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92 lines
2.5 KiB
Plaintext
92 lines
2.5 KiB
Plaintext
import Layout from '~/layouts/DefaultGuideLayout'
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import StepHikeCompact from '~/components/StepHikeCompact'
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export const meta = {
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id: 'ai-vecs-python-client',
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title: 'Python client',
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subtitle: 'Manage unstructured vector stores in PostgreSQL.',
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breadcrumb: 'AI Quickstarts',
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}
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Supabase provides a Python client called [`vecs`](https://github.com/supabase/vecs) for managing unstructured vector stores. This client provides a set of useful tools for creating and querying collections in PostgreSQL using the [pgvector](/docs/guides/database/extensions/pgvector) extension.
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## Quick start
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Let's see how Vecs works using a local database. Make sure you have the Supabase CLI [installed](/docs/guides/cli#installation) on your machine.
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### Initialize your project
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Start a local Postgres instance in any folder using the `init` and `start` commands. Make sure you have Docker running!
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```bash
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# Initialize your project
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supabase init
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# Start Postgres
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supabase start
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```
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### Create a collection
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Inside a Python shell, run the following commands to create a new collection called "docs", with 3 dimensions.
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```py
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import vecs
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# create vector store client
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vx = vecs.create_client("postgresql://postgres:postgres@localhost:54322/postgres")
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# create a collection of vectors with 3 dimensions
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docs = vx.get_or_create_collection(name="docs", dimension=3)
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```
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### Add embeddings
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Now we can insert some embeddings into our "docs" collection using the `upsert()` command:
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```py
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import vecs
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# create vector store client
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docs = vecs.get_or_create_collection(name="docs", dimension=3)
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# a collection of vectors with 3 dimensions
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vectors=[
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("vec0", [0.1, 0.2, 0.3], {"year": 1973}),
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("vec1", [0.7, 0.8, 0.9], {"year": 2012})
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]
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# insert our vectors
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docs.upsert(vectors=vectors)
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```
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### Query the collection
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You can now query the collection to retrieve a relevant match:
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```py
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import vecs
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docs = vecs.get_or_create_collection(name="docs", dimension=3)
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# query the collection filtering metadata for "year" = 2012
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docs.query(
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query_vector=[0.4,0.5,0.6], # required
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limit=1, # number of records to return
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filters={"year": {"$eq": 2012}}, # metadata filters
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)
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```
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## Deep Dive
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For a more in-depth guide on `vecs` collections, see [API](/docs/guides/ai/python/api).
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## Resources
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- Official Vecs Documentation: https://supabase.github.io/vecs/api
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- Source Code: https://github.com/supabase/vecs
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export const Page = ({ children }) => <Layout meta={meta} children={children} />
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export default Page
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