AI models communicate with programs via REST API calls, but Communication with AI is a bit complex. Spring AI provides an Abstract layer and tries to reduce the complexity and ease the development of AI models with minimum complexities.
Although the communication with the AI model is via RET API only, but has a lot of underlying complex structures. AI with Spring Boot tries to simplify the API call.
Spring AI is a lot inspired by Python projects, such as LangChain and LlamaIndex, but is not a direct port of those Python projects. Spring AI utilizes the existing spring features such as Autoconfiguration, Inversion of control, Embedded web server, etc.
Benefits of Spring AI
- Spring AI supports all major AI Model providers such as OpenAI, Microsoft, Amazon, Google, and Hugging Face.
- Spring AI supports almost all available Model types such as Chat, Text to Image conversion, Audio Transcription, Text to Speech conversion, and Moderation, etc.
- Spring by default supports Reactive and imperative API responses, that can be used to communicate with AI Models.
- The default response from the AI Model is String-based only, but Spring AI supports all major AI Model responses to POJO conversion.
- AI Models persist data in a special type of database called Vector Data Base. Spring AI supports almost all major Vector Databases such as Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate.
- Spring Boot supports starters and autoconfigures all AI Models and Vector Stores with the default value.
