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title, weight
| title | weight |
|---|---|
| Roadmap | 32 |
Qdrant 2023 Roadmap
Hi! This document is our plan for Qdrant development in 2023. Previous year roadmap is available here:
Goals of the release:
- Maintain easy upgrades - we plan to keep backward compatibility for at least one major version back.
- That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one major version.
- Storage should be compatible between any two consequent versions, so you can upgrade Qdrant with automatic data migration between consecutive versions.
- Make billion-scale serving cheap - qdrant already can serve billions of vectors, but we want to make it even more affordable.
- Easy scaling - our plan is to make it easy to dynamically scale Qdrant, so you could go from 1 to 1B vectors seamlessly.
- Various similarity search scenarios - we want to support more similarity search scenarios, e.g. sparse search, grouping requests, diverse search, etc.
Milestones
- ⚛️ Quantization support
- Scalar quantization f32 -> u8 (4x compression)
- Advanced quantization (8x and 16x compression)
- Support for binary vectors
- ⏫ Scalability
- Automatic replication factor adjustment
- Automatic shard distribution on cluster scaling
- Repartitioning support
- 👀 Search scenarios
- Diversity search - search for vectors that are different from each other
- Sparse vectors search - search for vectors with a small number of non-zero values
- Grouping requests - search within payload-defined groups
- Different scenarios for recommendation API
- Additionally
- Extend full-text filtering support
- Support for phrase queries
- Support for logical operators
- Simplify update of collection parameters
- Extend full-text filtering support