CourseRAG · Module 4: Embeddings and Vector Representations · part 21 of 82
Part 21 · Module 4: Embeddings and Vector Representations

Topic 4: Operating Embeddings

14 min read·21 Sept 2026

4.1 Batching, Concurrency, and Rate Limits at Index Time

Intuition: embedding a corpus is like mailing 100,000 letters. Send them in bundles (batching), use several post offices at once (concurrency), respect each office's opening hours (rate limits), and try again when a queue is full (retries).

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