options.
OpenAI Image autoconfiguration properties.
OpenAI Moderation API.
Builder to construct
OpenAiModerationApi instance.Moderation
Auto-configuration for OpenAI.OpenAiModerationModel is a class that implements the ModerationModel interface.
OpenAI Moderation API options.
OpenAI Moderation autoconfiguration properties.
RateLimit implementation for OpenAI.Utility used to extract known HTTP response headers for the OpenAI API.
The OpenAiRuntimeHints class is responsible for registering runtime hints for OpenAI
API classes.
Helper class to support Streaming function calling.
A FilterExpressionConverter implementation for OpenSearch AI search filter expressions.
Condition that matches if either:
The property
spring.ai.vectorstore.opensearch.aws.enabled is
explicitly set to false.
Required AWS SDK classes are missing from the classpath.
OpenSearch-based vector store implementation using OpenSearch's vector search
capabilities.
Builder class for creating OpenSearchVectorStore instances.
The representation of
Document along with its embedding.
Integration of Oracle database 23ai as a Vector Store.
Builder class for creating
OracleVectorStore instances.Auto-configuration for Oracle Vector Store.Configuration properties for Oracle Vector Store.
Groups the parsed PDF pages into
Documents.The ParagraphManager class is responsible for managing the paragraphs and hierarchy of
a PDF document.
Represents a document paragraph metadata and hierarchy.
Uses the PDF catalog (e.g.
Utility methods for
String parsing.Common configuration builder for the
PagePdfDocumentReader and the
ParagraphPdfDocumentReader.Re-implement the PDFLayoutTextStripperByArea on top of the PDFLayoutTextStripper
instead the original PDFTextStripper.
The PdfReaderRuntimeHints class is responsible for registering runtime hints for PDFBox
resources.
Converts
Filter.Expression into PgVector metadata filter expression format.Validates the schema of a PostgreSQL table used as a PGVectorStore.
PostgreSQL-based vector store implementation using the pgvector extension.
Defaults to CosineDistance.
The ID type for the Pg vector store schema.
By default, pgvector performs exact nearest neighbor search, which provides perfect
recall.
Auto-configuration for PostgreSQL Vector Store.Configuration properties for PostgreSQL Vector Store.
Converts
Filter.Expression into Pinecone metadata filter expression format.A VectorStore implementation backed by Pinecone, a cloud-based vector database.
Builder class for creating
PineconeVectorStore instances.First step interface requiring API key configuration.
Final step interface requiring index name configuration.
Internal implementation of the step builder pattern using records for
immutability.
Auto-configuration for Pinecone Vector Store.Configuration properties for Pinecone Vector Store.
Dialect for Postgres.
Auto-configuration class for PostgresMlEmbeddingModel.
PostgresML EmbeddingModel
PostgresML Embedding Options.
Configuration properties for Postgres ML.
PrefillToken
Converts
Filter.Expression into test string format.The Prompt class represents a prompt used in AI model requests.
Assertion utility class that assists in validating arguments for prompt-related
operations.
Memory is retrieved added into the prompt's system text.
Builder for PromptChatMemoryAdvisor.
Abstract Data Type (ADT) modeling metadata gathered by the AI during request
processing.
Abstract Data Type (ADT) modeling filter metadata for all prompts sent during an AI
request.
A template for creating prompts.
Strategy for pulling Ollama models.
Connection details for a Qdrant service client.
Qdrant vectorStore implementation.
Builder for creating instances of
QdrantVectorStore.Auto-configuration for Qdrant Vector Store.Configuration properties for Qdrant Vector Store.
Represents a query in the context of a Retrieval Augmented Generation (RAG) flow.
A component for augmenting an input query with additional data, useful to provide a
large language model with the necessary context to answer the user query.
A component for expanding the input query into a list of queries, addressing challenges
such as poorly formed queries by providing alternative query formulations, or by
breaking down complex problems into simpler sub-queries.
A component for transforming the input query to make it more effective for retrieval
tasks, addressing challenges such as poorly formed queries, ambiguous terms, complex
vocabulary, or unsupported languages.
Context for the question is retrieved from a Vector Store and added to the prompt's
user text.
A random ID generator that returns a UUID.
Abstract Data Type (ADT) encapsulating metadata from an AI provider's API rate limits
granted to the API key in use and the API key's current balance.
Converts
Filter.Expression into Redis search filter expression format.Redis-based vector store implementation using Redis Stack with RediSearch and
RedisJSON.
Auto-configuration for Redis Vector Store.Configuration properties for Redis Vector Store.
Evaluates the relevancy of a response to a query based on the context provided.
Service that helps caching remote
Resources on the local file system.Miscellaneous Resource utility methods.
Represents a
Model response that includes the
entire response along withe specified response entity type.An object specifying the format that the model must output.
An object specifying the format that the model must output.
JSON schema object that describes the format of the JSON object.
Interface representing metadata associated with an AI model's response.
Interface representing metadata associated with the results of an AI model.
Advisor that implements common Retrieval Augmented Generation (RAG) flows using the
building blocks defined in the
org.springframework.ai.rag package and following
the Modular RAG Architecture.RetryUtils is a utility class for configuring and handling retry operations.
Uses a large language model to rewrite a user query to provide better results when
querying a target system, such as a vector store or a web search engine.
An advisor that blocks the call to the model provider if the user input contains any of
the sensitive words.
The type of schema to generate for a given Java type.
Utility class for working with Cassandra schema.
Utility class for working with Cassandra schema.
Similarity search request.
SearchRequest Builder.
OCI serving mode.
Helper class to load the OCI Gen AI
ServingModehttps://www.elastic.co/guide/en/elasticsearch/reference/master/dense-vector.html
max_inner_product is currently not supported because the distance value is not
normalized and would not comply with the requirement of being between 0 and 1
A simple implementation of
ApiKey that holds an immutable API key value.A simple logger advisor that logs the request and response messages.
SimpleVectorStore is a simple implementation of the VectorStore interface.
An immutable
Content implementation representing content, metadata, and its
embeddings.Converts
Filter.Expression into SpEL metadata filter expression format.The Speech class represents the result of speech synthesis from an AI model.
The
SpeechMessage class represents a single text message to be converted to
speech by the OpenAI TTS API.The
SpeechModel interface provides a way to interact with the OpenAI
Text-to-Speech (TTS) API.The
SpeechPrompt class represents a request to the OpenAI Text-to-Speech (TTS)
API.Creates a new instance of SpeechResponse with the given speech result.
Types of Spring AI constructs which can be observed.
Auto-configuration for AI Retry.Properties for AI Retry.
Exponential Backoff properties.
JSON Schema Generator Module for Spring AI.
Options for customizing the behavior of the module.
A Spring
ApplicationContext-based implementation that provides a way to
retrieve a bean from the Spring context and wrap it into a ToolCallback.Converts a
Filter into a JSON Path expression.Dialect for SQL Server.
Auto-configuration for Server-Sent Events (SSE) HTTP client transport in the Model
Context Protocol (MCP).
Auto-configuration for WebFlux-based Server-Sent Events (SSE) client transport in the
Model Context Protocol (MCP).
Represents the StabilityAI API.
Auto-configuration for StabilityAI Image Model.Represents metadata associated with the image generation process in the StabilityAI
framework.
StabilityAiImageModel is a class that implements the ImageModel interface.
StabilityAiImageOptions is an interface that extends ImageOptions.
Configuration properties for Stability AI image model.
A simple implementation of
ToolCallbackProvider that maintains a static array
of ToolCallback objects.A
ToolCallbackResolver that resolves tool callbacks from a static registry.Auto-configuration for Standard Input/Output (stdio) transport in the Model Context
Protocol (MCP).
Advisor for execution flows ultimately resulting in a streaming call to an AI model.
A chain of
StreamAdvisor instances orchestrating the execution of a
ChatClientRequest on the next StreamAdvisor in the chain.StreamDetails
Helper class to support streaming function calling and thinking events.
Builder for
AnthropicApi.ChatCompletionResponse.The StreamingModel interface provides a generic API for invoking an AI models with
streaming response.
The
StreamingSpeechModel interface provides a way to interact with the OpenAI
Text-to-Speech (TTS) API using a streaming approach, allowing you to receive the
generated audio in a real-time fashion.StreamResponse
Converts the (raw) LLM output into a structured responses of type.
Renders a template using the StringTemplate (ST) v4 library.
Builder for configuring and creating
StTemplateRenderer instances.Enum representing different styles for images.
Title extractor with adjacent sharing that uses generative to extract
'section_summary', 'prev_section_summary', 'next_section_summary' metadata fields.
Implementation of
ToolCallback that adapts MCP tools to Spring AI's tool
interface.Implementation of
ToolCallbackProvider that discovers and provides MCP tools
from one or more MCP servers.A message of the type 'system' passed as input.
Renders a template using a given strategy.
A
DocumentReader that reads text from a Resource.A document reader that leverages Apache Tika to extract text from a variety of document
formats, such as PDF, DOC/DOCX, PPT/PPTX, and HTML.
Java client for the Bedrock Titan Embedding model.
Titan Embedding model ids.
Titan Embedding request parameters.
TitanEmbeddingRequest builder.
Titan Embedding response.
Token
Token count based strategy implementation for
BatchingStrategy.Estimates the number of tokens in a given text or message.
A
TextSplitter that splits text into chunks of a target size in tokens.Marks a method as a tool in Spring AI.
Represents a tool whose execution can be triggered by an AI model.
Provides
ToolCallback instances for tools defined in different sources.A resolver for
ToolCallback instances.Provides
ToolCallback instances for tools defined in different sources.Auto-configuration for common tool calling features of
ChatModel.A set of options that can be used to configure the interaction with a chat model,
including tool calling.
A builder to create a
ToolCallingChatOptions instance.An
ObservationFilter to include the tool call content (input/output) in the
observation.Service responsible for managing the tool calling process for a chat model.
Context used to store data for tool calling observations.
Interface for an
ObservationConvention for tool calling observations.Tool calling observation documentation.
High cardinality key names.
Low cardinality key names.
Configuration properties for tool calling.
A functional interface to convert tool call results to a String that can be sent back
to the AI model.
Represents the context for tool execution in a function calling scenario.
Definition used by the AI model to determine when and how to call the tool.
Utility class for creating
ToolDefinition builders and instances from Java
Method objects.Interface for determining when tool execution should be performed based on model
responses.
Interface for determining when tool execution should be performed based on model
responses.
An exception thrown when a tool execution fails.
A functional interface to process a
ToolExecutionException by either converting
the error message to a String that can be sent back to the AI model or throwing an
exception to be handled by the caller.The result of a tool execution.
Metadata about a tool specification and execution.
Marks a tool argument.
The ToolResponseMessage class represents a message with a function content in a chat
application.
Registers runtime hints for the tool calling APIs.
Miscellaneous tool utility methods.
TracingAwareLoggingObservationHandler<T extends io.micrometer.observation.Observation.Context>
An
ObservationHandler that can wrap another one and makes the tracing data
available for the ObservationHandler.onStop(Observation.Context) method.An implementation of the AbstractEmbeddingModel that uses ONNX-based Transformer models
for text embeddings.
Auto-configuration for Transformers Embedding Model.Configuration properties for the Transformer Embedding model.
Configurations for the
HuggingFaceTokenizer used to convert sentences into
tokens.Root of the hierarchy of Model access exceptions that are considered transient - where
a previously failed operation might be able to succeed when the operation is retried
without any intervention by application-level functionality.
Uses a large language model to translate a query to a target language that is supported
by the embedding model used to generate the document embeddings.
A utility class that provides methods for resolving types and classes related to
functions.
Connection details for a Typesense service client.
DockerComposeConnectionDetailsFactory for TypesenseConnectionDetails.Converts
Filter.Expression into Typesense metadata filter expression format.Configuration properties for Typesense service client.
A vector store implementation that uses Typesense as the backend.
Auto-configuration for Typesense Vector Store.Configuration properties for Typesense Vector Store.
Utility class for detecting and normalizing URLs.
Abstract Data Type (ADT) encapsulating metadata on the usage of an AI provider's API
per AI request.
An utility class to provide support methods handling
Usage.A message of the type 'user' passed as input Messages with the user role are from the
end-user or developer.
Validation modes for template renderers.
The
VectorStore interface defines the operations for managing and querying
documents in a vector database.Builder interface for creating VectorStore instances.
Memory is retrieved from a VectorStore added into the prompt's system text.
Builder for VectorStoreChatMemoryAdvisor.
Retrieves documents from a vector store that are semantically similar to the input
query.
Builder for
VectorStoreDocumentRetriever.Collection of attribute keys used in vector store observations (spans, metrics,
events).
Auto-configuration for Spring AI vector store observations.
Context used to store metadata for vector store operations.
A
ObservationConvention for VectorStoreObservationContext.Documented conventions for vector store observations.
High-cardinality observation key names for vector store operations.
Low-cardinality observation key names for vector store operations.
Configuration properties for vector store observations.
Collection of systems providing vector store functionality.
Handler for emitting the query response content to logs.
Types of similarity metrics used in vector store operations.
Auto-configuration for Vertex AI Embedding Connection.
VertexAiEmbeddingConnectionDetails represents the details of a connection to the Vertex
AI embedding service.
Configuration properties for Vertex AI Embedding.
Utility class for constructing parameter objects for Vertex AI embedding requests.
Auto-configuration for Vertex AI Gemini Chat.
Vertex AI Gemini Chat Model implementation that provides access to Google's Gemini
language models.
Options for the Vertex AI Gemini Chat API.
Configuration properties for Vertex AI Gemini Chat.
Configuration properties for Vertex AI Gemini Chat.
Constants for Vertex AI Gemini.
The VertexAiGeminiRuntimeHints class is responsible for registering runtime hints for
Vertex AI Gemini API classes.
Enum representing methods for evaluating harmful content.
Enum representing different threshold levels for blocking harmful content.
Enum representing different categories of harmful content.
Auto-configuration for Vertex AI Gemini Chat.
Implementation of the Vertex AI Multimodal Embedding Model.
VertexAI Embedding Models: - Text
embeddings - Multimodal
embeddings
Class representing the options for Vertex AI Multimodal Embedding.
Configuration properties for Vertex AI Gemini Chat.
Auto-configuration for Vertex AI Gemini Chat.
A class representing a Vertex AI Text Embedding Model.
VertexAI Embedding Models: - Text
embeddings - Multimodal
embeddings
Options for the Vertex AI Text Embedding service.
Configuration properties for Vertex AI Gemini Chat.
Implementation of
ToolCallingManager specifically designed for Vertex AI
Gemini.Converts
Filter.Expression into Weaviate metadata filter expression format.A vector store implementation that stores and retrieves vectors in a Weaviate database.
Defines the consistency levels for Weaviate operations.
Represents a metadata field configuration for Weaviate vector store.
Defines the supported types for metadata fields.
Auto-configuration for Weaviate Vector Store.Configuration properties for Weaviate Vector Store.
Single class implementation of the ZhiPuAI Chat Completion API and
ZhiPuAI Embedding API.
Represents a chat completion response returned by model, based on the provided input.
Chat completion choice.
Represents a streamed chunk of a chat completion response returned by model, based on the provided input.
Chat completion choice.
The reason the model stopped generating tokens.
Message comprising the conversation.
The function definition.
An array of content parts with a defined type.
The image content of the message.
The role of the author of this message.
The relevant tool call.
Creates a model response for the given chat conversation.
An object specifying the format that the model must output.
Helper factory that creates a tool_choice of type 'none', 'auto' or selected function by name.
ZhiPuAI Chat Completion Models:
ZhiPuAI Model.
Represents an embedding vector returned by embedding endpoint.
List of multiple embedding responses.
ZhiPuAI Embeddings Models:
Embeddings.
Creates an embedding vector representing the input text.
Represents a tool the model may call.
Function definition.
Create a tool of type 'function' and the given function definition.
Log probability information for the choice.
Message content tokens with log probability information.
The most likely tokens and their log probability, at this token position.
Usage statistics for the completion request.
Chat
Auto-configuration for ZhiPuAI.ZhiPuAiChatOptions represents the options for the ZhiPuAiChat model.
Configuration properties for ZhiPuAI chat model.
Embedding
Auto-configuration for ZhiPuAI.ZhiPuAI Embedding Model implementation.
The ZhiPuAiEmbeddingOptions class represents the options for ZhiPuAI embedding.
Configuration properties for ZhiPuAI embedding model.
ZhiPuAI Image API.
ZhiPuAI Image API model.
Image
Auto-configuration for ZhiPuAI.ZhiPuAiImageModel is a class that implements the ImageModel interface.
ZhiPuAiImageOptions represents the options for image generation using ZhiPuAI image
model.
Configuration properties for ZhiPuAI chat model.
The ZhiPuAiRuntimeHints class is responsible for registering runtime hints for ZhiPu AI
API classes.
Helper class to support Streaming function calling.
Common value constants for ZhiPu api.