spring-projects/spring-ai

Features

• AI Application Frameworks - Ships a portable, fluent Java framework for integrating AI models, tools, and vector stores into applications.
• Fluent Chat Clients - Provides a chainable, idiomatic API for constructing and sending chat requests to AI models.
• Tool Call Execution Loops - Handles the iterative cycle of invoking tools requested by the model and feeding results back until no more calls are needed.
• Provider Switching - Provides a portable abstraction to swap between AI providers like OpenAI and Ollama without code changes.
• Model Context Protocol - Implements the Model Context Protocol to establish a standardized bridge between AI models and external services.
• Model Context Protocol Integrations - Establishes a standardized bridge between AI models and external services using the Model Context Protocol.
• Chat Client Advisors - Adds a chain of interceptors to modify prompts, inject context, or log interactions before calling the AI model.
• Fluent Chat Client APIs - Ships a fluent, chainable API for constructing and sending chat requests to AI models with configurable advisors.
• Multi-Provider Chat Clients - Connects Java applications to multiple AI chat providers through a unified, portable API with streaming and structured output support.
• Portable Chat APIs - Provides a portable, fluent API to send prompts and receive responses from major AI providers in both sync and streaming modes.
• Streaming Chat Responses - Streams the model's reply token-by-token using a reactive Flux API for real-time output.
• AI Model Clients - Provides a fluent, chainable API for constructing and sending chat requests to AI models.
• Prompt Template Retrieval - Constructs user and system messages using template strings with runtime variable substitution.
• AI Tooling Frameworks - Registers Java methods as callable tools that AI models can invoke during conversations with dynamic resolution.
• AI Tooling Interfaces - Registers Java methods or functions as callable tools that an AI model can invoke during a conversation.
• AI Tool Annotations - Exposes Java methods as callable AI tools by annotating them with @Tool, with automatic input schema generation.
• Conversation Memory Stores - Stores and retrieves chat history for coherent multi-turn conversations across sessions.
• Retrieval-Augmented Generation - Combines document retrieval with language model generation for context-aware question answering.
• Model Output Transformers - Transforms unstructured AI responses into strongly-typed Java POJOs for structured data handling.
• MCP Protocol Integrations - Establishes a standardized bridge between AI models and external services using the Model Context Protocol.
• Function Calling Interfaces - Provides a framework for AI models to request execution of user-defined functions to access real-time information.
• Automatic Tool Executions - Uses an advisor to handle the full tool-calling lifecycle automatically without manual intervention.
• Advisor Frameworks - Wraps recurring patterns like memory, tool-calling, and RAG into reusable advisors that transform data sent to and from language models.
• Reusable Advisor Patterns - Wraps recurring patterns like memory, tool-calling, and RAG into reusable advisors that transform data flow.
• Tool Calling - Exposes Java methods as callable tools that an AI model can invoke during a conversation.
• Persistent Chat Histories - Stores and retrieves conversation history across sessions using pluggable persistent backends.
• Service Provider Abstractions - Wraps multiple AI model providers behind a common interface, allowing provider switching without code changes.
• Multi-Provider Abstractions - Provides a consistent abstraction for interacting with chat, embedding, image, and speech models across multiple providers.
• Model Response Streaming - Supports both synchronous and streaming API options for AI model interactions with real-time output.
• Textual Response Relevance Evaluators - Provides built-in evaluators that judge whether AI responses stay on topic and are factually supported.
• Retrieval-Augmented Generation Frameworks - Implements RAG pipelines that combine document retrieval from vector stores with language model generation.
• Retrieval Augmented Generation Pipelines - Builds question-answering systems that retrieve relevant documents from a vector store and inject them into AI prompts.
• Prompt Augmenters - Enriches user queries with relevant information from a knowledge base before sending them to the AI model.
• Structured Output Converters - Transforms unstructured text responses from AI models into strongly-typed Java objects using built-in converters.
• Text Embeddings - Converts text, documents, or batches of text into numerical vector representations using an embedding model.
• AI Model and API Integration - Provides a consistent abstraction for interacting with chat, text-to-image, and embedding models across multiple providers.
• AI Interaction Interceptors - Intercepts, inspects, and modifies prompt data and responses flowing between the user and the language model through advisors.
• Knowledge Base Question Answering - Implements a Retrieval-Augmented Generation pattern that uses a vector store to retrieve relevant documents and answer user questions.
• Structured Return Objects - Maps AI model text output directly into Java objects or collections of objects for type-safe use.
• Document Ingestion Pipelines - Extracts, transforms, and loads documents into a vector store for retrieval-augmented generation pipelines.
• Vector Stores - Offers a portable API to store and query vector embeddings across a wide range of database providers.
• Conversation Memory Retrieval - Supports chat conversation memory and retrieval-augmented generation for context-aware interactions.
• Factual Grounding - Checks if claims in AI responses are factually supported by the given context to catch hallucinations.
• Conversation History Backends - Stores and retrieves conversation history across multiple pluggable storage backends.
• Embedding Generation - Provides a portable API for converting text into numerical vector representations across multiple AI providers.
• MCP Server Integrations - Supports the Model Context Protocol for consuming external AI services or exposing Spring services to the AI ecosystem.
• AI Prompt Sending APIs - Sends a textual prompt to an AI language model and receives a generated natural-language response.
• AI Advisor Chains - Implements a configurable chain of advisor interceptors that modify prompts and responses around AI model calls.
• Object Mappings - Converts structured AI model responses directly into Plain Old Java Objects for type-safe downstream processing.
• POJO Mappings - Converts AI model responses directly into Plain Old Java Objects for type-safe downstream processing.
• Chat API Integrations - Offers a fluent, idiomatic API for interacting with chat models, similar to WebClient and RestClient.
• Model Tool Calls - Allows AI models to request execution of user-defined tools during conversations for real-time data access.
• Anthropic Claude Connections - Connects to Anthropic's Claude models through the official Java SDK for chat and completion tasks.
• Dynamic Tool Management - Provides a ToolCallbackResolver for looking up tools by name at runtime from a registry.
• Client Connection Lifecycles - Handles protocol negotiation, tool discovery, resource access, and message transport for MCP server connections.
• Chat Model Configurations - Sets model parameters such as temperature, token limit, and stop sequences at startup or per request.
• Service Exposures - Makes Spring-based services discoverable and callable by AI applications through the Model Context Protocol.
• Text-to-Image Generators - Provides a unified API for generating images from text descriptions using supported AI models.
• API Speech Synthesizers - Provides a portable API for generating spoken audio from text using supported AI providers.
• Bean Deserializations - Converts raw AI model text responses into typed Java objects by appending JSON schema instructions to the prompt.
• Native Schema Enforcements - Sends JSON schemas directly to the model's structured output endpoint for reliable schema compliance.
• Full Response Aggregation - Retrieves the complete AI model response including metadata such as token usage and multiple generations.
• Image Model Provider Switching - Provides a portable API to switch image generation providers by changing a configuration value.
• Transcription Provider Switching - Provides a portable API to switch transcription providers by changing a configuration value.
• Image Description Generators - Provides a unified API for generating images from text prompts using supported AI models.
• Unified Audio Transcription - Provides a portable API for converting spoken audio into text using supported AI providers.
• Streaming Speech Outputs - Provides streaming speech synthesis that delivers audio incrementally for real-time playback.
• Vector Database ETL Tools - Extracts, transforms, chunks, and loads documents from various sources into vector databases for RAG.
• Similarity Query Engines - Accepts text queries and returns semantically similar documents ranked by vector similarity.
• AI Observability and Evaluation - Captures metrics, traces, and logs for AI model interactions and evaluates response quality to detect hallucinations.
• Document Chunking Expansion - Divides document text into smaller segments based on token count or structure for AI context windows.
• Document and File Processing - Reads PDF, JSON, HTML, Markdown, DOCX, PPTX, and plain text files into Document objects.
• Document Data Extraction - Loads, processes, and structures documents from various sources for ingestion into AI workflows.
• Data Loaders - Extracts, transforms, and loads data from various sources into a vector database to support retrieval-augmented generation.
• Document Writers - Stores processed Document objects into a vector database for use in retrieval-augmented generation workflows.
• Filtered Queries - Offers a portable API across vector database providers, including a SQL-like metadata filter for precise retrieval.
• Metadata Filters - Applies portable SQL-like metadata filters to vector store queries for precise result retrieval.
• Filtered Similarity Searches - Restricts vector similarity results using metadata field expressions for precise retrieval.
• Tool and Resource Exposures - Provides tools, resources, and prompt templates to MCP clients with concurrent connection management.
• TTS Provider Switching - Provides a portable API to switch text-to-speech providers by changing a configuration value.
• MCP Tool Behavior Annotations - Creates tools, resources, and prompts on a server using Java annotations to reduce boilerplate.
• Prompt Templates - Builds user and system messages by substituting runtime variables into template strings at request time.
• Audio-to-Text Alignment - Provides a portable API for converting spoken audio from files into written text using speech-to-text models.
• Text-to-Speech Synthesizers - Provides a portable API for converting text strings into audio files using text-to-speech models.
• Content Moderation - Inspects AI model output against safety policies to block harmful or inappropriate responses.
• Auto-Configuration Mechanisms - Bootstraps AI model and vector store connections automatically using Spring Boot auto-configuration.
• AI Starter Configurations - Auto-configures AI model and vector store connections using dependency-driven starters and standard properties.
• Spring Boot AI Starters - Auto-configures AI model and vector store connections using Spring Boot starters and dependency-driven configuration.
• AI Observability - Provides insights and monitoring data into the lifecycle of AI model interactions for debugging and performance analysis.

Star history