semvec/pypi-packages huggingface dataset:
agentgpt 0.0.1
Create, run and manage your intelligent LLM agents on dataframes.
Requires Python: >=3.9,<4.0
Keywords: nnext,agents,dataframes,engine,client

# <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>
superagent 0.1.1
Create, run and manage your intelligent LLM agents on dataframes.
Requires Python: >=3.9,<4.0
Keywords: nnext,agents,dataframes,engine,client

# <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>
aigent-nnext 0.0.44
Create, run and manage your intelligent LLM agents on dataframes.
Requires Python: >=3.9,<4.0
Keywords: nnext,agents,dataframes,engine,client

# <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>