Metadata-Version: 2.1
Name: nnext
Version: 0.0.43
Summary: Client library for the NNext Neural search engine
Home-page: https://nnext.io/docs/Python-22a9be22c5cf4869bda849e3f06c0993
Keywords: neural search,semantic,search,engine,client
Author: NNext Team
Author-email: team@nnext.ai
Requires-Python: >=3.7,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: betterproto (==2.0.0b4)
Requires-Dist: grpcio (>=1.46.0,<2.0.0)
Requires-Dist: httpx[http2] (>=0.23.0,<0.24.0)
Requires-Dist: loguru (>=0.5.3,<0.6.0)
Requires-Dist: nest-asyncio (>=1.5.5,<2.0.0)
Requires-Dist: numpy (>=1.21,<2.0)
Requires-Dist: pydantic (>=1.8,<2.0)
Requires-Dist: tqdm (>=4.56.0,<5.0.0)
Requires-Dist: typing-extensions (>=4.0.0,<5.0.0)
Project-URL: Repository, https://github.com/nnextai/pynnext
Description-Content-Type: text/markdown

# <a href="https://nnext.ai/"><img src="https://d3g1vr8yw3euzd.cloudfront.net/nnext-ultra-wide-tingle.png" alt="NNext Python Client"></a>

## About

The NNext Python Client.

NNext is a

* ⚡ blazingly fast
* 🔍 nearest-neighbors vector search engine

<a href="https://twitter.com/intent/follow?screen_name=nnextai"><img src="https://img.shields.io/badge/Follow-nnextai-blue.svg?style=flat&logo=twitter"></a>

[Installation](#installation) |  [Quick Start](#quick-start) | [Documentation](#documentation)

## Installation

To install the pynnext client, activate a virtual environment, and install via pip:

### Supported Python Versions

```shell
Python >= 3.7, < 3.11
```

#### Mac/Linux

```shell
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install nnext
```

#### Windows

```shell
pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install nnext
```

## Quick Start

In order start interacting with NNext, you need to obtain a client
here [https://console.nnext.ai/](https://console.nnext.ai/).

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

Let's begin by installing the Connecting to NNext.

```sql
SELECT images.uid,
       images.name,
       images.vector < - > 'VECTOR(0.19, 0.81, 0.75, 0.11)'::vector AS dist
FROM nnext-public-data.images.laion
ORDER BY
    dist
    LIMIT 100
```

```python
import nnext

nnclient = nnext.NNextClient(api_key="NNEXT_API_KEY")

# Perform a query.
QUERY = """
        SELECT images.uid, images.name,
          images.vector <-> 'VECTOR(0.19, 0.81, 0.75, 0.11)'::vector AS dist
        FROM nnext-public-data.images.laion
        ORDER BY
            dist
        LIMIT 100;
    """
query_job = nnclient.query(QUERY)  # API request
rows = query_job.result()  # Waits for query to finish

for row in rows:
    print(row.name)
```

## Documentation

More documentation is available here here [https://nnext.ai/docs](https://nnext.ai/docs).:

<a href="https://nnext.ai/docs" target="_blank" rel="noopener noreferrer"><img src="https://d3g1vr8yw3euzd.cloudfront.net/3.png" height="100"></a>

