🧮 Vector Databases & Embeddings
Learn Vector Databases & Embeddings on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- Embeddings, Simply (7 min) — ⚡ An embedding turns a piece of text (or an image, or audio) into a list of numbers — a vector — that captures its mean…
- What a Vector DB Does (and When You Need One) (7 min) — ⚡ A vector database stores embeddings and answers 'find the K most similar vectors to this one' quickly, even over mill…
- Indexing: HNSW, IVF & the Trade-off (8 min) — ⚡ Exact nearest-neighbor search is too slow at scale, so vector DBs use Approximate Nearest Neighbor (ANN) indexes that…
- Metadata Filtering & Hybrid Search (7 min) — ⚡ Production retrieval rarely uses vectors alone. Two additions make it reliable: metadata filtering and hybrid search.…
- Chunking & Ingestion Pipelines (7 min) — ⚡ Retrieval quality is decided before any search runs — at ingestion and chunking. An embedding represents one chunk, s…
- Operating a Vector Store (8 min) — ⚡ A vector store is a living system. Operating it well means keeping it fresh, scaling it, and measuring retrieval qual…
Lessons
Embeddings, Simply
⚡ An embedding turns a piece of text (or an image, or audio) into a list of numbers — a vector — that captures its meaning. Similar meanings produce vectors that are close together in this high-dimensional space; unrelated things land far apart. This converts a hard problem (doe…
What a Vector DB Does (and When You Need One)
⚡ A vector database stores embeddings and answers 'find the K most similar vectors to this one' quickly, even over millions of items. It does approximate nearest-neighbor (ANN) search, trading a tiny bit of accuracy for huge speed gains over brute-force comparison. You need one …
Indexing: HNSW, IVF & the Trade-off
⚡ Exact nearest-neighbor search is too slow at scale, so vector DBs use Approximate Nearest Neighbor (ANN) indexes that are far faster while occasionally missing a true neighbor. The core tension is the speed/accuracy (recall) trade-off, which you tune. HNSW (Hierarchical Naviga…
Metadata Filtering & Hybrid Search
⚡ Production retrieval rarely uses vectors alone. Two additions make it reliable: metadata filtering and hybrid search. Metadata filtering attaches structured fields (date, author, type, tenant, permissions) to each vector and restricts search to matching items. This is essentia…
Chunking & Ingestion Pipelines
⚡ Retrieval quality is decided before any search runs — at ingestion and chunking. An embedding represents one chunk, so the chunk must be a self-contained, coherent unit of meaning. Chunk by structure where possible (sections, headings, paragraphs) rather than blind fixed-size …
Operating a Vector Store
⚡ A vector store is a living system. Operating it well means keeping it fresh, scaling it, and measuring retrieval quality so regressions don't slip through. Freshness: handle inserts, updates, and deletes as source data changes; stale or orphaned vectors degrade results and can…