评估
Spring AI旨在为Java程序员提供Spring和AI的集成，简化包含人工智能功能的Java应用程序的开发，避免不必要的复杂性。
Spring AI项目从著名的Python项目，例如LangChain和LlamaIndex中汲取灵感， 但Spring AI并不是这些项目的直接移植，而是结合Spring框架和AI的特点，提供了更加Java友好的API。
Spring AI核心特性如下：
- Support for all major Model providers such as OpenAI, Microsoft, Amazon, Google, and Huggingface.
- Supported Model types are Chat and Text to Image with more on the way.
- Portable API across AI providers for Chat and for Embedding models. Both synchronous and stream API options are supported. Dropping down to access model specific features is also supported.
- Mapping of AI Model output to POJOs.
- Support for all major Vector Database providers such as Azure Vector Search, Chroma, Milvus, Neo4j, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate
- Portable API across Vector Store providers, including a novel SQL-like metadata filter API that is also portable.
- Function calling
- Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.
- ETL framework for Data Engineering
Spring AI structured output diagram:
ChatGPT & Friends
- LangChain: https://github.com/langchain-ai/langchain
- LlamaIndex: https://github.com/run-llama/llama_index
- LangChain4j: https://github.com/langchain4j/langchain4j
- ChatGPT Spring Boot Starter: https://github.com/linux-china/chatgpt-spring-boot-starter
- Qdrant: High-performance, massive-scale Vector Database - https://github.com/qdrant/qdrant
References
- Spring AI Docs: https://docs.spring.io/spring-ai/reference/
- ChatGPT: https://openai.com/chatgpt
- OpenAI API: https://platform.openai.com/docs/api-reference
- Assistants Tools: https://platform.openai.com/docs/assistants/tools