Skip to content

feat(deps): update dependency tensorchord/vectorchord (0.3.0 → 0.4.2) #87

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 19, 2025

Conversation

renovate[bot]
Copy link
Contributor

@renovate renovate bot commented Jun 19, 2025

This PR contains the following updates:

Package Update Change
tensorchord/VectorChord minor 0.3.0 -> 0.4.2

Release Notes

tensorchord/VectorChord (tensorchord/VectorChord)

v0.4.2

Compare Source

VectorChord 0.4.2 Release Notes

  • fix compilation on aarch64 macos
  • add support for pgxnclient: you can install VectorChord with pgxnclient install vchord==0.4.2 now

Full Changelog: tensorchord/VectorChord@0.4.1...0.4.2

v0.4.1

Compare Source

VectorChord 0.4.1 Release Notes

  • Fix potential precision issue if the dimension of vectors is 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768.

Full Changelog: tensorchord/VectorChord@0.4.0...0.4.1

v0.4.0

Compare Source

VectorChord 0.4 Release Notes


Major Improvements
  • Streaming I/O & Page Prefetch

    • Complete rewrite of page layout to enable pipelined computation with streaming I/O.
    • On PostgreSQL 17, uses fadvise to prefetch buffers into the OS page cache, eliminating per-buffer read waits and fully leveraging disk throughput.
    • In upcoming PostgreSQL 18, direct support for io_uring will further streamline asynchronous I/O.
    • Benchmarks: 2–3× lower latency on cold queries (no buffer or page cache), translating to significantly improved tail latency in production.
  • Prefilter Acceleration

    • Introduces true prefilter support for vector + filter queries.
    • Previous postfilter approach ranked full result sets and then applied filters—inefficient when selectivity is low (e.g., 1% filter rate).
    • Applies SQL filters before full precision vector distance computations, avoiding unnecessary work.
    • Benchmarks: Up to 3× faster end-to-end search on highly selective filters without any additional tuning.

Other Improvements
  • Optimized Residual Quantization

    • Collaboration with RaBitQ author Jianyang: refactored distance term $|⟨o, q–c⟩|$ into $⟨o, q⟩ – ⟨o, c⟩$, so the query vector is quantized only once.
    • Result: ~20% QPS improvement over 0.3.
    • Recommendation: Enable residual quantization for L2 workloads.
  • Fast Walsh-Hadamard Transform for Rotation

    • Collaboration with RaBitQ author Jianyang: replaced manual vchordrq.prewarm_dim GUC with an on-the-fly Fast Walsh-Hadamard Transform.
    • Removes the need to configure a prewarmed dimension list and yields marginal speed gains during setup.

Thank you for using VectorChord! As always, we welcome feedback and contributions on GitHub.

Full Changelog: tensorchord/VectorChord@0.3.0...0.4.0


Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.


  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot added the type/minor label Jun 19, 2025
@ahinko ahinko merged commit 7190248 into main Jun 19, 2025
5 checks passed
@ahinko ahinko deleted the renovate/tensorchord-vectorchord-0.x branch June 19, 2025 12:58
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

Successfully merging this pull request may close these issues.

1 participant