Enter the Brave New World of GenAI with Vector Search

What's the key to making LLMs useful for your business? Learn how vector search connects generative AI to your company's private data.

#1about 6 minutes

A brief history of artificial intelligence development

Artificial intelligence concepts date back to ancient Greece, with modern computing foundations laid by figures like Alan Turing in the 20th century.

#2about 7 minutes

Understanding AI, machine learning, and deep learning

AI is the broad field of mimicking human intelligence, with machine learning as a subset that learns from data, and deep learning as the core using neural networks.

#3about 4 minutes

The recent evolution of generative AI models

Key developments in the last two decades, from early neural network language models to the transformative "Attention Is All You Need" paper, led to models like GPT and Stable Diffusion.

#4about 6 minutes

GenAI applications and emerging professional roles

Generative AI powers multimodal applications like ChatGPT and GitHub Copilot, creating specialized roles such as AI engineer, ML ops, and prompt engineer.

#5about 8 minutes

Defining key GenAI concepts like GPT and LLMs

Core technologies like Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), and Large Language Models (LLMs) form the foundation of modern AI systems.

#6about 3 minutes

Exploring APIs and frameworks for Java developers

Developers can leverage frameworks like LangChain and Llama 2, with specific Java libraries such as Jlama, JVector, and LangChain4j enabling GenAI development in the Java ecosystem.

#7about 9 minutes

How vector databases enable similarity search

Vector databases store data as multi-dimensional numerical representations called embeddings, using algorithms like Approximate Nearest Neighbor (ANN) to perform fast similarity searches.

#8about 4 minutes

Practical use cases for vector embeddings

Vector embeddings are used for similarity searches, content recommendations, anomaly detection, and text classification, often implemented with a Retrieval-Augmented Generation (RAG) pattern.

#9about 8 minutes

Demo of setting up Astra DB for vector search

A step-by-step walkthrough shows how to create a free vector database instance on DataStax Astra DB, configure a collection, and prepare for data loading.

#10about 5 minutes

Challenges and ethical concerns in generative AI

While powerful, generative AI faces challenges like model hallucinations, data privacy issues, and the need for regulatory oversight to ensure ethical usage.