Handling document updates and deletions in a vector store requires a clear strategy because vector stores are optimized for similarity searches, not transactional operations. When a document is updated or deleted, you need to ensure the corresponding vector embeddings and metadata stay in sync. For updates, this typically involves regenerating the vector embedding for the modified document and replacing the old entry. For deletions, you remove the vector and associated data from the store. Most vector databases or libraries (like FAISS or Pinecone) provide APIs for these operations, but implementation details depend on the tool and how your data is organized.
To update a document, first retrieve the existing entry using its unique identifier or metadata. Regenerate the vector embedding for the revised document using the same embedding model as before. Then, replace the old vector and metadata in the store. For example, in a system using PostgreSQL with the pgvector extension, you might execute an UPDATE query to overwrite the embedding column for a specific row. If the vector store doesn’t support in-place updates (common in append-only systems), you may need to delete the old entry and insert a new one. For deletions, use the document’s unique ID to remove the vector and metadata. In cloud-based services like Pinecone, this is done via a delete() method that accepts a list of IDs. Metadata filters (e.g., source=doc123) can also help target entries for deletion if IDs aren’t available.
Key challenges include ensuring consistency and performance. For example, frequent updates can fragment the index in some vector databases, degrading search efficiency. Batch processing updates during off-peak times might mitigate this. Versioning is another consideration: if your application needs historical data, you might insert new vectors for updated documents instead of overwriting them, tagging versions in metadata. Storage costs can balloon if old vectors aren’t cleaned up. Tools like Weaviate handle this with built-in versioning and retention policies, while simpler systems (e.g., FAISS) require manual management. Always test update/delete workflows under load to avoid surprises—vector stores prioritize read operations, so write-heavy workloads may need tuning.