.cursorrules Graphical Apps Development

by @Dmitriy Leybel

# Project Synopsis

Pyllments is a Python library for building graphical and API-based LLM applications through chaining together Elements in a potentially cyclic graph. Elements and Payloads are a type of Components. A Component is composed of a Model and Views. The Model handles the underlying data and logic, while the Views are the UI components that are used to display display the interactive UI used to interact with the Model.

An Element is a type of Component that is responsible for a specific function. For instance, an Element can handle the LLM selection and generation by making calls to LLM providers. Another Element may handle the chat interface, whose Model would store the chat message history, and the Views would be the text boxes and buttons used to interact with the chat interface. Elements are meant to connect to other Elements through Ports. All that is necessary to link Elements together is to link the output port of one Element to the input port of Another. Each output port may have unlimited input ports it connects to, and each input port may have unlimited output ports it connects to. The ports follow an observer pattern where the output port is the subject and the input port is the observer. The subject notifies the observers when a certain event that we set within the Element is triggered.

In order to connect an input and and output port, they need to be setup in a manner that sends and receives the same type of Payload. A Payload is also a Component with a Model as well as views responsible for the display logic. Elements may receive payloads and use methods of the Payload to generate the views for the UI. The sending Element is responsible for packing data into the Payload.

I am currently working on making this a fully-fledged framework.

# Project Organization

Here is an example of the file structure of an individual element:

chat_interface:
  - __init__.py
  - chat_interface_element.py
  - chat_interface_model.py
  - css:
    - buttons.css
    - column.css
    - input.css

# Primary Libraries Used

- Panel is used to create the visualization layer and run the GUI. Views tend to consist of Panel objects which can be styled with Python and CSS.
- Param is used to create parameterized classes which help create parameters that handle type validation, default values, constraints, and most importantly, reactivity(setting event handlers to catch changes).
- Langchain is responsible for the specific functions pertaining to incorporating LLM workflows.

# Development Priorities

Pyllments code is prioritized on being developer-friendly, where extensibility and modularity are first-class citizens. Elements should be customizeable with clean and intuitive interfaces. It should also be easy to create new elements depending on the needs of the developer.

# Documentation

Docstrings should use a NumPy/SciPy style.

AI Development and Deployment Toolkit

A suite of tools designed to streamline AI development, focusing on no-code/low-code solutions, interactive learning, and customizable AI applications:

AI Chatbot Builder Platform

A no-code/low-code platform for creating custom AI chatbots by chaining pre-built Elements, enabling quick deployment of tailored chat solutions.

Interactive AI Workshop Software

Educational tool for teachers and students to explore AI concepts by building graphical or API-based AI applications using Elements.

AI Model Experimentation Suite

Platform for data scientists to prototype and test machine learning models by assembling Elements into workflows, facilitating rapid iteration on AI solutions.

LLM-based API Creator

Service that enables developers to create custom APIs by combining Elements focused on API design and integrating LLMs.

Interactive Documentation Generator

Tool that auto-generates interactive documentation for software projects, allowing users to design UIs for demonstrating API functionalities.

Collaborative AI Development Environment

Platform for multiple users to work on AI projects simultaneously, facilitating version control and real-time collaboration through visual Element chaining.

AI-Powered Data Analysis Dashboard

Application that merges data analytics and LLMs by connecting Elements for data processing, interaction, and visualization.

Customizable Virtual Assistant Interface

Interactive tool for creating virtual personal assistants by chaining Elements to handle tasks like scheduling and reminders.

Game AI Development Kit

Kit for game developers to create intelligent NPCs by assembling Elements that simulate decision-making and generate dialogues using LLMs.

AI-Driven Customer Support Portal

Customizable platform for building automated customer support systems by selecting Elements for handling FAQs and feedback collection.

Overview of .cursorrules prompt

The .cursorrules file outlines a Python library named “Pyllments,” designed for building graphical and API-based applications involving LLMs (Large Language Models) by connecting modular components called Elements. Each Element is a composite of a Model for data and logic, and Views for UI interaction. These Elements are interconnected through Ports, allowing dynamic, observer pattern-based communication. A Payload, another component type with its Model and Views, facilitates data handling and UI generation within Elements. The project is being developed into a complete framework, focusing on developer-friendly features such as extensibility, modularity, and customizable interfaces. The library leverages Panel for visualization, Param for class parameterization, and Langchain for LLM workflows. Docstrings should adhere to NumPy/SciPy documentation styles.

Updated: March 15, 2025

Pyllments is ideal for developers building modular, extensible Python applications with LLM capabilities using graphical and API-based interfaces, promoting component reuse and UI consistency.

Usefull for: