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+Metadata-Version: 2.4
+Name: vecs
+Version: 0.4.5
+Summary: pgvector client
+Home-page: https://github.com/supabase/vecs
+Author: Oliver Rice
+License: MIT
+Classifier: Development Status :: 4 - Beta
+Classifier: Natural Language :: English
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: pgvector==0.3.*
+Requires-Dist: sqlalchemy==2.*
+Requires-Dist: psycopg2-binary==2.9.*
+Requires-Dist: flupy==1.*
+Requires-Dist: deprecated==1.2.*
+Provides-Extra: dev
+Requires-Dist: pytest; extra == "dev"
+Requires-Dist: parse; extra == "dev"
+Requires-Dist: numpy; extra == "dev"
+Requires-Dist: pytest-cov; extra == "dev"
+Provides-Extra: docs
+Requires-Dist: mkdocs; extra == "docs"
+Requires-Dist: pygments; extra == "docs"
+Requires-Dist: pymdown-extensions; extra == "docs"
+Requires-Dist: pymarkdown; extra == "docs"
+Requires-Dist: mike; extra == "docs"
+Provides-Extra: text-embedding
+Requires-Dist: sentence-transformers==2.*; extra == "text-embedding"
+Dynamic: author
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: summary
+
+# vecs
+
+<p>
+    <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>
+    <a href="https://github.com/supabase/vecs/actions">
+        <img src="https://github.com/supabase/vecs/workflows/tests/badge.svg" alt="test status" height="18">
+    </a>
+    <a href="https://github.com/supabase/vecs/actions">
+        <img src="https://github.com/supabase/vecs/workflows/pre-commit/badge.svg" alt="Pre-commit Status" height="18">
+    </a>
+</p>
+
+<p>
+    <a href="https://badge.fury.io/py/vecs"><img src="https://badge.fury.io/py/vecs.svg" alt="PyPI version" height="18"></a>
+    <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>
+    <a href="https://pypi.org/project/vecs/"><img src="https://img.shields.io/pypi/dm/vecs.svg" alt="Download count" height="18"></a>
+</p>
+
+---
+
+**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"]
+```