ermnvldmr.com/services/docs/content/en/quetza/guides/conversational-ai.md

title, weight

title	weight
Conversational AI Integration	4

Goal: Integrate Large Language Models (LLMs) while managing context and Discord's timeout constraints.

1. Context Management Strategy

Conversations require history to be coherent.

• Scoping: Map<GuildID, Map<UserID, Message[]>>
  • Guild Key: Prevents cross-server leaks.
  • User Key: Prevents cross-user confusion in public channels.
• Lifecycle: Implement a rolling window (e.g., last 50 messages) to prevent context bloat.

2. Response Streaming

LLMs are slow; Discord is fast (3s timeout).

{{% steps %}}

Step 1: Defer Interaction

Immediately call interaction.deferReply(). This buys you 15 minutes.

Step 2: Invoke LLM

Call your AI service asynchronously.

Step 3: Edit Response

When data arrives, use interaction.editReply(content). {{% /steps %}}

3. Implementation Sketch

// commands/chat.ts
export async function execute(interaction, controller) {
  // 1. Defer
  await interaction.deferReply();

  // 2. Process
  const history = controller.getHistory(interaction.guildId, interaction.user.id);
  const response = await aiService.generate(interaction.options.getString('prompt'), history);

  // 3. Respond
  await interaction.editReply(response);
  controller.updateHistory(interaction.guildId, interaction.user.id, response);
}

Warning

API Costs: AI APIs are often billed by token. Implement strict user-based rate limits in your Controller to prevent abuse.