From Pints to Insights: Unveiling Semantic Search Power with Word Embeddings and Vector Databases

🍺🔍 "Revolutionizing Search: How Semantic AI Transforms Beer Selection & Beyond | Lucidate Explains"

Join us on a fascinating journey from hops to high-tech with Lucidate's latest innovation in semantic search. In this video, Richard Walker dives into how AI not only helps you pick the perfect beer but also reshapes how we search for information in finance, investment banking, and more.

What You'll Discover:

[0:00] Introduction to Semantic Search
[0:45] The Hoptimus Prime Scenario: AI in Beer Selection
[1:23] Keyword vs. Semantic Search: What’s the Difference?
[3:07] "Vectorizing" text
[4:28] Building Word Embeddings
[7:42] The mathematics of semantics
[7:42] Hoptimus Prime and S.A.S.S.
[10:45] Join Lucidate’s YouTube Membership for Exclusive Content

Whether you're a tech enthusiast, a professional in finance, or just curious about AI's impact on everyday decisions, there's something here for everyone.

📚 Resources & Links:

🚀 Stay Ahead with Lucidate:
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#SemanticSearch #AIInnovation #LucidateTech #datascience
Alternative titles? Which do you prefer? Let me know in the descriptiopn!

"AI Transforms How We Search: Semantic Search Revolution in Action"
"Beyond Keywords: Semantic AI's Game-Changing Role in Information Retrieval"

"Navigating the Data Deluge: Semantic Search Solutions by Lucidate"
"The Future of Search: Lucidate's Deep Dive into AI-Driven Semantic Analysis"

"Choosing Beer with AI? How Semantic Search is Redefining Decision-Making"

"From Hops to AI: Revolutionizing Beer Choice with Semantic Search Tech"
"Semantic Search Unveiled: Lucidate's AI Breakthrough in Understanding Data"

"AI's New Frontier: Semantic Search's Impact from Brews to Banking"

"Unlocking AI's Potential: Semantic Search for Precise, Contextual Results"

"Revolution in Search: How Lucidate's AI Mastery Is Changing the Game"

[neurojitsu]

your explanations make me feel so much smarter! Then the memory fades, and I'm scratching my head again...

[zugbob]

So when a search is made on a sentence is the entire sentence embedded and searched on?

Would it make sense to also search per keyword semantically and combine the results?

[PavelSTL]

The word embeddings are pretty clear, although the explanation implies they are static after training and could be simply looked up in some file, but is that really the case ? I thought the embedding models might not give you the *exact* same embedding numbers every time you run the same word through them, should be easy to test. Ok though, what I'm still struggling to understand is how sentence, or chunk text embeddings work. You can vectorize a chunk up to 8k tokens by OpenAI ada-2, and the resulting embedding will be the same size as individual words (1536). So how does semantic search work for chunks then? Clearly the embedding model cannot be trained on all possible letter combinations of up to 8k tokens, at least the same way as words are. Is the chunk broken into individual words (tokens) and then the average of all individual word embeddings taken to represent the entire chunk with one 'mean' embedding?