chore: import upstream snapshot with attribution
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## 🎥 Demo
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/**
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* Example 01: Basic Chat
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*
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* This example demonstrates the most basic usage of LLMz in chat mode.
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* It shows how to:
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* - Set up a Botpress client for LLM interactions
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* - Create a simple CLI chat interface
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* - Execute LLMz with basic instructions
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* - Handle conversation flow with automatic user interaction
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*
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* Key concepts:
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* - Chat mode execution with user interaction
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* - Basic instruction prompting
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* - Conversation history management
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*/
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import { CLIChat } from '../utils/cli-chat'
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import { Client } from '@botpress/client'
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import { execute } from 'llmz'
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// Initialize the Botpress Client for LLM interactions
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// This client handles authentication and communication with language models
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const client = new Client({
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botId: process.env.BOTPRESS_BOT_ID!, // Your Botpress bot identifier
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token: process.env.BOTPRESS_TOKEN!, // Authentication token for API access
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})
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// Create a CLI chat interface that provides:
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// - Command-line user input/output
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// - Conversation history management
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// - Message formatting and display
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const chat = new CLIChat()
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// Main conversation loop
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// chat.iterate() returns true until an exit is detected or iterations exceeded
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// chat.iterate captures user input and manages conversation state
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while (await chat.iterate()) {
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// Execute LLMz with the user's message and conversation context
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await execute({
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// Instructions define the agent's role and behavior
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instructions:
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"You are a helpful assistant. Greet the user and suggest topics for discussion using buttons. Don't let users type themselves, suggest topics instead.",
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// Pass the chat interface to enable interactive conversation
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// This automatically adds the user's message to the execution context
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chat,
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// The Botpress client for LLM communication
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client,
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})
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}
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