Introduction to Spring AI

When it comes to AI large language model development, the most popular programming language is Python. Python dominates this space. The second most popular language is JavaScript/TypeScript. For example, the popular LangChain library has two versions: Python and JavaScript/TypeScript. With the popularity of AI large language model application development, there is a very real question facing the majority of Java developers: whether to switch to Python. Learning a new programming language is not difficult. What is difficult is adapting to the new ecosystem. This includes not only the programming language itself, but also standard libraries, commonly used third-party libraries, best practices for code writing, error debugging, performance tuning, production environment deployment, SRE, etc. These all require the accumulation of time and experience.

Current AI large language model application development uses the AI model as a service and interacts with it through the API interface provided by the model service. This interactive capability can be achieved in any programming language. The difference lies in the availability of high-quality libraries that simplify the development of large language model applications. The advantage of Python is that there are already a large number of mature libraries available, and development efficiency will be very high.

With the popularity of AI models, related Java libraries are also rapidly enriched. Representative ones include Spring AI and LangChain4J. The emergence of these libraries allows Java developers to develop AI applications using the language they are most familiar with. Of course, it needs to be admitted that there is still some gap between the maturity of Java libraries and Python libraries. However, these Java libraries are constantly being updated.

The most popular libraries for developing large model applications in Java are Spring AI and LangChain4j. LangChain4j is relatively mature, and many of its concepts are similar to LangChain. Spring AI is a sub-project of Spring, which has a similar design with other Spring projects. It has done a good job in extensibility, and can be easily integrated into existing Spring projects. The advantage of Spring AI is that, by relying on the Spring framework as the foundation, whether it is an existing Spring application, or a newly created Spring application, it can be well integrated. It is also a good choice for existing applications to expand AI-related functionality. In addition, Spring AI is also more secure in terms of stability and update speed. The development of Spring AI is similar to Spring Cloud. There will be more and more integrations, including large language models, text embedding models, vector stores and so on. Developing with Spring AI is more favorable in the long run.