Features
Spring AI provides the following features:

Support for all major AI Model providers such as Anthropic, OpenAI, Microsoft, Amazon, Google, and Ollama. Supported model types include:
Chat Completion
Embedding
Text to Image
Audio Transcription
Text to Speech
Moderation
Portable API support across AI providers for both synchronous and streaming API options are supported. Access to model-specific features is also available.
Structured Outputs - Mapping of AI Model output to POJOs.
Support for all major Vector Database providers such as Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate.
Portable API across Vector Store providers, including a novel SQL-like metadata filter API.
Tools/Function Calling - permits the model to request the execution of client-side tools and functions, thereby accessing necessary real-time information as required.
Observability - Provides insights into AI-related operations.
Document injection ETL framework for Data Engineering.
AI Model Evaluation - Utilities to help evaluate generated content and protect against hallucinated response.
ChatClient API - Fluent API for communicating with AI Chat Models, idiomatically similar to the WebClient and RestClient APIs.
Advisors API - Encapsulates recurring Generative AI patterns, transforms data sent to and from Language Models (LLMs), and provides portability across various models and use cases.
Support for Chat Conversation Memory and Retrieval Augmented Generation (RAG).
Spring Boot Auto Configuration and Starters for all AI Models and Vector Stores - use the start.spring.io to select the Model or Vector-store of choice.
This feature set lets you implement common use cases such as "Q&A over your documentation" or "Chat with your documentation."