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