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Qdrant 2024 Roadmap
Hi! This document is our plan for Qdrant development in 2024. Previous year roadmap is available here:
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 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, start a discussion, or drop up a message. 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. 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!
Core Milestones
- 📃 Hybrid Search and Sparse Vectors
- Make Sparse Vectors serving as cheap and fast as Dense Vectors
- Introduce Hybrid Search into Qdrant Client
- Dense + Sparse + Fusion in one request
- Customizable Re-Ranking
- 🏗️ Scalability
- Faster shard synchronization
- Non-blocking snapshotting
- Incremental replication
- Specialized nodes
- Read-only nodes
- Indexing nodes
- Multi-region deployments
- Automatic replication over availability zones
- Faster shard synchronization
- ⚙️ 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