To handle embedding model updates without reprocessing all data, you need a strategy that balances efficiency with consistency. The simplest approach is to version your embeddings and use a hybrid system that gradually transitions to the new model. For example, when deploying an updated model (e.g., moving from Sentence-BERT v1 to v2), keep the old embeddings for existing data and generate new embeddings only for incoming data. This avoids reprocessing terabytes of historical data upfront. During queries, your system can search both the old and new embedding spaces, or use a translation layer to map new embeddings to the old space temporarily. Over time, you can incrementally reprocess high-priority data (e.g., frequently accessed records) to reduce technical debt.
Another practical method involves backward-compatible training. When training the new model, add a loss term that encourages new embeddings to align with the old ones for the same data. For instance, if your original model used cosine similarity, the updated model could include a regularization term to minimize the distance between old and new embeddings for a subset of anchor points. This lets you query old and new embeddings in the same space without immediate full reprocessing. OpenAI used a similar approach when transitioning from older text-embedding models to newer ones, ensuring that embeddings for the same text didn’t drift too far. However, this requires access to the old model during training and may slightly limit the new model’s performance gains.
For large-scale systems, a phased rollout with shadow indexing works well. Deploy the new model alongside the old one, generating embeddings for all new data and storing them in a separate index. Run both systems in parallel, and compare query results between the old and new indexes to validate consistency. Once confident, reprocess historical data in batches during low-traffic periods or prioritize reprocessing based on usage patterns. For example, an e-commerce platform might update product embeddings in batches, starting with high-traffic items like seasonal products. Tools like FAISS or Annoy support adding new embeddings incrementally, reducing downtime. This approach minimizes disruption while allowing continuous operation, and it’s widely used in recommendation systems where embedding freshness impacts user experience.