Juarez Junior
Langchain4J - An Introduction for Impatient Developers
#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.
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An overview of the LangChain4j framework for Java
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Code walkthrough for building a RAG-based chatbot
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