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107 lines
3.3 KiB
Plaintext
107 lines
3.3 KiB
Plaintext
import Layout from '~/layouts/DefaultGuideLayout'
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export const meta = {
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id: 'pgvector',
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title: 'pgvector: Embeddings and vector similarity',
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description:
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'pgvector: a PostgreSQL extension for storing embeddings and performing vector similarity search.',
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}
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[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).
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Learn more about Supabase's [AI & Vector](/docs/guides/ai) offering.
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## Concepts
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### Vector similarity
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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.
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### Embeddings
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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](https://platform.openai.com/docs/guides/embeddings) which match the GPT model you're using.
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## Usage
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### Enable the extension
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<Tabs
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scrollable
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size="small"
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type="underlined"
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defaultActiveId="dashboard"
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>
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<TabPanel id="dashboard" label="Dashboard">
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1. Go to the [Database](https://supabase.com/dashboard/project/_/database/tables) page in the Dashboard.
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2. Click on **Extensions** in the sidebar.
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3. Search for "vector" and enable the extension.
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</TabPanel>
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<TabPanel id="sql" label="SQL">
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```sql
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-- Example: enable the "vector" extension.
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create extension vector
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with
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schema extensions;
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-- Example: disable the "vector" extension
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drop
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extension if exists vector;
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```
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Even though the SQL code is `create extension`, this is the equivalent of "enabling the extension".
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To disable an extension, call `drop extension`.
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</TabPanel>
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</Tabs>
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## Usage
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### Create a table to store vectors
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```sql
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create table posts (
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id serial primary key,
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title text not null,
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body text not null,
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embedding vector(1536)
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);
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```
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### Storing a vector / embedding
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In this example we'll generate a vector using the OpenAI API client, then store it in the database using the Supabase client.
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```js
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const title = 'First post!'
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const body = 'Hello world!'
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// Generate a vector using OpenAI
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const embeddingResponse = await openai.createEmbedding({
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model: 'text-embedding-ada-002',
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input: body,
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})
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const [{ embedding }] = embeddingResponse.data.data
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// Store the vector in Postgres
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const { data, error } = await supabase.from('posts').insert({
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title,
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body,
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embedding,
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})
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```
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## More pgvector and Supabase resources
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- [Supabase Clippy: ChatGPT for Supabase Docs](https://supabase.com/blog/chatgpt-supabase-docs)
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- [Storing OpenAI embeddings in Postgres with pgvector](https://supabase.com/blog/openai-embeddings-postgres-vector)
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- [A ChatGPT Plugins Template built with Supabase Edge Runtime](https://supabase.com/blog/building-chatgpt-plugins-template)
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- [Template for building your own custom ChatGPT style doc search](https://github.com/supabase-community/nextjs-openai-doc-search)
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export const Page = ({ children }) => <Layout meta={meta} children={children} />
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export default Page
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