All Classes and Interfaces
Class
Description
Abstract class for the Bedrock API.
Encapsulates the metrics about the model invocation.
Abstract
OutputParser implementation that uses a pre-configured
DefaultConversionService to convert the LLM output into the desired type
format.Abstract implementation of the
EmbeddingClient interface that provides
dimensions calculation caching.Abstract
OutputParser implementation that uses a pre-configured
MessageConverter to convert the LLM output into the desired type format.Java client for the Bedrock Jurassic2 chat model.
Ai21 Jurassic2 models version.
AI21 Jurassic2 chat request parameters.
Penalty with float scale value.
Penalty with integer scale value.
Ai21 Jurassic2 chat response.
Explains why the generation process was halted for a specific completion.
The generatedToken fields.
The prompt includes the raw text, the tokens with their log probabilities, and the top-K alternative tokens
at each position, if requested.
The textRange field indicates the start and end offsets of the token in the decoded text string.
Provides detailed information about each token in both the prompt and the completions.
The topTokens field is a list of the top K alternative tokens for this position, sorted by probability,
according to the topKReturn request parameter.
Native runtime hints.
AllOfGenerateResponseDetails
AllOfStreamResponseDetails
Anthropic models version.
AnthropicChatRequest encapsulates the request parameters for the Anthropic chat model.
AnthropicChatResponse encapsulates the response parameters for the Anthropic chat model.
Lets the generative know the content was generated as a response to the user.
Converts
Filter.Expression into Azure Search OData filter syntax.ChatClient implementation for Microsoft Azure AI backed by
OpenAIClient.The configuration information for a chat completions request.
ChatResponseMetadata implementation for
Microsoft Azure OpenAI Service.The configuration information for the embedding requests.
Usage implementation for Microsoft Azure OpenAI Service.Uses Azure Cognitive Search as a backing vector store.
An implementation of
OutputParser that transforms the LLM output to a specific
object type using JSON schema.Auto-configuration for Bedrock Anthropic Chat Client.Java
ChatClient and StreamingChatClient for the Bedrock Anthropic chat
generative.Configuration properties for Bedrock Anthropic.
Configuration properties for Bedrock AWS connection.
Auto-configuration for Bedrock Cohere Chat Client.Bedrock Cohere Chat autoconfiguration properties.
Auto-configuration for Bedrock Cohere Embedding Client.EmbeddingClient implementation that uses the
Bedrock Cohere Embedding API.Bedrock Cohere Embedding autoconfiguration properties.
Auto-configuration for Bedrock Llama2 Chat Client.Java
ChatClient and StreamingChatClient for the Bedrock Llama2 chat
generative.Configuration properties for Bedrock Llama2.
The BedrockRuntimeHints class is responsible for registering runtime hints for Bedrock
AI API classes.
Auto-configuration for Bedrock Titan Chat Client.Bedrock Titan Chat autoconfiguration properties.
Auto-configuration for Bedrock Titan Embedding Client.EmbeddingClient implementation that uses the
Bedrock Titan Embedding API.Bedrock Titan Embedding autoconfiguration properties.
Usage implementation for Bedrock API.BestOfSequence
Abstract Data Type (ADT) encapsulating information on the completion choices in the AI
response.
The ChatOptions represent the common options, portable across different chat models.
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.
Abstract Data Type (ADT) modeling 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 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.
Converts
Filter.Expression into Chroma metadata filter expression format.ChromaVectorStore is a concrete implementation of the VectorStore
interface.Java client for the Bedrock Cohere chat model.
Cohere models version.
CohereChatRequest encapsulates the request parameters for the Cohere command model.
Builder for the CohereChatRequest.
Prevents the model from generating unwanted tokens or incentivize the model to include desired tokens.
(optional) Specify how and if the token likelihoods are returned with the response.
Specifies how the API handles inputs longer than the maximum token length.
CohereChatResponse encapsulates the response parameters for the Cohere command model.
Generated result along with the likelihoods for tokens requested.
The reason the response finished being generated.
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.
CompatGenerateRequest
Converts the Document text and metadata into a AI, prompt-friendly text representation.
Details
A document is a container for the content and metadata of a document.
Represents a single embedding vector.
EmbeddingClient is a generic interface for embedding clients.
Embedding response object.
A RateLimit implementation that returns zero for all property getters
A EmpytUsage implementation that returns zero for all property getters
ErrorResponse
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.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.
Represents a model function call handler.
A Spring
ApplicationContextAware implementation that provides a way to retrieve
a Function from the Spring context and wrap it into a FunctionCallback.Note that the underlying function is responsible for converting the output into format
that can be consumed by the Model.
GenerateParameters
GenerateRequest
GenerateResponse
Represents a response returned by the AI.
An implementation of
ChatClient that interfaces with HuggingFace Inference
Endpoints for text generation.Interface for generating unique document IDs.
ImageOptions represent the common options, portable across different image generation
models.
Info
A SHA-256 based ID generator that returns the hash as a UUID.
Utility class for JSON processing.
Keyword extractor that uses generative to extract 'excerpt_keywords' metadata field.
OutputParser implementation that uses a DefaultConversionService to
convert the LLM output into a List instance.Java client for the Bedrock Llama2 chat model.
Llama2 models version.
Llama2ChatRequest encapsulates the request parameters for the Meta Llama2 chat model.
Llama2ChatResponse encapsulates the response parameters for the Meta Llama2 chat model.
The reason the response finished being generated.
OutputParser implementation that uses a pre-configured
MappingJackson2MessageConverter to convert the LLM output into a
java.util.Map<String, Object> instance.Converts a list of messages to a prompt for bedrock models.
Converts
Filter.Expression into Milvus metadata filter expression format.Parameters for Milvus client connection.
Configuration for the Milvus vector store.
The ModelClient interface provides a generic API for invoking AI models.
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.
Converts
Filter.Expression into Neo4j condition expression format.An enum to configure the distance function used in the Neo4j vector index.
Configuration for the Neo4j vector store.
Java Client for the Ollama API.
Chat request object.
Ollama chat response object.
Generate embeddings from a model.
The response object returned from the /embedding endpoint.
The request object sent to the /generate endpoint.
The response object returned from the /generate endpoint.
Chat message object.
The role of the message in the conversation.
Auto-configuration for Ollama Chat Client.ChatClient implementation for Ollama.Ollama Chat autoconfiguration properties.
Ollama connection autoconfiguration properties.
EmbeddingClient implementation for Ollama.Ollama Embedding autoconfiguration properties.
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: https://platform.openai.com/docs/api-reference/chat and
OpenAI Embedding API: https://platform.openai.com/docs/api-reference/embeddings.
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.
An object specifying the format that the model must output.
Specifies a tool the model should use.
Represents an embedding vector returned by embedding endpoint.
List of multiple embedding responses.
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.
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.
API error response.
Error details.
Usage statistics for the completion request.
Enumeration of OpenAI API response headers.ChatClient implementation for OpenAI backed by OpenAiApi.ChatResponseMetadata implementation for OpenAI.Open AI Embedding Client implementation.
OpenAI Image API.
OpenAiImageClient is a class that implements the ImageClient interface.
OpenAI Image API options.
OpenAI Image 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.
Usage implementation for OpenAI.Converts the (raw) LLM output into a structured responses of type.
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.
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.Uses the "vector_store" table to store the Spring AI vector data.
Defaults to CosineDistance.
By default, pgvector performs exact nearest neighbor search, which provides perfect
recall.
Converts
Filter.Expression into Pinecone metadata filter expression format.A VectorStore implementation backed by Pinecone, a cloud-based vector database.
Configuration class for the PineconeVectorStore.
Auto-configuration class for PostgresMlEmbeddingClient.
PostgresML EmbeddingClient
Configuration properties for Postgres ML.
PrefillToken
Converts
Filter.Expression into test string format.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 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.The RedisVectorStore is for managing and querying vector data in a Redis database.
Configuration for the Redis vector store.
Service that helps caching remote
Resources on the local file system.Interface representing metadata associated with an AI model's response.
Interface representing metadata associated with the results of an AI model.
Similarity search request builder.
SimpleVectorStore is a simple implementation of the VectorStore interface.
Represents the StabilityAI API.
StabilityAiImageClient is a class that implements the ImageClient interface.
Represents metadata associated with the image generation process in the StabilityAI
framework.
StabilityAiImageOptions is an interface that extends ImageOptions.
StreamDetails
The StreamingModelClient interface provides a generic API for invoking a AI models with
streaming response.
StreamResponse
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.
A message of the type 'system' passed as input.
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 chat model.
Titan models version.
TitanChatRequest encapsulates the request parameters for the Titan chat model.
Titan request text generation configuration.
TitanChatResponse encapsulates the response parameters for the Titan chat model.
The reason the response finished being generated.
Titan response result.
Titan chat model streaming response.
Java client for the Bedrock Titan Embedding model.
Titan Embedding model ids.
Titan Embedding request parameters.
TitanEmbeddingRequest builder.
Titan Embedding response.
Token
https://www.sbert.net/index.html https://www.sbert.net/docs/pretrained_models.html
Configurations for the
HuggingFaceTokenizer used to convert sentences into
tokens.Abstract Data Type (ADT) encapsulating metadata on the usage of an AI provider's API
per AI request.
A message of the type 'user' passed as input Messages with the user role are from the
end-user or developer.
The
VectorStore interface defines the operations for managing and querying
documents in a vector database.Vertex AI API client for the Generative Language model.
A list of floats representing the embedding.
Message generation request body.
The response from the model.
Content filtering metadata associated with processing a single request.
Reasons why content may have been blocked.
The base unit of structured text.
A collection of source attributions for a piece of content.
A citation to a source for a portion of a specific response.
All of the structured input text passed to the model as a prompt.
An input/output example used to instruct the Model.
Information about a Generative Language Model.
API error response.
Error details.
The VertexRuntimeHints class is responsible for registering runtime hints for Vertex AI
API classes.
Converts
Filter.Expression into Weaviate metadata filter expression format.A VectorStore implementation backed by Weaviate vector database.
Configuration class for the WeaviateVectorStore.
https://weaviate.io/developers/weaviate/concepts/replication-architecture/consistency#tunable-consistency-strategies