VectorChord Engineering Blog & Releases

We’re excited to announce the release of VectorChord 1.1 as we kick off the new year of horse. VectorChord 1.0 was a milestone for Postgres-native vector search: it made large-scale indexing fast enough to feel like iteration, not an outage. In 1.1, …

For teams trying to run vector search at billion scale themselves, the challenge is often not raw performance, but practicality. Many solutions designed for billion-scale, low-latency vector search come with practical constraints, requiring tradeoffs …

Two years ago, when we published the very first pgvecto.rs blog post, we made a bet: Postgres is the best place to do vector search. Since then we’ve been iterating on that bet — from VBASE with filtered vector search, to longer vector support, to …

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 …

We’re thrilled to announce the release of VectorChord 0.5! This release marks a significant step forward in our mission to provide more flexible, powerful, and controllable vector search capabilities. In the 0.5 release, we’re introducing two major …

Modern applications rely on PostgreSQL for its fully ACID‑compliant, expressive SQL, and rich ecosystem of extensions. The database handles relational workloads exceptionally well, but many projects also need to search for large text …

We’re excited to announce the release of VectorChord 0.4, a significant update that enhances high-performance vector search within PostgreSQL. We believe this release pushes the boundaries of what’s possible for vector search in PostgreSQL, …

In this article, we will describe one method for uploading, indexing, and searching a big dataset of vector data in a cost-efficient manner using the real-world LAION-400M dataset and the VectorChord extension in PostgreSQL. We’re attempting to show …

In the standard approach to Retrieval Augmented Generation (RAG), a common practice is to break down large documents into smaller, more manageable chunks. This is primarily done to improve the efficiency of the retrieval process, leveraging the …

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. …

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 …

Building effective Retrieval-Augmented Generation (RAG) systems for documents often feels like wrestling with messy, complex pipelines. Especially when dealing with PDFs or scanned images, traditional methods rely heavily on Optical Character …

We’re thrilled to announce the release of VectorChord 0.3, a major milestone that significantly boosts the performance and applicability of advanced vector search techniques directly within your Postgres database! Building on our 0.2 release, which …

Monitoring the health of our planet is one of the most critical challenges of our time. Our planet faces immense environmental pressures, and understanding these changes requires sifting through an unimaginable amount of satellite data – petabytes of …

Introduction: real-world challenges Specialized vector databases aren’t always needed. With PostgreSQL extensions like pgvector, pgvectorscale, VectorChord (which evolved from pgvecto.rs)****, you get vector search plus relational power—no extra …

We’re excited to announce the release of VectorChord-BM25 version 0.2, our PostgreSQL extension designed to bring advanced BM25-based full-text search ranking capabilities directly into your database! VectorChord-BM25 allows you to leverage the power …

You might have come across discussions or blog posts suggesting that PostgreSQL’s built-in full-text search (FTS) struggles with performance compared to dedicated search engines or specialized extensions. A notable recent example comes from Neon’s …

In the era of RAG (Retrieval-Augmented Generation), efficiently retrieving relevant data from vast datasets is crucial for businesses and developers. Traditional keyword-based retrieval methods, such as those utilizing BM25 as a scoring mechanism, …