open-index/open-npm huggingface dataset:
npm package: @nem035/ai-agent-task-runner
Description: AI Agent Task Runner
Author: nemanjacoder@gmail.com
Latest version: 0.0.6

# AI Agent Task Runner

This repo is a WIP.

The agent currently has 2 plugins, one to search Wikipedia and another to perform calculations.

Here's an example

```
Question: Generate a painting of a famous deaf classical music composer sitting under the most popular tree in Japan, painted in the style of the painter that lost his ear.
Answer: <url> (A Van Gogh painting of Mozzart sitting under a cherry blossom tree) 
```

### How it works?

The prompting is based on the [Thought/Action/Observation](https://arxiv.org/abs/2210.03629) and [Chain of Thought](https://arxiv.org/abs/2201.11903) techniques.


Long output with the env var `VERBOSE` being true:
```
Question: What is the population of the capital of France raised to the 3rd power?
Thought: I should find the population of the capital of France and raise it to the third power.
Action: wikipedia: What is the capital of France? 
PAUSE
 -- running plugin action wikipedia What is the capital of France? 
Observation: variants of the above close-ended questions which possess specific responses
are: On what day were you born? ("Saturday.") What is the capital of France?
("Paris
Thought: The capital of France is Paris. I should find its population and raise it to the third power.
Action: wikipedia: What is the population of Paris? 
PAUSE
 -- running plugin action wikipedia What is the population of Paris? 
Observation: population of 12.271.794 habitants on January 1, 2023, or about 19% of the
population of France, making the region France's primate city. The Paris Region
Thought: The population of Paris is 12.271.794. I should raise it to the third power.
Action: calculate: 12271794 ** 3 
PAUSE
 -- running plugin action calculate 12271794 ** 3 
Observation: 1.848094477184203e+21
Answer: The population of the capital of France, Paris, raised to the third power is 1.848094477184203e+21.
```
npm package: @nem035/ai-intern
Description: AI Intern
Author: nemanjacoder@gmail.com
Latest version: 0.0.1

# AI Agent Task Runner

This repo is a WIP.

The agent currently has 2 plugins, one to search Wikipedia and another to perform calculations.

Here's an example

```
Question: Generate a painting of a famous deaf classical music composer sitting under the most popular tree in Japan, painted in the style of the painter that lost his ear.
Answer: <url> (A Van Gogh painting of Mozzart sitting under a cherry blossom tree) 
```

### How it works?

The prompting is based on the [Thought/Action/Observation](https://arxiv.org/abs/2210.03629) and [Chain of Thought](https://arxiv.org/abs/2201.11903) techniques.


Long output with the env var `VERBOSE` being true:
```
Question: What is the population of the capital of France raised to the 3rd power?
Thought: I should find the population of the capital of France and raise it to the third power.
Action: wikipedia: What is the capital of France? 
PAUSE
 -- running plugin action wikipedia What is the capital of France? 
Observation: variants of the above close-ended questions which possess specific responses
are: On what day were you born? ("Saturday.") What is the capital of France?
("Paris
Thought: The capital of France is Paris. I should find its population and raise it to the third power.
Action: wikipedia: What is the population of Paris? 
PAUSE
 -- running plugin action wikipedia What is the population of Paris? 
Observation: population of 12.271.794 habitants on January 1, 2023, or about 19% of the
population of France, making the region France's primate city. The Paris Region
Thought: The population of Paris is 12.271.794. I should raise it to the third power.
Action: calculate: 12271794 ** 3 
PAUSE
 -- running plugin action calculate 12271794 ** 3 
Observation: 1.848094477184203e+21
Answer: The population of the capital of France, Paris, raised to the third power is 1.848094477184203e+21.
```
npm package: @nem035/combs
Description: Simple utility to generate all combinations for a given array.
Author: nemanjacoder@gmail.com
Latest version: 1.0.0

# Combs [![npm](https://img.shields.io/npm/v/@nem035/combs.svg?style=flat-square)](https://www.npmjs.com/package/@nem035/combs)

Simple utility to generate all combinations for a given array.

Note: order doesn't matter, `["a", "b"]` and `["b", "a"]` are the same combination.

## Usage

```js
const combs = require("@nem035/combs");

const res = combs(["a", "b", "c"]);
/*
[
  ["a"],
  ["b"],
  ["c"],
  ["a", "b"],
  ["a", "c"],
  ["b", "c"],
  ["a", "b", "c"],
]
*/
```

Doesn't matter what's in the array:

```js
const combs = require("@nem035/combs");

const res = combs(["a", 2, { value: "hi" }]);
/*
[
  ["a'],
  [2],
  [{ value: "hi' }],
  ["a', 2],
  ["a', { value: "hi' }],
  [2, { value: "hi' }],
  ["a', 2, { value: "hi' }]
]
*/
```

## Installation

```sh
# npm
npm i @nem035/combs

# yarn
yarn add @nem035/combs
```

## Licence

MIT
npm package: @nem035/eslint-config
Description: eslint-config =============
Author: Nemanja Stojanovic
Latest version: 1.0.0

eslint-config
=============

[![npm version](https://img.shields.io/npm/v/@nem035/eslint-config.svg?style=flat-square)](https://www.npmjs.com/package/@nem035/eslint-config)

```bash
npm install --save @nem035/eslint-config
```
npm package: @nem035/gpt-3-encoder
Description: Javascript BPE Encoder Decoder for GPT-2 / GPT-3
Author: nemanjacoder@gmail.com
Latest version: 1.1.7

# GPT-3-Encoder
Javascript BPE Encoder Decoder for GPT-2 / GPT-3

## About
GPT-2 and GPT-3 use byte pair encoding to turn text into a series of integers to feed into the model. This is a javascript implementation of OpenAI's original python encoder/decoder which can be found [here](https://github.com/openai/gpt-2)

## Install with npm

```
npm install @nem035/gpt-3-encoder
```

## Usage

Compatible with Node >= 12

```js
const {encode, decode} = require('@nem035/gpt-3-encoder')

const str = 'This is an example sentence to try encoding out on!'
const encoded = encode(str)
console.log('Encoded this string looks like: ', encoded)

console.log('We can look at each token and what it represents')
for(let token of encoded){
  console.log({token, string: decode([token])})
}

const decoded = decode(encoded)
console.log('We can decode it back into:\n', decoded)

```