vectorsearch on Vchord

How We Made 100M Vector Indexing in 20 Minutes Possible on PostgreSQL

Wed, 03 Dec 2025 00:00:00 +0000

1. Introduction

In the past few months, we’ve heard consistent feedback from users and partners: while our goal of providing a scalable, high-performance alternative to pgvector is well-received, index build time and memory usage remain major concerns at billion-scale.

Now VectorChord can index 100 million 768-dimensional vectors in 20 minutes on a 16 vCPU machine with just 12 GB of memory. By contrast, indexing the same data with pgvector requires around 200 GB of memory and about 40 hours on a 16-core instance. And pgvector with insufficient memory often suffer from page swapping, making builds even slower.

Introducing Vechord: Turn PostgreSQL into your search engine in a Pythonic way.

Wed, 23 Apr 2025 00:00:00 +0000

Today, we’re thrilled to announce the release of Vechord, a new Python library designed to dramatically simplify building robust search infrastructure directly on top of the PostgreSQL database.

In the rapidly evolving world of AI and large language models (LLMs), Retrieval-Augmented Generation (RAG) and semantic search have become crucial components. However, setting up the necessary vector search infrastructure often involves learning new database technologies, managing complex integrations, or wrestling with intricate frameworks. This adds friction and slows down development, especially for teams already comfortable with PostgreSQL.

Vector Search at 10,000 QPS in PostgreSQL with VectorChord

Thu, 27 Feb 2025 00:00:00 +0000

VectorChord is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search, and serves as the successor to pgvecto.rs. In our previous blog post, we showed that with just $250 per month, VectorChord achieved 131 QPS with 0.95 precision on 100 million vectors—demonstrating impressive cost-effective performance for large-scale vector search.

Building on that success, this post focuses on high performance by exploring how VectorChord can reach 10,000 QPS in PostgreSQL. We’ll walk you through how PostgreSQL’s reliable relational foundation integrates with advanced vector indexing to deliver fast, scalable search capabilities, and provide a production-ready setup along with a detailed architecture guide for managing large-scale vector computations even under heavy loads.

Benefits and Steps of External Centroids Building in VectorChord

Tue, 10 Dec 2024 00:00:00 +0000

Clustering is an important component in vector search, and the k-means algorithm remains one of the most widely used techniques. However, its simplicity comes with a significant drawback: resource-intensive computation can render it impractical for large datasets. This blog explores why this challenge arises and how leveraging external builds to overcome it. We also share insights into our approach to implementing this solution.

Why we need it

VectorChord uses IVF + RaBitQ to perform vector search. The quality of the IVF cluster can heavily affect search precision and speed. K-means usually takes dozens of rounds before convergence. For each round, all the sampled vectors need to be assigned to the nearest cluster. This is the most computationally intensive part. The good news is that this process can be easily parallelized.

pgvecto.rs 0.2: Unifying Relational Queries and Vector Search in PostgreSQL

Tue, 20 Feb 2024 00:00:00 +0000

We are excited to announce the release of pgvecto.rs 0.2, a significant milestone in the journey of bridging the gap between relational queries and vector search in PostgreSQL. This update brings together the power of both worlds, offering enhanced efficiency and enabling complex queries within PostgreSQL.

In the past, developers and data scientists encountered the significant challenge of managing separate systems for relational queries and vector search. This resulted in increased complexity and resource overhead. However, with the release of pgvecto.rs 0.2, we have addressed this issue by integrating the cutting-edge VBASE method from OSDI 2023. This integration has substantially refined the efficiency of vector search within PostgreSQL.