Chapter 8. LangChain4j
The previous chapter introduced how to consume AI models as an inference model API. This approach works for simple problems, but when you develop more-complicated solutions that heavily involve AI, you need more features than a simple request/response.
In this chapter, we introduce LangChain4j, a framework for simplifying the integration of AI/LLM capabilities into Java applications providing high-level capabilities like memory or data augmentation. We’ll cover examples using plain Java and its integration with Quarkus and Spring Boot so you can get a full picture of its use in various Java projects.
In this chapter, you’ll learn Langchain4j from the basics to advanced scenarios, using prompting, memory, data augmentation, and image processing. We will save RAG for the next chapter.
What Is LangChain4j?
LangChain4j is a Java implementation inspired by the popular Python LangChain framework. It helps developers build applications that integrate with LLMs. LangChain4j provides tools and abstractions to simplify the integration of LLMs into Java-based applications, enabling functionalities like NLP, text generation, question-answer, and more.
As the LangChain framework, LangChain4j offers features to simplify the development of applications that integrate with LLMs. Let’s dig into some of these key features.
Unified APIs
LLMs offer various APIs to access them. For example, the API to access the OpenAI ChatGPT model might differ from the one to access a Hugging ...
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