Embeddings & Vector Search, Explained

What an embedding actually is (meaning turned into a list of numbers), how 'nearness' between two pieces of text is measured, and how vector databases search millions of them by meaning instead of keywords.

1. Meaning as Coordinates - What an Embedding Actually Is An embedding is a list of numbers (a vector) that captures the meaning of a piece of text or image, placing it on a map where similar meanings land near each other and unrelated ones land far apart.
2. Measuring Similarity - From 'Near' to Search by Meaning How a computer measures how 'near' two vectors are (cosine similarity / distance), and why embedding your query and finding the nearest stored vectors gives you search by meaning that handles synonyms and paraphrase, not keyword matching.
3. Vector Databases & the Gotchas - Searching Millions, Without Getting Burned How vector databases store millions of embeddings and find nearest neighbors fast using approximate (ANN) indexes, the main tools (pgvector, FAISS, Pinecone, and friends), and the three traps that quietly ruin results: cross-model vectors, chunking, and confusing similarity with correctness.