47705a8968
* fix: rewrite relative URLs when syncing to GitHub discussion Relative URLs back to supabse.com won't work in GitHub discussions, so rewrite them back to absolute URLs starting with https://supabase.com * fix: replace all supabase urls with relative urls * chore: add linting for relative urls * chore: bump linter version * Prettier --------- Co-authored-by: Chris Chinchilla <chris.ward@supabase.io>
125 lines
4.1 KiB
Plaintext
125 lines
4.1 KiB
Plaintext
---
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id: 'pgvector'
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title: 'pgvector: Embeddings and vector similarity'
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description: '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 Postgres extension for vector similarity search. It can also be used for storing [embeddings](/blog/openai-embeddings-postgres-vector).
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<Admonition type="note">
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The name of pgvector's Postgres extension is [vector](https://github.com/pgvector/pgvector/blob/258eaf58fdaff1843617ff59ea855e0768243fe9/README.md?plain=1#L64).
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</Admonition>
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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](/docs/guides/ai/quickstarts/generate-text-embeddings) for retrieval augmented generation.
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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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queryGroup="database-method"
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>
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<TabPanel id="dashboard" label="Dashboard">
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1. Go to the [Database](/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(384)
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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 Transformer.js, then store it in the database using the Supabase client.
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```js
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import { pipeline } from '@xenova/transformers'
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const generateEmbedding = await pipeline('feature-extraction', 'Supabase/gte-small')
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const title = 'First post!'
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const body = 'Hello world!'
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// Generate a vector using Transformers.js
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const output = await generateEmbedding(body, {
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pooling: 'mean',
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normalize: true,
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})
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// Extract the embedding output
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const embedding = Array.from(output.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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## Specific usage cases
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### Queries with filtering
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If you use an IVFFlat or HNSW index and naively filter the results based on the value of another column, you may get fewer rows returned than requested.
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For example, the following query may return fewer than 5 rows, even if 5 corresponding rows exist in the database. This is because the embedding index may not return 5 rows matching the filter.
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```
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SELECT * FROM items WHERE category_id = 123 ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
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```
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To get the exact number of requested rows, use [iterative search](https://github.com/pgvector/pgvector/?tab=readme-ov-file#iterative-index-scans) to continue scanning the index until enough results are found.
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## More pgvector and Supabase resources
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- [Supabase Clippy: ChatGPT for Supabase Docs](/blog/chatgpt-supabase-docs)
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- [Storing OpenAI embeddings in Postgres with pgvector](/blog/openai-embeddings-postgres-vector)
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- [A ChatGPT Plugins Template built with Supabase Edge Runtime](/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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