To efficiently update a search index with new embeddings without reprocessing everything, use a vector database that supports incremental updates and partition your data strategically. Many vector databases like FAISS, Annoy, or HNSW-based systems allow adding new vectors directly to the existing index. For example, FAISS provides an add method to append new embeddings, avoiding the need to rebuild the entire index. This works when the embedding model remains unchanged, ensuring new data is compatible with existing vectors. Partitioning data by time (e.g., daily chunks) and maintaining separate indexes for each partition can also reduce computational overhead. During queries, search across all partitions or merge them periodically for efficiency.
Another key practice is leveraging metadata and versioning. Attach timestamps or version tags to embeddings, enabling filtered searches (e.g., querying only the latest data). If updates require model changes (e.g., retraining), versioned indexes allow phased transitions. For instance, maintain a “live” index for current data and a separate index for older entries, updating the live index daily. Distributed systems like Elasticsearch or Vespa can further streamline this by scaling horizontally—adding nodes to handle new data while isolating updates to specific shards. This minimizes downtime and ensures consistent performance as the dataset grows.
Finally, implement automated validation and optimization. After appending new data, run sanity checks to confirm search accuracy (e.g., verifying known similar items still rank correctly). Schedule periodic index optimization (e.g., FAISS’s reconstruct or merge operations) during low-traffic periods to maintain search speed. For deletions or updates, use soft deletes (marking records as inactive) or maintain a lookup table to exclude obsolete entries during searches. Tools like Milvus or Pinecone offer built-in support for these workflows, simplifying maintenance. By combining incremental updates, metadata filtering, and automated checks, you can keep the index current without full reprocessing.