Embeddings: Core Concepts for Developers
summary
Embeddings are numeric arrays that map meaning into hundreds of dimensions and are stored in a Pinecone vector database for fast similarity lookup. Examples include king − man + woman = queen and the word “bank” splitting into distinct vectors for financial, river, and aviation senses. Practical finding: semantic distances use cosine distance so red and purple can be closer than red and orange because of language usage patterns. A QR links to try creating and upserting embeddings yourself.
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chapters (8)
- 0:00Embeddings introduction
- 1:05Text to vectors
- 3:15Semantic vs keyword search
- 6:43Visualizing high-dimensional vectors
- 8:08Word2Vec arithmetic
- 12:03Explaining embeddings
- 13:27Color analogy limits
- 20:29TL;DR embeddings
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