Introducing the Embedding Adapter

Every organization that operates a vector database faces the same problem eventually: the embedding model you chose six months ago has been superseded by something better. Migrating means re-encoding every document, image, or profile in your corpus. For large-scale systems, this is a multi-day, multi-thousand-dollar operation that requires significant downtime.

The adapter approach

The Embedding Adapter learns a high-fidelity mapping between embedding spaces. Given paired samples from the old and new model, it trains a lightweight transformation that converts vectors from one space to the other.

This means you can start querying with a new embedding model immediately, without re-encoding your existing index. The adapter handles the translation in real time.

Performance characteristics

In our evaluations on MS MARCO and internal benchmarks, the adapter recovers 95-99% of native retrieval accuracy compared to a full re-index. The latency overhead is sub-millisecond per query. For most applications, this is indistinguishable from native performance.

Beyond model upgrades

The same technology enables cross-silo querying. Two organizations using different embedding models can search each other's indices without exchanging raw data. The adapter provides a shared geometric space for federated retrieval.

We published the technical details in our paper on Local Drift Adapters, which introduces per-cluster mixture-of-expert adapters that outperform global linear baselines on heterogeneous vector databases.

The Embedding Adapter is available today for enterprise customers. Reach out to discuss deployment.