Enter the Brave New World of GenAI with Vector Search
summary
Generative AI pairs large language models with purpose-built vector databases that store embeddings as arrays of floats (e.g. 1536 dimensions) and use approximate nearest neighbor search to return semantic matches for retrieval‑augmented generation. Popular tooling named includes LangChain, LlamaIndex, Semantic Kernel and Java ports like LangChain4J and J‑Lama; example deployment patterns combine OpenAI GPT APIs with a vector store for RAG. Key facts: GPT‑3.5 reached 1M users in five days; main failure modes are hallucination and model data cutoff, so RAG and up‑to‑date sources are essential.
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chapters (21)
- 0:00Introduction to GenAI
- 1:05Talk agenda overview
- 1:49Speaker background
- 3:13Brief history of AI
- 5:21AI vs Machine Learning vs Deep Learning
- 7:08What is Generative AI
- 9:15Generative AI timeline
- 11:04Key players in GenAI models
- 13:56Generative AI applications
- 14:36Understanding modality in GenAI
- 15:39Key roles in GenAI ecosystem
- 17:03How GPT works
- 19:52Natural Language Processing basics
- 20:55Large Language Models explained
- 23:02Java libraries for GenAI
- 25:10Java-specific GenAI tools
- 26:56Vector databases introduction
- 30:05Vector embeddings and search
- 31:29Benefits and challenges of GenAI
- 32:11Retrieval Augmented Generation
- 32:53Resources and closing
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