LangChain4j
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Software Development
Building Self-Evaluating AI Systems
AI systems are increasingly being designed to do more than generate responses to user requests. Applications can retrieve information from…
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Java
Extracting Reliable Structured Data from LLMs
Large language models are good at understanding natural language, but applications usually need information in a predictable form. A customer…
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Enterprise Java
Intelligent Document Processing with Apache Camel, Docling & LangChain4j
Enterprise applications often need to process large numbers of documents such as PDFs, invoices, manuals, reports, and policy documents. Extracting…
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Core Java
LangChain4j RAG in Java: Embedding, Chunking, and Retrieval Without the Python Envy
A practical pipeline for document ingestion, embedding, and retrieval, all on the JVM, with a Spring AI comparison for good…
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Enterprise Java
Building a Document Q&A RAG AI Assistant with LangChain4j
Large Language Models have transformed how applications interact with users, but they come with a major limitation: they do not…
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Enterprise Java
RAG Architecture on the JVM: Building a Production-Ready Pipeline With LangChain4j
A practical walkthrough of embedding models, vector stores, retrieval strategies, prompt engineering, and evaluation — without leaving the JVM. 1.…
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Enterprise Java
Building AI-Powered Applications with Spring AI and LangChain4j
1. Introduction: Java Enters the AI Arena For years, Python dominated AI development while Java—the backbone of enterprise systems—remained on…
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Enterprise Java
LangChain4j Quarkus MCP Example
The Model Context Protocol (MCP) is gaining traction as an essential bridge between Large Language Models (LLMs) and external tools.…
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Core Java
LLM Apps in Java Using LangChain4j
LangChain4j is a powerful Java framework that simplifies the integration of large language models (LLMs) into Java applications. It provides…
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