LangChain4j
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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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Enterprise Java
Java Langchain MongoDB Example
This article explores how to build an AI-powered chatbot using Langchain4j, MongoDB Atlas, and Ollama running locally. This solution leverages…
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Enterprise Java
Using LangChain4j in Micronaut
LangChain4j is a Java library that simplifies working with LLMs, enabling us to create AI-driven applications with minimal complexity. By…
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