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Taryn King
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## I have read the [CONTRIBUTING.md](https://github.com/supabase/supabase/blob/master/CONTRIBUTING.md) file. YES ## What kind of change does this PR introduce? Updates verbiage throughout docs to use postgres over postgresql. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Updated terminology throughout documentation, guides, and resources for consistent product naming across all user-facing materials, including page titles, descriptions, and reference documentation. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
85 lines
2.2 KiB
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85 lines
2.2 KiB
Plaintext
---
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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 Postgres.'
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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 Postgres 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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data=[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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