[Feature] Implement LLM Support for Text-Based Question Handling

[miguelcsx]

Description

Integrate a Language Learning Model (LLM) with LangChain into the Discord bot to handle text-based questions from users. The system will automatically interpret questions and respond with answers generated by the LLM, along with relevant online content. By default, the command will act as /ask unless specified otherwise. This feature will enhance the bot’s ability to assist users by providing AI-generated answers combined with real-time web searches for more comprehensive information.

Goals

• Enable users to ask text-based questions directly to the Discord bot (without needing explicit slash commands).
• Implement LLM integration (using LangChain) to generate responses.
• Incorporate web search functionality to retrieve relevant content from the internet and provide it alongside the LLM answer.
• Default the question command to /ask when the user does not specify a command.

Tasks

1. LLM Integration:

   • Set up LangChain to integrate with a preferred LLM (e.g., GPT-4 or another suitable model).
   • Ensure that the bot can interpret and respond to text-based questions in a conversational manner.
   • Define the default behavior for all incoming questions to use the /ask command unless otherwise specified.

2. Web Search Functionality:

   • Integrate web scraping or an external API to perform web searches for additional information related to the user’s question.
   • Ensure that web search results are relevant to the question asked and presented in a structured manner (e.g., top 3 links with brief summaries).
   • Combine LLM-generated answers with online content to enhance the richness of the response.

3. Event-Driven Command Handling:

   • Implement event-based question handling rather than relying on explicit slash commands.
   • Allow users to ask questions in a free-form text format and handle the detection of whether a question is being asked.
   • Default any questions asked (without commands) to be treated as /ask requests, processed by the LLM and web search.

4. User Experience and Feedback:

   • Ensure the bot delivers LLM-generated answers in a user-friendly format, with clear distinctions between the AI response and web-sourced information.
   • Allow users to interact with the content (e.g., clicking on links) and ask follow-up questions based on the results provided.
   • Provide a smooth fallback experience if the LLM or web search fails (e.g., informative error messages or retry options).

5. Customization and Flexibility:

   • Allow users to specify whether they want just the LLM answer, only web results, or a combination of both.
   • Provide options for customizing how detailed the LLM answer should be, depending on the user's needs (e.g., short answer vs. detailed explanation).

6. Testing and Tuning:

   • Test the bot’s ability to handle a wide range of questions, ensuring accurate LLM responses and relevant web search results.
   • Gather user feedback to fine-tune the LLM's responses and the relevance of web search content.
   • Test the bot’s ability to understand questions even when phrased differently or when asked in casual language.

Acceptance Criteria

• Users can ask questions in free-form text without using explicit slash commands, and the bot will default to the /ask command.
• The bot responds to each question with a comprehensive LLM-generated answer, supplemented with related web search content.
• The system correctly handles event-based question detection and response without requiring manual slash commands.
• Users can customize whether they want the LLM response, web results, or both in their answers.
• The system gracefully handles errors and provides informative feedback when issues arise.

Priority

High

Type

Feature

Notes

Consider expanding LLM functionality to allow multi-turn conversations (where the bot remembers previous questions and answers). Ensure privacy and security concerns are addressed when fetching web content and interacting with external APIs.