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73 lines
3.6 KiB
Markdown
73 lines
3.6 KiB
Markdown
# Qdrant 2024 Roadmap
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Hi!
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This document is our plan for Qdrant development in 2024.
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Previous year roadmap is available here:
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* [Roadmap 2023](roadmap-2023.md)
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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 (this stays the same in 2024).
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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 serving easy on multi-billion scale** - Qdrant already can serve billions of vectors cheaply, using techniques as quantization. In the 2024 year, we plan to make it even easier to scale it.
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* Faster and more reliable replications
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* Out-of-the-box read-write segregation
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* Specialized nodes and multi-region deployments
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* **Better ecosystem** - in 2023 we introduced [fastembed](https://github.com/qdrant/fastembed) to simplify embedding generation but keep it out of the core.
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In 2024 we plan to continue this trend: implement more advanced and specialized tools while keeping the core focused on the main use-case.
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* Advanced support for sparse vectors - we plan to make sparse vectors inference as fast and easy as the dense one.
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* Hybrid search out of the box with no overhead - something you can build with Qdrant today, but in a more convenient way.
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* Practical RAG - battle-tested RAG practices with production-grade implementation.
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* **Various similarity search scenarios** - develop vector similarity beyond just kNN search.
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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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## Core Milestones
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* 📃 Hybrid Search and Sparse Vectors
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* [ ] Make Sparse Vectors serving as cheap and fast as Dense Vectors
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* [ ] Introduce Hybrid Search into Qdrant Client
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* [ ] Dense + Sparse + Fusion in one request
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* [ ] Customizable Re-Ranking
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---
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* 🏗️ Scalability
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* [ ] Faster shard synchronization
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* [ ] Non-blocking snapshotting
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* [ ] Incremental replication
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* [ ] Specialized nodes
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* [ ] Read-only nodes
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* [ ] Indexing nodes
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* [ ] Multi-region deployments
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* [ ] Automatic replication over availability zones
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---
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* ⚙️ Performance
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* [ ] Specialized vector indexing for edge cases HNSW is not good at
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* [ ] Text-index performance and resource consumption improvements
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* [ ] IO optimizations for disk-bound workloads
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---
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* 🏝️ New Data Exploration techniques
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* [ ] Improvements in Discovery API to support more use-cases
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* [ ] Diversity Sampling
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* [ ] Better Aggregations
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* [ ] Advanced text filtering
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* [ ] Phrase queries
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* [ ] Logical operators
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