deployment section

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Oliver Rice committed 2023-06-01 14:33:39 -05:00
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@@ -54,7 +54,7 @@ Or, more specifically:
create table vecs.docs (
id text primary key,
vec vector(3) not null,
metadata jsonb not null default '{}'::jsonb,
metadata jsonb not null default '{}'::jsonb
);
```
@@ -124,11 +124,39 @@ or [OpenAI integration example](https://supabase.github.io/vecs/integrations_ope
## Deploying with Supabase
As usual, if you combine [supabase/vecs](https://github.com/supabase/vecs) with the rest of Supabase, you get more than the sum of the parts.
Once you're happy with your vecs collection, you can make it acessible to your front-end and supabase-js by exposing the collection as a view
Once you're happy with your vecs collection, you can make it acessible to your front-end through a supabase client library by exposing the collection as a view
in your public schema.
![Expose with view](/images/blog/2023-06-01-vecs/single-database.png)
For example, you could create a view
```sql
create view public.docs as
select
id,
embedding,
metadata, # Expose the metadata as JSON
(metadata->>'url')::text as url # Extract the URL as a string
from
vecs.docs
```
And then access it with the supabase-js client library within your applications:
```js
const { data, error } = await supabase
.from('docs')
.select('id, embedding, metadata')
.eq('url', '/hello-world')
```
For more deployment options, including enterprise scalable architecture, check out the [engineering for scale guide](https://supabase.com/docs/guides/ai/engineering-for-scale#simple-workloads).
## Future ideas
Currently, `vecs` is unopinionated about where vectors come from or how their produced. While there will always be a need for generic vector
Currently, `vecs` is unopinionated about where vectors come from or how they're produced. While there will always be a need for generic vector
storage and querying, its becoming clear that text and image vectorization make up +95% of usage. That gives us the opportunity to
streamline those workflows for users.
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