Langchain4J - An Introduction for Impatient Developers

Stop writing boilerplate HTTP code. LangChain4J lets you build powerful, data-aware AI applications in just a few lines of Java.

#1about 6 minutes

Core concepts and ethical considerations of generative AI

Generative AI uses transformer models to create synthetic content, but its broad applications come with ethical challenges like deepfakes and algorithmic bias.

#2about 3 minutes

The commoditization of AI and the pursuit of AGI

Generative AI services are becoming a commodity like cloud computing, abstracting complexity and driving the industry towards the goal of Artificial General Intelligence (AGI).

#3about 5 minutes

Simplifying GenAI development with the LangChain4J framework

LangChain4J abstracts the complexity of interacting with GenAI services like OpenAI, reducing verbose Java code for tasks like RAG and function calling.

#4about 4 minutes

Leveraging Oracle Database 23ai for AI vector search

Oracle Database 23ai includes a native vector data type and extended SQL functions, enabling it to act as a powerful vector store for AI applications.

#5about 6 minutes

Demo: Comparing pure Java with LangChain4J for API calls

A code comparison demonstrates how LangChain4J significantly reduces the boilerplate code needed to interact with the OpenAI API compared to a pure Java implementation.

#6about 8 minutes

Demo: Implementing RAG with LangChain4J and a vector database

This demo illustrates the Retrieval-Augmented Generation (RAG) pattern by ingesting a PDF, creating vector embeddings, and using Oracle Database 23ai to provide context for more accurate LLM responses.