xieydd on Vchord

VectorChord 0.5: New RaBitQ-empowered DiskANN Index and Continuous Recall Measurement

Fri, 26 Sep 2025 00:00:00 +0000

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 updates: experimental support for the DiskANN graph index and a new recall measurement tool to monitor the health of your IVF+RaBitQ indexes proactively.

New Index Preview: RaBitQ-empowered DiskANN Index

From day one, VectorChord’s core indexing solution has relied on a powerful combination: the cluster-based IVF algorithm and RaBitQ quantization—what we ship as vchordrq (IVF+RaBitQ). This approach consistently delivers excellent low-latency, high-recall performance across a wide range of use cases and remains our recommended default for most scenarios.

All-in-one VectorChord Suite: Building Production-Ready RAG Solutions Directly in PostgreSQL

Tue, 29 Apr 2025 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.

Beyond Text: Unlock OCR-Free RAG in PostgreSQL with Modal & VectorChord

Tue, 15 Apr 2025 00:00:00 +0000

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 Recognition (OCR) and layout analysis. These steps can be slow, error-prone, and often lose crucial visual context like tables, figures, and formatting. But what if you could query documents based on how they look, not just the extracted text?

Vector Search Over PostgreSQL: A Comparative Analysis of Memory and Disk Solutions

Thu, 03 Apr 2025 00:00:00 +0000

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 infrastructure. But adoption isn’t seamless. Here’s what real users say:

The query takes 30 seconds for the first time and 100ms if it’s a repeat query. The problem is very slow IO.

Is it normal to take 3 hours to build the index for 12 million rows with 75 dimension vector?

After 13 hours of building index for 200 million vectors of 75 dimensions each, I have noticed a heavy memory usage at 100GB/124GB at 30% of building index, and server will eventually crash.

We’re hitting some challenges, particularly around need for frequent index updates, throughput constraints, lack of parallel index scans and no built-in quantization.

Hybrid search with Postgres Native BM25 and VectorChord

Thu, 13 Mar 2025 00:00:00 +0000

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, excel in ranking documents based on term frequency and exact keyword matches. This makes them highly effective for structured queries and keyword-heavy content. However, they struggle with understanding synonyms, contextual nuances, and semantic intent. Conversely, vector-based retrieval captures deep semantic meaning, enabling better generalization across varied queries but sometimes sacrificing precision in exact keyword matches. Hybrid Search bridges this gap by combining BM25’s precision with vector search’s contextual understanding, delivering faster, more accurate, and semantically aware results.

Introducing Terraform Support for PGVecto.rs Cloud

Wed, 24 Jul 2024 00:00:00 +0000

We are thrilled to announce the release of our Terraform provider for PGVecto.rs Cloud. This new provider is designed to simplify the process of managing resources on our cloud service, providing users with an intuitive and streamlined experience for managing their Postgres cluster setup and operation through infrastructure as a code tool — Terraform. For those unfamiliar with Terraform, it is an open-source tool for building, changing, and versioning infrastructure safely and efficiently. It enables users to define their infrastructure as code, allowing them to provision and manage resources programmatically.