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60 lines
2.9 KiB
Markdown
60 lines
2.9 KiB
Markdown
# Qdrant 2023 Roadmap
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Hi!
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This document is our plan for Qdrant development in 2023.
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Previous year roadmap is available here:
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* [Roadmap 2022](roadmap-2022.md)
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Goals of the release:
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* **Maintain easy upgrades** - we plan to keep backward compatibility for at least one minor version back.
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* That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one minor version.
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* Storage should be compatible between any two consequent versions, so you can upgrade Qdrant with automatic data migration between consecutive versions.
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* **Make billion-scale serving cheap** - qdrant already can serve billions of vectors, but we want to make it even more affordable.
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* **Easy scaling** - our plan is to make it easy to dynamically scale Qdrant, so you could go from 1 to 1B vectors seamlessly.
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* **Various similarity search scenarios** - we want to support more similarity search scenarios, e.g. sparse search, grouping requests, diverse search, etc.
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## How to contribute
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If you are a Qdrant user - Data Scientist, ML Engineer, or MLOps, the best contribution would be the feedback on your experience with Qdrant.
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Let us know whenever you have a problem, face an unexpected behavior, or see a lack of documentation.
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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).
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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.
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For those familiar with Rust - check out our [contribution guide](../CONTRIBUTING.md).
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If you have problems with code or architecture understanding - reach us at any time.
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Feeling confident and want to contribute more? - Come to [work with us](https://qdrant.join.com/)!
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## Milestones
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* :atom_symbol: Quantization support
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* [x] Scalar quantization f32 -> u8 (4x compression)
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* [x] Product quantization (4x, 8x, 16x, 32x, and 64x compression)
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* [x] Binary quantization (32x compression, 40x speedup)
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* [x] Support for binary vectors
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---
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* :arrow_double_up: Scalability
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* [ ] Automatic replication factor adjustment
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* [ ] Automatic shard distribution on cluster scaling
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* [x] Repartitioning support
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---
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* :eyes: Search scenarios
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* [ ] Diversity search - search for vectors that are different from each other
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* [x] Discovery search - constrain the space in which the search is performed
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* [x] Sparse vectors search - search for vectors with a small number of non-zero values
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* [x] Grouping requests - search within payload-defined groups
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* [x] Different scenarios for recommendation API
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---
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* Additionally
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* [ ] Extend full-text filtering support
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* [ ] Support for phrase queries
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* [ ] Support for logical operators
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* [x] Simplify update of collection parameters
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