Metadata-Version: 2.1
Name: nnext
Version: 0.0.16
Summary: Python client library for the NNext. A ⚡ blazingly fast, 🔍 nearest-neighbors vector search engine for building delightful ML apps
Home-page: https://nnext.ai
Author: NNext, co
Author-email: team@nnext.ai
License: Apache 2.0
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Requires-Dist: grpcio (>=1.44.0) ; python_version >= "3.6"
Requires-Dist: protobuf (>=3.20.0) ; python_version >= "3.6"


Here's our logo (hover to see the title text):

Inline-style: 

.. image:: https://nnext.io/_next/static/image/src/common/assets/image/appModern/logo-color.2658edf06bcc58c34d55ea41eaef7a2b.png
   :target: https://nnext.io/_next/static/image/src/common/assets/image/appModern/logo-color.2658edf06bcc58c34d55ea41eaef7a2b.png
   :alt: alt text


About
-----

NNext is a


* ⚡ blazingly fast
* 📖 source-available `[Elastic License 2.0] <https://www.elastic.co/licensing/elastic-license>`_
* 🔍 nearest-neighbors vector search engine

Quick Start
-----------

Here's a quick example showcasing how you can create an index, insert vectors/documents and search it on NNext.

Let's begin by starting the NNext server via Docker:

.. code-block::

   docker run -p 6040:6040 -v/tmp/data:/data nnext/nnext:latest --data-dir /data --api-key=Hu52dwsas2AdxdE

We have a `API Client <#api-clients>`_ in python only, but let's use it for this example.

Install the Python client for NNext:

.. code-block::

   pip install nnext

We can now initialize the client and create a ``movies`` index:

.. code-block:: python

   import nnext
   from nnext import _and, _eq, _gte, _in

   nnclient = nnext.Client({
     'api_key': 'Hu52dwsas2AdxdE',
     'nodes': [{
       'host': 'localhost',
       'port': '6040'
     }],
     'connection_timeout_seconds': 2
   })


