VictoKu1/encycloped.ai

AI Summary: Implement a feature that allows users to select text within an article, triggering a modal window displaying three AI-generated topic suggestions based on the selected text. Users can then click a suggestion to create a new article on that topic or enter a custom topic. The modal should be well-designed, accessible, and handle API calls efficiently.

AI Summary: Debug and fix the logic within the Flask application that generates and validates the references section of dynamically created encyclopedia articles. The current system sometimes produces articles with missing or incomplete reference sections, causing broken links between in-text citations and the listed sources. The solution requires investigating the article generation process, likely involving the interaction with the LLM API (e.g., ChatGPT), and implementing robust validation to ensure complete and accurate reference lists.

AI Summary: Implement a feature allowing users to choose between using the OpenAI API (default) or a local LLM (Ollama with DeepSeek-R1) when running the encycloped.ai application. This involves adding command-line argument handling, prompting the user for local LLM configuration, and updating the application's logic to seamlessly switch between the two LLM providers. Documentation updates are also required.

AI Summary: Implement a blue circle loading animation that displays while waiting for the LLM API response when loading encyclopedia topics. The animation should be visible during searches, topic selections, and direct URL access to new topics, disappearing once the response is received. The animation should be centrally located, visually unobtrusive, and not interfere with other UI elements. Bonus points for making the animation customizable.

AI Summary: Implement an agent that analyzes LLM-generated encyclopedia articles to identify and suggest related new article topics to users. This agent should present these suggestions as clickable links, triggering article generation or retrieval upon click.

AI Summary: Implement a system to track the generation date of encyclopedia topics, automatically updating topics older than one month by resubmitting them to the LLM for a refresh while preserving the existing topic structure. This involves adding database functionality (or persistent storage) to store generation dates and a mechanism to trigger LLM updates based on age.