How do I effectively manage document versioning when using Chroma DB for high-volume RAG systems?

Managing updates in Chroma DB requires a strategic approach to metadata. Instead of clearing the collection, you should implement an "upsert" logic by using unique IDs for every document chunk. When a document is modified, you generate new embeddings only for the affected IDs. In a production environment I managed last year, we added a 'timestamp' or 'version_hash' to the metadata. This allowed our retrieval logic to filter for the most recent version of a vector during the query phase, ensuring that the LLM always received the most up-to-date context without the need for a full re-index.

Margaret, have you noticed if the storage size of Chroma DB grows significantly when keeping multiple versions of the same vector in the same collection?

I highly recommend using the delete method with specific where filters based on document IDs before inserting new data to keep the index clean.