jargons.dev AI - jAI ✨

[babblebey]

Implementation PR

• feat: start jAI and implement chat route ai#1
• feat: port jargonsdev/ai into ✨jAI module jargons.dev#181

Summary

We propose creating jargons.dev AI ✨, a Retrieval-Augmented Generation (RAG) application that integrates the existing jargons.dev dictionary as its core knowledge base. This AI application will primarily serve as an API layer, enabling other applications within the jargons.dev ecosystem (e.g., browser extensions, IDE plugins and more) to retrieve word definitions or context-based explanations seamlessly.

Motivation

While the dictionary already offers an excellent resource for definitions, there are limitations when users search for terms not yet present in the database. The RAG application would enhance user experience by:

1. Providing intelligent fallbacks for words not found in the dictionary, ensuring users receive meaningful and contextually relevant responses.
2. Allowing broader integration possibilities across the jargons.dev ecosystem (e.g., browser/IDE extensions).
3. Opening up new avenues for enhancing user engagement and utility through AI-driven interactions.
   • Word Follow-up Chat - See Word Follow-up Chat with jargons.dev AI ✨ #6
   • AI Prompted Contribution - a tiny notice communicating the absence of a word in the dictionary hence alerting user to make the contribution via the jargons Editor

Goal

• Develop a RAG-based API application that leverages the dictionary as its primary data source.
• Integrate fallback mechanisms (with prompt engineering) to generate contextually accurate explanations for out-of-dictionary terms.
• Create flexible and scalable API endpoints for easy integration with:
  • Browser extensions
  • IDE plugins
  • Future jargons.dev applications and features
• Ensure the API application is optimized for speed and accuracy, with robust logging to track dictionary misses and fallback usage for potential improvements.