Versioning vectors or models in AI databases is handled by combining unique identifiers, metadata tracking, and storage strategies to manage changes over time. Each version of a model or its associated vectors is assigned a distinct identifier (like a UUID, commit hash, or semantic version tag) to differentiate iterations. Metadata, such as training data sources, hyperparameters, or performance metrics, is stored alongside the vectors or model weights to document changes. For example, a vector database might store embeddings under a version tag like text-embedding-v3, while a model registry could link a model checkpoint to a Git commit hash. This ensures that developers can trace which data or code produced a specific version.
A common technical approach is to isolate versions using namespaces, collections, or partitions within the database. For instance, a vector database like Pinecone or Milvus allows creating separate indexes or collections for different model versions. If a model is retrained, new vectors are stored in a new index named product_embeddings_v2, while the older v1 remains accessible for rollback or comparison. Similarly, model registries like MLflow or Hugging Face’s Model Hub use versioned artifacts, where uploading a new model increments a version number (e.g., resnet50:v4). Metadata APIs often enable querying by version, such as fetching all vectors generated by a specific model iteration or filtering models by training date.
Integration with workflows is critical. CI/CD pipelines can automate versioning by triggering model registration or vector re-indexing when code changes merge into a main branch. For example, a GitHub Action could run after training to upload a new model version to a registry and update a reference in the application’s configuration. Data versioning tools like DVC or LakeFS can link vector datasets to specific code or model versions, ensuring reproducibility. Rollback mechanisms are also essential: if a model’s performance degrades in production, switching back to a previous version becomes straightforward by pointing the application to the older index or model artifact. This structured approach reduces errors and maintains consistency across development, testing, and deployment stages.