# vecs
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---
**Documentation**: <a href="https://supabase.github.io/vecs/latest/" target="_blank">https://supabase.github.io/vecs/latest/</a>
**Source Code**: <a href="https://github.com/supabase/vecs" target="_blank">https://github.com/supabase/vecs</a>
---
`vecs` is a python client for managing and querying vector stores in PostgreSQL with the [pgvector extension](https://github.com/pgvector/pgvector). This guide will help you get started with using vecs.
If you don't have a Postgres database with the pgvector ready, see [hosting](https://supabase.github.io/vecs/hosting/) for easy options.
## Installation
Requires:
- Python 3.7+
You can install vecs using pip:
```sh
pip install vecs
```
## Usage
Visit the [quickstart guide](https://supabase.github.io/vecs/latest/api) for more complete info.
```python
import vecs
DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
# create vector store client
vx = vecs.create_client(DB_CONNECTION)
# create a collection of vectors with 3 dimensions
docs = vx.get_or_create_collection(name="docs", dimension=3)
# add records to the *docs* collection
docs.upsert(
records=[
(
"vec0", # the vector's identifier
[0.1, 0.2, 0.3], # the vector. list or np.array
{"year": 1973} # associated metadata
),
(
"vec1",
[0.7, 0.8, 0.9],
{"year": 2012}
)
]
)
# index the collection for fast search performance
docs.create_index()
# query the collection filtering metadata for "year" = 2012
docs.query(
data=[0.4,0.5,0.6], # required
limit=1, # number of records to return
filters={"year": {"$eq": 2012}}, # metadata filters
)
# Returns: ["vec1"]
```
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"description": "# vecs\n\n<p>\n <a href=\"https://www.python.org/downloads/\"><img src=\"https://img.shields.io/badge/python-3.7+-blue.svg\" alt=\"Python version\" height=\"18\"></a>\n <a href=\"https://github.com/supabase/vecs/actions\">\n <img src=\"https://github.com/supabase/vecs/workflows/tests/badge.svg\" alt=\"test status\" height=\"18\">\n </a>\n <a href=\"https://github.com/supabase/vecs/actions\">\n <img src=\"https://github.com/supabase/vecs/workflows/pre-commit/badge.svg\" alt=\"Pre-commit Status\" height=\"18\">\n </a>\n</p>\n\n<p>\n <a href=\"https://badge.fury.io/py/vecs\"><img src=\"https://badge.fury.io/py/vecs.svg\" alt=\"PyPI version\" height=\"18\"></a>\n <a href=\"https://github.com/supabase/vecs/blob/master/LICENSE\"><img src=\"https://img.shields.io/pypi/l/markdown-subtemplate.svg\" alt=\"License\" height=\"18\"></a>\n <a href=\"https://pypi.org/project/vecs/\"><img src=\"https://img.shields.io/pypi/dm/vecs.svg\" alt=\"Download count\" height=\"18\"></a>\n</p>\n\n---\n\n**Documentation**: <a href=\"https://supabase.github.io/vecs/latest/\" target=\"_blank\">https://supabase.github.io/vecs/latest/</a>\n\n**Source Code**: <a href=\"https://github.com/supabase/vecs\" target=\"_blank\">https://github.com/supabase/vecs</a>\n\n---\n\n`vecs` is a python client for managing and querying vector stores in PostgreSQL with the [pgvector extension](https://github.com/pgvector/pgvector). This guide will help you get started with using vecs.\n\nIf you don't have a Postgres database with the pgvector ready, see [hosting](https://supabase.github.io/vecs/hosting/) for easy options.\n\n## Installation\n\nRequires:\n\n- Python 3.7+\n\nYou can install vecs using pip:\n\n```sh\npip install vecs\n```\n\n## Usage\n\nVisit the [quickstart guide](https://supabase.github.io/vecs/latest/api) for more complete info.\n\n```python\nimport vecs\n\nDB_CONNECTION = \"postgresql://<user>:<password>@<host>:<port>/<db_name>\"\n\n# create vector store client\nvx = vecs.create_client(DB_CONNECTION)\n\n# create a collection of vectors with 3 dimensions\ndocs = vx.get_or_create_collection(name=\"docs\", dimension=3)\n\n# add records to the *docs* collection\ndocs.upsert(\n records=[\n (\n \"vec0\", # the vector's identifier\n [0.1, 0.2, 0.3], # the vector. list or np.array\n {\"year\": 1973} # associated metadata\n ),\n (\n \"vec1\",\n [0.7, 0.8, 0.9],\n {\"year\": 2012}\n )\n ]\n)\n\n# index the collection for fast search performance\ndocs.create_index()\n\n# query the collection filtering metadata for \"year\" = 2012\ndocs.query(\n data=[0.4,0.5,0.6], # required\n limit=1, # number of records to return\n filters={\"year\": {\"$eq\": 2012}}, # metadata filters\n)\n\n# Returns: [\"vec1\"]\n```\n\n\n",
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