Getting Started on Vchord

Overview

Mon, 01 Jan 0001 00:00:00 +0000

vchord is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search.

With vchord, 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 price¹. For further insights, check out our launch blog post.

Features

vchord introduces remarkable enhancements over pgvecto.rs and pgvector:

⚡ Enhanced Performance: Delivering optimized operations with up to 5x faster queries, 16x higher insert throughput, and 16x quicker¹ index building compared to pgvector’s HNSW implementation.

Installation

Mon, 01 Jan 0001 00:00:00 +0000

VectorChord is tested on the following operating system:

Ubuntu (x86_64, aarch64)
MacOS (x86_64, aarch64)
Windows (x86_64)
Alpine Linux (x86_64, aarch64) ¹
Debian (powerpc64le, s390x, riscv64) ²

Please report a bug if you encounter issues on any of the above operating systems, or submit a feature request for additional platform support.

Docker

VectorChord Image

The easiest way to try VectorChord is to run it from a ready-to use Docker image.

Vchord Suite

Mon, 01 Jan 0001 00:00:00 +0000

Retrieval-Augmented Generation (RAG) is revolutionizing our interaction with vast datasets and language models. RAG systems offer more precise, contextually aware, and current answers by retrieving pertinent information before generating responses. Nonetheless, constructing robust, scalable, and efficient RAG pipelines poses considerable challenges, particularly for production use.

Many solutions feature complex architectures, using distinct databases for vector search, keyword search, and primary data storage. This commonly results in data synchronization challenges, greater infrastructure complexity, and higher operational costs.