Day 7 of 14 35 minEmbeddingsCosine similarityVector databases
Turn text into vectors that capture meaning, compare them with cosine similarity, and find the most relevant texts for a question. The foundation of semantic search and RAG.
By the end of today you can
- Explain what an embedding is and why similar meanings get similar vectors
- Measure similarity between vectors with cosine similarity
- Find the nearest neighbours of a query in a set of documents
- Explain why keyword matching misses things that embeddings catch
- Store and search embeddings in a vector database such as pgvector
What's inside
- 6 sections
- 3 diagrams and mind maps
- 3 code examples you can run
- 5-question quiz
- 1Meaning as a position
- 2Measuring similarity
- 3Why not just match keywords?
- 4Getting real embeddings
- 5Nearest-neighbour search
- 6Storing embeddings: vector databases
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