postgresql 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.

Bringing Search‑Engine Ranking to PostgreSQL with VectorChord‑BM25

Tue, 12 Aug 2025 00:00:00 +0000

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 collections—product descriptions, support tickets, documentation—and present the most relevant rows first. PostgreSQL’s native tools offer a foundation for this, yet their default ranking logic can leave ideal matches buried under less useful results. VectorChord‑BM25 changes that equation by introducing the BM25 relevance‑scoring algorithm directly into PostgreSQL.

VectorChord: Cost-Efficient Upload & Search of 400 Million Vectors on AWS

Fri, 30 May 2025 00:00:00 +0000

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 you what kind of hardware setup you need to allow you to search through this vast dataset using VectorChord. We want to make sure that you have a good experience with a search that is both accurate and fast.

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.

3 Billion Vectors in PostgreSQL to Protect the Earth

Tue, 08 Apr 2025 00:00:00 +0000

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 pixels captured constantly. From tracking deforestation in the Amazon to identifying illegal mining operations in remote regions or monitoring agricultural impacts on sensitive ecosystems, turning this data into actionable intelligence is paramount.

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.

VectorChord-BM25: Revolutionize PostgreSQL Search with BM25 Ranking — 3x Faster Than ElasticSearch

Mon, 24 Feb 2025 00:00:00 +0000

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 scoring and ranking right into PostgreSQL, making your searches smarter and faster.

What’s New?

BM25 Scoring & Ranking: Get more precise and relevant search results with BM25, helping you find what matters most.
Optimized Indexing: Thanks to the Block WeakAnd algorithm, searches are quicker and more efficient, even with large datasets.
Enhanced Tokenization: Improved stemming and stop word handling mean better accuracy and smoother performance.

Do we really need a specialized vector database? (2023)

Tue, 18 Feb 2025 00:00:00 +0000

Notice: This article, originally published in 2023, discusses the relevance and necessity of specialized vector databases, particularly in relation to Postgres and its capabilities. For further insights and discussions on this topic, please refer to the following link: Do we really need a specialized vector database? | Hacker News.

With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector database can act as a cheap but highly effective “external brain” for your LLM. But do we really need a specialized vector database?

VectorChord 0.2: 60K-Dimensional Vectors, 50% Faster Indexing with ARM

Mon, 17 Feb 2025 00:00:00 +0000

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 Up to 60,000 Dimensions

In PostgreSQL, the foundational storage unit is an 8KB page. While this design is robust for many workloads, it introduces constraints for modern use cases like high-dimensional vector search. For instance, pgvector faces a hard limit of 2000 dimensions per vector due to this fixed page size, as larger vectors cannot be indexed within a single page.

ARM + VectorChord: Affordable, Fast Vector Search

Tue, 21 Jan 2025 00:00:00 +0000

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 instances are way more affordable than traditional x86 ones, making them a solid choice for balancing cost and performance in the cloud.

That said, ARM’s architecture differs from x86, so software that isn’t optimized for it might run slower due to emulation. This is where VectorChord steps in—a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search. As the successor to pgvecto.rs, VectorChord lets you store a whopping 400,000 vectors for just $1, delivering massive savings: 6x more vectors than Pinecone’s optimized storage and 26x more than pgvector / pgvecto.rs for the same price.

Why HNSW is Not the Answer

Wed, 18 Dec 2024 00:00:00 +0000

HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to efficiently navigate vector embeddings make it particularly appealing. However, despite its apparent advantages, HNSW may not be the optimal solution for large-scale and dynamic vector similarity search. In this blog post, we challenge the dominance of HNSW and explore why disk-based alternatives, such as IVF (Inverted File Index), might be more practical for massive datasets.

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.

My binary vector search is better than your FP32 vectors

Fri, 22 Mar 2024 00:00:00 +0000

Within the field of vector search, an intriguing development has arisen: binary vector search. This approach shows promise in tackling the long-standing issue of memory consumption by achieving a remarkable 30x reduction. However, a critical aspect that sparks debate is its effect on accuracy.

We believe that using binary vector search, along with specific optimization techniques, can maintain similar accuracy. To provide clarity on this subject, we showcase a series of experiments that will demonstrate the effects and implications of this approach.