Lesson 25 of 38 · Written learning
Embeddings and similarity search
Understand how vector representations support semantic search and where similarity can mislead.
An embedding model maps text or another input to a list of numbers. Inputs used in related contexts may receive vectors that are closer under a chosen metric. Cosine similarity compares direction; dot product also reflects magnitude; Euclidean distance measures straight-line separation. The best choice depends on the embedding model and index configuration.
Search is a ranking problem
A vector index helps retrieve nearby items without comparing every vector in a large collection. Keyword search finds exact or near-exact terms; hybrid search combines lexical and semantic signals. Neither method understands a document as a person does. Search quality depends on the corpus, query, representation, and ranking process.
- Use the embedding model's recommended normalization and distance metric.
- Keep the text, source, and metadata associated with each stored vector.
- Compare semantic retrieval with a keyword baseline on real queries.
- A close vector match is a candidate for reading, not proof that the passage answers the question.
Return to the Embeddings, vector search, and RAG stage · Suggested timing is flexible; move at your own pace.