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OpenAI API .cursorrules prompt files

The OpenAI API is a cloud-based service for accessing advanced AI models, enabling streamlined integration of natural language processing capabilities into applications.

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5

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NEW

Cursor AI setup using Python & OpenAI API .cursorrules prompt file

You are tasked with writing a multi-agent program using Python and the OpenAI API. Follow these instructions carefully to create the program:

Use Python version {{PYTHON_VERSION}} and adhere strictly to PEP 8 style guidelines. The OpenAI API version you'll be using is {{OPENAI_API_VERSION}}.
Begin by importing the necessary libraries. You'll need:

The 'openai' package for API interactions
The 'swarm' module from the OpenAI package
Any other standard Python libraries you might need (e.g., 'random', 'time')

Create a Swarm class that will manage the multi-agent system. This class should:

Initialize with the number of agents ({{NUM_AGENTS}})
Have methods for creating agents, assigning tasks, and aggregating results

Set up individual agents within the Swarm class. Each agent should:

Have a unique identifier
Be capable of performing the assigned task using the OpenAI API
Return results in a format that can be aggregated by the Swarm

Implement the main task execution logic. The task to be performed is:
<task\_description>
{{TASK_DESCRIPTION}}
</task\_description>
Ensure that the implementation divides this task among the agents effectively.
In the main section of your script:

Create an instance of the Swarm class
Initialize the OpenAI API with the appropriate key (use a placeholder like 'YOUR_API_KEY_HERE')
Execute the multi-agent task
Display or return the aggregated results

Include error handling and any necessary helper functions to ensure robust execution.
Add comments throughout your code to explain the purpose and functionality of each section.

Please write your complete Python code within <code> tags. Ensure that your code is fully functional, follows PEP 8 guidelines, and effectively utilizes the OpenAI API and swarm module to create a multi-agent system that accomplishes the described task.

stack:

Python

OpenAI API

Cursor AI setup using Python & OpenAI API .cursorrules prompt file

eesb99

0

NEW

Python OpenAI API .cursorrules prompt file

{
"aiAssistantRole": "You are an AI assistant specialized in Python development.",
"developmentApproach": [
"Clear project structure with separate directories for source code, tests, docs, and config.",
"Modular design with distinct files for models, services, controllers, and utilities.",
"Configuration management using environment variables.",
"Robust error handling and logging, including context capture.",
"Detailed documentation using docstrings and README files.",
"Code style consistency using Ruff.",
"use English only in code generation and code explanations",
{
"aiFriendlyCodingPractices": [
"Descriptive variable and function names",
"Type hints",
"Detailed comments for complex logic",
"Rich error context for debugging"
]
}
],
"additionalInstructions": "You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.",
"rules": [
"Adapt your language to match the user's questions",
"Always use the latest official OpenAI API version, ensuring it's above 1.0.0."
],
"projectStructure": {
"Root Directory": {
"description": "Contains main project files and directories",
"key_files": [
".env (environment variables)",
".gitignore (Git version control)",
"README.md (project documentation and setup instructions)"
]
},
"ai_agent_framework": {
"description": "Core package of the AI agent framework",
"key_files": [
"main.py (entry point for the application)"
]
},
"ai_agent_framework/agents": {
"description": "Houses individual agent implementations",
"key_files": [
"agent_registry.py (manages and accesses different types of agents)"
]
},
"ai_agent_framework/knowledge": {
"description": "Manages knowledge base and data processing",
"key_files": [
"embeding_chainbuzz.py (imports and embeds data into knowledge base)",
"knowledge_base.py (implements VectorDB class for managing the knowledge base)",
"knowledge.db (SQLite database for vector knowledge base)"
]
},
"ai_agent_framework/frontend": {
"description": "Implements user interface for interacting with AI agents",
"key_files": [
"chat_interface.py (handles chat-based interaction with users)"
]
},
"data_source": {
"description": "Contains data files and scripts for data collection",
"key_files": [
"chainbuzz_news_en.json (English news data from Chainbuzz)",
"chainbuzz.py (fetches and updates news data from Chainbuzz API)"
]
}
}
}