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EmEmbeddings1
Embeddings
Meaning captured in numbers
retrievalRow 1: Primitivesintermediate1 hourRequires: Pr
Overview
Embeddings convert text into numerical vectors that capture semantic meaning, enabling similarity search and knowledge retrieval.
What is it?
Dense vector representations of text that encode semantic meaning.
Why it matters
Embeddings enable semantic search - finding content by meaning rather than keywords. They are the foundation of RAG systems.
How it works
Neural networks process text and output fixed-size vectors. Similar concepts produce vectors that are close together in vector space.
Real-World Examples
Semantic Search
Finding documents by meaning, not just keywords
Document Clustering
Grouping similar documents automatically
Recommendation Systems
Finding similar items based on embeddings