pgvectorscale-diskann
OfficialScale vector search beyond RAM limits.
AuthorMercurium-Analytics
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Large-scale vector search demands more RAM than is affordable, making naive HNSW impractical for 100M+ vectors. DiskANN with pgvectorscale enables memory-efficient indexing and query processing on commodity hardware.
Core Features & Use Cases
- DiskANN-based index for large embeddings with streaming access.
- SBQ-based vector compression to shrink index memory footprint with minimal recall loss.
- Suitable for RAG pipelines, knowledge bases, and enterprise document corpora requiring scalable vector search.
Quick Start
Create a StreamingDiskANN index for your embeddings (vector(768)) using SBQ storage and run a sample top-10 nearest-neighbor query.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: pgvectorscale-diskann Download link: https://github.com/Mercurium-Analytics/pg-search-vector/archive/main.zip#pgvectorscale-diskann Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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