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MUVERA Multi‑Vector Retrieval: In‑Depth Guide to the Fixed‑Dimensional Encoding (FDE) Python Implementation In modern large‑scale search systems, documents are often represented by multiple vectors (hundreds per document) to capture fine‑grained semantics and boost accuracy. However, matching each query against every vector becomes prohibitively slow at scale. MUVERA (Multi‑Vector Retrieval via Fixed‑Dimensional Encodings) introduces Fixed‑Dimensional Encoding (FDE): a technique that compresses a set of vectors into a single high‑dimensional embedding, preserving original similarity relationships. This article walks you through FDE’s core ideas, configuration, helper functions, algorithmic flow, Python API, performance characteristics, and practical examples—everything you need to run FDE end to …
