aintaining a vector database in long-running LangGraph systems requires active lifecycle management. Over time, collections accumulate outdated embeddings, schema changes, or version drift from evolving embedding models. A disciplined maintenance routine prevents performance degradation and semantic noise.
Start by versioning embeddings—store the model name, timestamp, and schema in metadata. When upgrading embedding models, create a new collection and gradually migrate data rather than overwriting. Periodically remove stale or low-usage vectors through background jobs to keep index size manageable. Milvus automates segment compaction and index rebuilding, but monitoring metrics such as recall, latency, and memory use ensures early detection of imbalance.
Backup strategy is equally important. In Zilliz Cloud, snapshots and replication provide durability across regions. For self-managed Milvus clusters, schedule incremental backups and verify recovery regularly. Combining good hygiene with observability dashboards ensures LangGraph agents always query a healthy, up-to-date retrieval layer.