I can suggest several improvements for Qdrant. The first one is better native embedding and re-ranking support. Qdrant currently functions as a storage plus retrieval engine, not as a reasoning system, so built-in embedding generation or built-in re-ranker models, or a smarter query understanding layer could reduce dependency on external machine learning services. Additionally, fast large-scale filtering operations could be implemented, such as automatic index suggestions, adaptive query planning, and smart indexing of metadata fields, which would make Qdrant even more efficient. Another observed limitation is a metadata filtering strategy where users can filter by user ID, document type, time, or domain, which reduces noise significantly. Another needed improvement is deduplication plus versioning, as it can enhance quality by removing duplicate chunks and versioning embeddings when documents update. From a production perspective, addressing retrieval latency and embedding drift by observing failed queries would be important, allowing Qdrant to fit OpenTelemetry perfectly as well. One of the key limitations is that Qdrant does not have built-in role-based access control, and while being self-hosted is a benefit, it can also be improved. There is also no automatic governance policy, so I must design my own access rules, retention rules, or versioning. Additionally, there is no native encryption of payload fields to manage at the infrastructure or app level. It would be a perfect 10 if Qdrant could reduce noise, especially by eliminating duplicate chunks. Another thing would be having built-in re-ranker models. If these improvements were made, I would give it a 10.
