Versioning embeddings in a production system requires a structured approach to track changes, ensure reproducibility, and maintain compatibility. The core idea is to treat embeddings as data artifacts tied to specific models or processing steps. Each version should include metadata that identifies the source model, parameters, and data used to generate the embeddings. For example, if you update a model from BERT-base to BERT-large, the new embeddings should be stored separately with a version tag like v2_model-bert-large-2023-09. This avoids overwriting existing embeddings and allows systems to reference specific versions during inference or analysis.
A practical implementation involves storing embeddings in a database or object storage with version identifiers. Each entry could include fields like embedding_id, model_version, creation_date, and a hash of the input data. When retrieving embeddings, applications can specify the version they need, ensuring consistency. For instance, a recommendation system might use v1 embeddings for users who haven’t opted into a new feature, while v2 serves others. Versioning also simplifies rollbacks—if a new model produces unstable results, reverting to v1 is as simple as updating a configuration file to point to the previous version. Tools like MLflow or custom metadata tables can help track these relationships programmatically.
Another key consideration is backward compatibility and dependency management. If downstream systems (e.g., classifiers or search engines) rely on embedding dimensions or normalization steps, version changes must not break these integrations. For example, switching from 512-dimensional embeddings to 768-dimensional ones without warning could crash services expecting the original size. To mitigate this, run compatibility checks during deployment: validate that new embeddings work with existing consumers in a staging environment before promoting them. Additionally, maintain a deprecation policy—announce version changes in advance and support older versions for a defined period. This balance between innovation and stability ensures that embedding updates enhance the system without introducing unexpected failures.