mirror of
https://github.com/supabase/supabase.git
synced 2026-07-05 01:14:23 +08:00
db24eb2e90
* Revert "docs: rename vector to pgvector (#23308)" This reverts commitc30d5f0b1f. * Revert "docs: update vector reference to pgvector (#23305)" This reverts commitb2bae1351c. * Revert "Fix vector extension (#23386)" This reverts commit7341b30c27. * Revert "Rename vector to pgvector on client side (#23286)" This reverts commitb48b6e201b. * docs: add note to pgvector docs that ext name is vector * fix: add link to vector ext card
111 lines
3.5 KiB
Plaintext
111 lines
3.5 KiB
Plaintext
---
|
|
id: 'pgvector'
|
|
title: 'pgvector: Embeddings and vector similarity'
|
|
description: 'pgvector: a PostgreSQL extension for storing embeddings and performing vector similarity search.'
|
|
---
|
|
|
|
[pgvector](https://github.com/pgvector/pgvector/) is a PostgreSQL extension for vector similarity search. It can also be used for storing [embeddings](https://supabase.com/blog/openai-embeddings-postgres-vector).
|
|
|
|
<Admonition type="note">
|
|
|
|
The name of pgvector's Postgres extension is [vector](https://github.com/pgvector/pgvector/blob/258eaf58fdaff1843617ff59ea855e0768243fe9/README.md?plain=1#L64).
|
|
|
|
</Admonition>
|
|
|
|
Learn more about Supabase's [AI & Vector](/docs/guides/ai) offering.
|
|
|
|
## Concepts
|
|
|
|
### Vector similarity
|
|
|
|
Vector similarity refers to a measure of the similarity between two related items. For example, if you have a list of products, you can use vector similarity to find similar products. To do this, you need to convert each product into a "vector" of numbers, using a mathematical model. You can use a similar model for text, images, and other types of data. Once all of these vectors are stored in the database, you can use vector similarity to find similar items.
|
|
|
|
### Embeddings
|
|
|
|
This is particularly useful if you're building on top of OpenAI's [GPT-3](https://openai.com/blog/gpt-3-apps/). You can create and store [embeddings](/docs/guides/ai/quickstarts/generate-text-embeddings) for retrieval augmented generation.
|
|
|
|
## Usage
|
|
|
|
### Enable the extension
|
|
|
|
<Tabs
|
|
scrollable
|
|
size="small"
|
|
type="underlined"
|
|
defaultActiveId="dashboard"
|
|
queryGroup="database-method"
|
|
>
|
|
<TabPanel id="dashboard" label="Dashboard">
|
|
|
|
1. Go to the [Database](https://supabase.com/dashboard/project/_/database/tables) page in the Dashboard.
|
|
2. Click on **Extensions** in the sidebar.
|
|
3. Search for "vector" and enable the extension.
|
|
|
|
</TabPanel>
|
|
<TabPanel id="sql" label="SQL">
|
|
|
|
```sql
|
|
-- Example: enable the "vector" extension.
|
|
create extension vector
|
|
with
|
|
schema extensions;
|
|
|
|
-- Example: disable the "vector" extension
|
|
drop
|
|
extension if exists vector;
|
|
```
|
|
|
|
Even though the SQL code is `create extension`, this is the equivalent of "enabling the extension".
|
|
To disable an extension, call `drop extension`.
|
|
|
|
</TabPanel>
|
|
</Tabs>
|
|
|
|
## Usage
|
|
|
|
### Create a table to store vectors
|
|
|
|
```sql
|
|
create table posts (
|
|
id serial primary key,
|
|
title text not null,
|
|
body text not null,
|
|
embedding vector(384)
|
|
);
|
|
```
|
|
|
|
### Storing a vector / embedding
|
|
|
|
In this example we'll generate a vector using Transformer.js, then store it in the database using the Supabase client.
|
|
|
|
```js
|
|
import { pipeline } from '@xenova/transformers'
|
|
const generateEmbedding = await pipeline('feature-extraction', 'Supabase/gte-small')
|
|
|
|
const title = 'First post!'
|
|
const body = 'Hello world!'
|
|
|
|
// Generate a vector using Transformers.js
|
|
const output = await generateEmbedding(body, {
|
|
pooling: 'mean',
|
|
normalize: true,
|
|
})
|
|
|
|
// Extract the embedding output
|
|
const embedding = Array.from(output.data)
|
|
|
|
// Store the vector in Postgres
|
|
const { data, error } = await supabase.from('posts').insert({
|
|
title,
|
|
body,
|
|
embedding,
|
|
})
|
|
```
|
|
|
|
## More pgvector and Supabase resources
|
|
|
|
- [Supabase Clippy: ChatGPT for Supabase Docs](https://supabase.com/blog/chatgpt-supabase-docs)
|
|
- [Storing OpenAI embeddings in Postgres with pgvector](https://supabase.com/blog/openai-embeddings-postgres-vector)
|
|
- [A ChatGPT Plugins Template built with Supabase Edge Runtime](https://supabase.com/blog/building-chatgpt-plugins-template)
|
|
- [Template for building your own custom ChatGPT style doc search](https://github.com/supabase-community/nextjs-openai-doc-search)
|