vectorchord

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, …

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 …

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 …

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. …

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 …

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 …

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, …

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 …

We’re excited to share something special with you: VectorChord-BM25, a new extension designed to make PostgreSQL’s full-text search even better. Whether you’re building a small app or managing a large-scale system, this tool brings advanced BM25 …

Today, we’re thrilled to share VectorChord 0.2—a release shaped by community feedback and designed to tackle the evolving challenges of vector search in PostgreSQL. %[https://github.com/tensorchord/VectorChord/] Optimized Storage Layout: Supporting …

ARM64 is quietly becoming a go-to for cost-effective cloud servers, especially on AWS. With ARM-based processors like the Graviton series (Graviton3 and Graviton4), you can save a good chunk of money without sacrificing performance. These ARM64 …

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 …

We’re pleased to announce our new vector search extension for PostgreSQL, providing a highly cost-effective way to manage large vectors. Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768-dimensional vectors for …