---
title: "pgvecto.rs"
type: "tool"
slug: "tensorchord-pgvecto-rs"
canonical_url: "https://www.graphcanon.com/tools/tensorchord-pgvecto-rs"
github_url: "https://github.com/tensorchord/pgvecto.rs"
homepage_url: "https://docs.vectorchord.ai/getting-started/overview.html"
stars: 2177
forks: 84
primary_language: "Rust"
license: "Apache-2.0"
categories: ["vector-databases", "data-retrieval"]
tags: ["postgres", "llm", "hacktoberfest", "rust", "chatgpt", "gpt", "nearest-neighbor-search", "faiss"]
updated_at: "2026-07-07T18:45:20.228066+00:00"
---
# pgvecto.rs
> Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres
pgvecto.rs is a Rust-based PostgreSQL extension that provides scalable vector search capabilities with support for high-dimensional data, advanced filtering techniques, and multiple data types.
## Facts
- Repository: https://github.com/tensorchord/pgvecto.rs
- Homepage: https://docs.vectorchord.ai/getting-started/overview.html
- Stars: 2,177 ยท Forks: 84 ยท Open issues: 76 ยท Watchers: 20
- Primary language: Rust
- License: Apache-2.0
- Last pushed: 2025-02-26T14:11:43+00:00
## Categories
- [Vector Databases](/categories/vector-databases.md)
- [Data & Retrieval](/categories/data-retrieval.md)
## Tags
postgres, llm, hacktoberfest, rust, chatgpt, gpt, nearest-neighbor-search, faiss
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## README (excerpt)
```text
pgvecto.rs
`pgvecto.rs` is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on [pgrx](https://github.com/pgcentralfoundation/pgrx).
> [!NOTE]
> We have a new implementation [VectorChord](https://github.com/tensorchord/VectorChord/) with better stability and performance. Users are encouraged to migrate to VectorChord. Check out the [migration guide](https://docs.vectorchord.ai/vectorchord/admin/migration.html).
## Comparison with pgvector
Checkout [pgvecto.rs vs pgvector](https://docs.vectorchord.ai/faqs/comparison-pgvector.html) for more details.
| Feature | pgvecto.rs | pgvector |
| --- | --- | --- |
| Filtering | Introduces VBASE method for vector search and relational query (e.g. Single-Vector TopK + Filter + Join). | When filters are applied, the results may be incomplete. For example, if you originally intended to limit the results to 10, you might end up with only 5 results with filters. |
| Vector Dimensions | Supports up to 65535 dimensions. | Supports up to 2000 dimensions. |
| SIMD | SIMD instructions are dynamically dispatched at runtime to maximize performance based on the capabilities of the specific machine. | Added CPU dispatching for distance functions on Linux x86-64" in 0.7.0. |
| Data Types | Introduces additional data types: binary vectors, FP16 (16-bit floating point), and INT8 (8-bit integer). | \- |
| Indexing | Handles the storage and memory of indexes separately from PostgreSQL | Relies on the native storage engine of PostgreSQL |
| WAL Support | Provides Write-Ahead Logging (WAL) support for data, index support is working in progress. | Provides Write-Ahead Logging (WAL) support for index and data. | |
## [Documentation](https://docs.vectorchord.ai/getting-started/overview.html)
- Getting Started
- [Overview](https://docs.vectorchord.ai/getting-started/overview.html)
- [Installation](https://docs.vectorchord.ai/getting-started/installation.html)
- Usage
- [Indexing](https://docs.vectorchord.ai/usage/indexing.html)
- [Search](https://docs.vectorchord.ai/usage/search.html)
- Administration
- [Configuration](https://docs.vectorchord.ai/admin/configuration.html)
- [Upgrading from older versions](https://docs.vectorchord.ai/admin/upgrading.html)
- Developers
- [Development Tutorial](https://docs.vectorchord.ai/developers/development.html)
## Quick start
For new users, we recommend using the [Docker image](https://github.com/tensorchord/pgvecto.rs/pkgs/container/pgvecto-rs) to get started quickly.
```sh
docker run \
--name pgvecto-rs-demo \
-e POSTGRES_PASSWORD=mysecretpassword \
-p 5432:5432 \
-d ghcr.io/tensorchord/pgvecto-rs:pg17-v0.4.0
```
Then you can connect to the database using the `psql` command line tool. The default username is `postgres`, and the default password is `mysecretpassword`.
```sh
psql -h localhost -p 5432 -U postgres
```
Run the following SQL to ensure the extension is enabled.
```sql
DROP EXTENSION IF EXISTS vectors;
CREATE EXTENSION vectors;
```
pgvecto.rs introduces a new data type `vector(n)` denoting an n-dimensional vector. The `n` within the brackets signifies the dimensions of the vector.
You could create a table with the following SQL.
```sql
-- create table
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/tensorchord-pgvecto-rs`](/api/graphcanon/tools/tensorchord-pgvecto-rs)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_