VectorChord
supervc-stack/VectorChord
Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.
Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.
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Install
cargo add VectorChordREADME
VectorChord
Ready for the Billion-Scale Era. Host 100M vectors on a single i4i.xlarge ($247/mo) and scale seamlessly to 1B+.
[Official Site][official-site-link] · [Blog][blog-link] · [Docs][docs-link] · [Feedback][github-issues-link] · [Contact Us][email-link]
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VectorChord (vchord) is a PostgreSQL extension engineered for scalable, high-performance, and cost-effective vector search.
To efficiently store vectors while preserving search quality, VectorChord applies RaBitQ1 compression together with autonomous reranking. With VectorChord, you can store 400,000 vectors for just $1, enabling significant savings: 6x more vectors compared to Pinecone's optimized storage and 26x more than pgvector/pgvecto.rs for the same price.
![][image-compare]
Features
VectorChord introduces remarkable enhancements over pgvecto.rs and pgvector:
💰 Affordable Vector Search: Host 100M × 768-dimensional vectors → AWS i4i.xlarge ($247/month)2, host 1B × 96-dimensional vectors → i7ie.6xlarge ($2246/month)3, helping you keep infrastructure costs down while maintaining competitive search quality.
⚡ Accelerated Index Build: Index 100 million vectors in just 20 minutes. Powered by hierarchical K-means and highly optimized disk operations, VectorChord eliminates the bottleneck of vector indexing on a single machine with limited hardware resources.
📈 Smoothly Scale Up: Scale with confidence as your data grows. Through dimensionality reduction and sampling4, VectorChord effectively controls memory growth, enabling 1B-vector indexes to be built on machines with 128GB of memory in practice.
🔌 Seamless Integration: Fully compatible with pgvector data types and syntax while providing optimal defaults out of the box - no complex parameter tuning needed. Just drop in VectorChord for enhanced experience.
💾 Efficient Storage with Low-Bit Data type: Drastically reduce storage costs with our native 4-bit (RaBitQ4) and 8-bit (RaBitQ8) vector types. Achieve massive space savings without compromising search quality—RaBitQ8 maintains high precision with <1% recall loss.
Quick Start
For new users, we recommend using the Docker image to get started quickly. If you do not prefer Docker, please read [installation gui
Footnotes
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Gao, Jianyang, and Cheng Long. "RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search." Proceedings of the ACM on Management of Data 2.3 (2024): 1-27. ↩
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Please check out our blog post for more details. ↩
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Please check out our blog post for more details. ↩
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Please check out our blog post for more technique details and document for usages. ↩