Hi! This document is our plan for Qdrant development in 2024. Previous year roadmap is available here:

• Roadmap 2023
• Roadmap 2022

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.

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!

• 📃 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

• ⚙️ 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