Caching and Updating Embeddings

Lifecycle

Part of: Embeddings & Semantic Search

Embedding is not free. Every call to the model costs money and milliseconds, and a real knowledge base has millions of chunks that mostly never change. Re-embedding all of them on every deploy would waste both your budget and your latency. So production systems treat embeddings as a cache you maintain over time , not a thing you compute once and forget. This lesson is the unglamorous lifecycle work that keeps a vector index correct and cheap. What it is The embedding lifecycle is the set of rules for storing vectors, deciding when to recompute them, and keeping the index in sync with the source text. The core principle: a chunk's embedding is valid exactly as long as its text is unchanged. Cache the vector keyed to the text. If the text never changes, never re-embed it. How it works The standard trick is a content hash . Hash each chunk's text; store the vector alongside that hash. On the next pass, re-hash the current text and compare: If the hash matches, the text is byte-for-byte the same, so the old vector is still correct and you skip the expensive call. If it differs, the source changed and the stored vector is now stale , recompute it. This makes updates incremental : edit t

Challenge: Incremental Re-Embed Counter