# Automated Embedding (autoEmbed) quantization types including binaryNoRescore > Source-anchored research for Automated Embedding (autoEmbed) quantization types including binaryNoRescore. Run status: COMPLETED. Parent: [Atlas Vector Search automatic quantization and rescoring](https://llms-explorer.com/tree/atlas-vector-search-automatic-quantization-and-rescoring/) · 2 facets · 8 facts · page: https://llms-explorer.com/tree/automated-embedding-autoembed-quantization-types-including-binarynorescore/ ## Comparisons and alternatives - Independent background evidence: the Hugging Face/mixedbread quantization experiment reports roughly 92.5% retained retrieval performance without binary rescoring versus about 96% with rescoring. It uses float-query/binary-document rescoring, so these figures do not benchmark Atlas binaryNoRescore or its full-precision rescoring alternative. (low confidence) — [source](https://huggingface.co/blog/embedding-quantization) ## Facts and statements - The autoEmbed quantization field accepts float, scalar, binary, and binaryNoRescore; scalar is the default. (high confidence) (verified as of 2026-10-23) — [source](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md) - For autoEmbed, float stores 32-bit values. Scalar uses model-provided int8 values and approximately one quarter of float embedding storage and RAM. (high confidence) (verified as of 2026-10-23) — [source](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md) - For autoEmbed, binary indexes one-bit values but retains full-precision vectors for candidate rescoring. MongoDB states RAM use is about 1/24 of float; this is not a corresponding total disk-storage reduction. (high confidence) (verified as of 2026-10-23) — [source](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md) - binaryNoRescore uses model-provided one-bit vectors without rescoring. MongoDB describes faster queries and lower storage cost than binary, with lower accuracy; it gives no universal latency or recall improvement. (high confidence) (verified as of 2026-10-23) — [source](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md) - MongoDB server documentation lists default similarity as dotProduct for float, cosine for scalar, and euclidean for binary and binaryNoRescore. (high confidence) (verified as of 2026-10-23) — [source](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md) - Mongoid auto_embed_field accepts quantization and passes it into the autoEmbed index field specification. DRIVERS-3389 tracks the builder/type support required to expose these index options through language drivers. (medium confidence) (verified as of 2026-10-24) — [source](https://github.com/mongodb/mongoid/blob/master/lib/mongoid/fields.rb) - Scope boundary: Percona documents a self-managed autoEmbed pipeline whose Ollama model catalog sets quantization to float. This is evidence of a separate implementation context, not verification of Atlas binaryNoRescore support or performance. (low confidence) (verified as of 2026-10-01) — [source](https://docs.percona.com/percona-search-for-mongodb/configure-automatic-embedding-ollama.html)