mono/packages/kbot/tests/scripted

KBot Command Line Interface

Welcome to KBot CLI – a versatile command line tool designed to help you integrate and utilize AI-powered functionalities in your projects. This guide covers installation, configuration, usage, examples, and system architecture using Mermaid diagrams.

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Table of Contents

• Installation
• Configuration
• Usage
• Examples
• Architecture
• Mermaid Diagrams
• Contributing
• License

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Installation

Prerequisites

• Node.js (version 14 or later)
• npm (comes with Node.js) or yarn

Steps

1. Clone the repository:

git clone https://github.com/yourusername/kbot-cli.git
cd kbot-cli

2. Install dependencies:

npm install
# or
yarn install

3. Build the project (if applicable):

npm run build

4. (Optional) Run tests to verify installation:

npm test

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Configuration

KBot CLI requires some configuration files to run properly. Upon initialization, it creates a hidden configuration directory (e.g., .kbot) in your project root. Important files include:

• preferences.md: Contains your personal preferences for contextual responses.
• config.json: Stores API keys and other configuration settings.

You can customize these files to suit your needs.

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Usage

KBot CLI offers multiple modes:

• Completion Mode: Fetches completions based on your prompts.
• Tools Mode: Integrates with external tools for various functionalities.
• Assistant Mode: Provides conversational AI assistance.

Basic Command

Run the tool with a prompt and options:

kbot "Your prompt goes here" --mode completion --model text-davinci-003

Example: Processing a File

To process a specific file, pass the file path using the --include flag:

kbot "Summarize the following code" --include ./src/index.js

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Examples

Refer to the examples section for sample commands and workflows. You can generate and view detailed examples and markdown documentation directly in your terminal.

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Architecture

KBot CLI is modular with distinct components:

• Option Completion: Validates and configures task options.
• Message Aggregation: Gathers prompts, preferences, and source files.
• Parameter Construction: Prepares API request parameters.
• Request Execution: Executes API calls in different modes (completion, tools, assistant).

Below is a high-level overview of the workflow:

flowchart LR
Start[Start]
Options[Complete Options]
Messages[Gather Messages]
Params[Configure Parameters]
Client[Setup API Client]
Execute[Execute Request]
End[Display Result]

Start --> Options
Options --> Messages
Messages --> Params
Params --> Client
Client --> Execute
Execute --> End

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Mermaid Diagrams

Here are some additional Mermaid diagrams to illustrate various processes without using braces in node names:

Process Flow Diagram

flowchart LR
Input[User Input]
Validate[Validate Options]
Configure[Configure API Parameters]
Call[Call API]
Output[Return Response]

Input --> Validate
Validate --> Configure
Configure --> Call
Call --> Output

File Processing Workflow

flowchart TD
ReadFile[Read Source File]
ParseFile[Parse Content]
ApplyFilters[Apply Filters]
GenerateOutput[Generate Output]

ReadFile --> ParseFile
ParseFile --> ApplyFilters
ApplyFilters --> GenerateOutput

Note: All node names in these diagrams are enclosed in square brackets to ensure no curly braces are used.

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Contributing

Contributions are welcome! If you wish to enhance KBot CLI further, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Write tests for your changes.
4. Open a pull request with a detailed description of your changes.

Ensure your code adheres to the established project style and best practices.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy coding with KBot CLI!