Ce Gao on Vchord

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: Store 400k Vectors for $1 in PostgreSQL

Thu, 05 Dec 2024 00:00:00 +0000

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 the top 10 queries. This setup costs only $250 monthly and can be hosted on a single machine.

This means you can store 400k vectors for only $1, allowing you to save significantly: 6x more vectors compared to Pinecone (storage optimized instance) and 26x more than pgvector/pgvecto.rs for the same price.

Introducing PGVecto.rs Cloud - Scalable Vector Search in PostgreSQL, Now in the Cloud

Fri, 02 Aug 2024 00:00:00 +0000

In an exciting development, we are thrilled to announce the launch of PGVecto.rs Cloud - bringing the power of scalable vector search to your PostgreSQL databases, now available as a fully managed cloud service.

PGVecto.rs is a pioneering Postgres extension that enables high-performance vector search, empowering developers to build advanced similarity-based applications on top of their existing Postgres infrastructure. With PGVecto.rs Cloud, you can now unlock the benefits of this transformative technology without the overhead of managing the extension yourself.

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.

pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL

Tue, 19 Mar 2024 00:00:00 +0000

pgvector and pgvecto.rs are both vector search extensions designed to enhance the capabilities of PostgreSQL. These extensions revolutionize the way vector search is performed within the database, providing scalable, low-latency, and hybrid-enabled solutions. In this blog post, we will illustrate the differences between pgvector and pgvecto.rs.

TL; DR: Please navigate directly to the final section that contains a table showcasing the main distinctions between the two.

Search

Search is essential for a database. It plays a pivotal role in facilitating efficient data retrieval, enabling users to quickly find and access the information they need. We will demonstrate their performance and highlight the various features pgvecto.rs and pgvector offer.

Building LLM Applications in LangChain using pgvecto.rs

Fri, 08 Mar 2024 00:00:00 +0000

LangChain is a framework for developing applications powered by language models. It enables applications that:

Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

pgvecto.rs is a vector search extension in Postgres. In this blog post, we’ll show you how to build LLM applications with LangChain and pgvecto.rs.

Building the RAG pipeline with LlamaIndex and pgvecto.rs

Fri, 08 Mar 2024 00:00:00 +0000

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

pgvecto.rs provides a LlamaIndex integration that allows you to retrieve the most similar vectors. In this blog post, we will show you how to build a RAG pipeline with pgvecto.rs and LlamaIndex.

Install dependencies

Some dependencies are required to use the LlamaIndex integration:

pip install llama-index "pgvecto_rs[sdk]"

You could start the Postgres instance with pgvecto.rs extension in a docker container:

Do we really need a specialized vector database?

Fri, 08 Mar 2024 00:00:00 +0000

Why does LLM need vector search?

First, let me briefly introduce why LLM needs to use vector search technology. Vector search is a problem that has been around for a long time. The process of finding the most similar object in a collection given an object is vector search. Text/images, etc. can be converted into a vector representation, and the similarity problem of text/images can be transformed into a vector similarity problem.

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.