Versioning indexed documents and vectors requires a systematic approach to track changes, ensure consistency, and support rollbacks. The core idea is to maintain a clear link between documents, their vector representations, and the specific versions of both. This helps avoid data corruption, ensures reproducibility in machine learning applications, and simplifies debugging when discrepancies arise.
First, assign unique identifiers to documents and their corresponding vectors, and include version metadata explicitly. For example, when a document is updated, generate a new version number (e.g., doc_v2) and store it alongside the previous version. Similarly, if vectors are generated using a machine learning model, track the model version used to create them (e.g., sentence-transformers_v3). This ensures that queries against vectors match the correct document versions. Tools like Elasticsearch or FAISS can be configured to index documents and vectors with versioned IDs or metadata fields. For instance, a document stored in Elasticsearch might have a version field, and its vector in a separate database could reference that same version to maintain alignment.
Second, separate storage for different versions prevents accidental overwrites. Use time-stamped directories, versioned database partitions, or dedicated indexes for major changes. For example, when updating a search index, create a new index (e.g., products_2024-06) instead of modifying the existing one. This allows atomic swaps once the new version is validated. For vectors, store each model version’s outputs in distinct namespaces—like a Pinecone index per model version—to avoid mixing embeddings from incompatible models. If a document is revised, regenerate its vector using the latest model and store both the old and new vectors with version tags. This ensures backward compatibility for applications still relying on older data.
Finally, automate version tracking and testing. Implement CI/CD pipelines to handle reindexing and vector regeneration when documents or models change. For example, a script could detect updates to a document repository, generate new vectors with the current model, and deploy them to a staging index. Automated tests can verify that search results remain consistent across versions or flag breaking changes. Tools like DVC (Data Version Control) or MLflow can version datasets and models, while database features like snapshots or point-in-time recovery enable rollbacks. Regularly clean up obsolete versions to reduce storage costs, but retain critical versions for auditing. For instance, keep nightly snapshots for a week and monthly snapshots for a year, aligned with your compliance needs.