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| # Qdrant 2023 Roadmap | |
| Hi! | |
| This document is our plan for Qdrant development in 2023. | |
| Previous year roadmap is available here: | |
| * [Roadmap 2022](roadmap-2022.md) | |
| Goals of the release: | |
| * **Maintain easy upgrades** - we plan to keep backward compatibility for at least one minor version back. | |
| * That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one minor 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. | |
| ## How to contribute | |
| If you are a Qdrant user - Data Scientist, ML Engineer, or MLOps, the best contribution would be the feedback on your experience with Qdrant. | |
| Let us know whenever you have a problem, face an unexpected behavior, or see a lack of documentation. | |
| You can do it in any convenient way - create an [issue](https://github.com/qdrant/qdrant/issues), start a [discussion](https://github.com/qdrant/qdrant/discussions), or drop up a [message](https://discord.gg/tdtYvXjC4h). | |
| If you use Qdrant or Metric Learning in your projects, we'd love to hear your story! Feel free to share articles and demos in our community. | |
| For those familiar with Rust - check out our [contribution guide](../CONTRIBUTING.md). | |
| If you have problems with code or architecture understanding - reach us at any time. | |
| Feeling confident and want to contribute more? - Come to [work with us](https://qdrant.join.com/)! | |
| ## Milestones | |
| * :atom_symbol: Quantization support | |
| * [x] Scalar quantization f32 -> u8 (4x compression) | |
| * [x] Product quantization (4x, 8x, 16x, 32x, and 64x compression) | |
| * [x] Binary quantization (32x compression, 40x speedup) | |
| * [x] Support for binary vectors | |
| --- | |
| * :arrow_double_up: Scalability | |
| * [ ] Automatic replication factor adjustment | |
| * [ ] Automatic shard distribution on cluster scaling | |
| * [x] Repartitioning support | |
| --- | |
| * :eyes: Search scenarios | |
| * [ ] Diversity search - search for vectors that are different from each other | |
| * [x] Discovery search - constrain the space in which the search is performed | |
| * [x] Sparse vectors search - search for vectors with a small number of non-zero values | |
| * [x] Grouping requests - search within payload-defined groups | |
| * [x] Different scenarios for recommendation API | |
| --- | |
| * Additionally | |
| * [ ] Extend full-text filtering support | |
| * [ ] Support for phrase queries | |
| * [ ] Support for logical operators | |
| * [x] Simplify update of collection parameters | |