In the world of software development, two heads are often better than one. This is the rationale behind pair programming, where developers work together to write code more effectively. But what happens when one of those heads is an artificial intelligence powered by OpenAI? I decided to turn this hypothetical into reality by crafting a Python project that serves as a ‘digital pair programmer’ for developing AWS Lambda applications using Python.
You find the source code for this experiment on Github.
Imagine you are building a serverless application with AWS Lambda and could use a helping hand—or in this case, a helping AI. My project leverages OpenAI’s new Assistants feature to provide real-time assistance, recommendations, and code snippets while you’re developing your Lambda functions.
Here’s a look at how the system works in tandem with serverless AWS Lambda functions, simulating a pair programming session with a twist—it’s with an AI.
The project starts with a Python script that interfaces with OpenAI, acting as your AI-powered pair programmer. This script operates as a middleman, translating your Lambda development needs into tasks for the AI to tackle.
The script was intricately designed with several functions, each serving a clear purpose to offer a smooth pair programming experience.
Through a Thread, constant dialogue with the AI is established. You, the developer, initiate the conversation by posing a question or stating a development need. The AI then takes this input and decides if any specific functions should be evoked to aid the development process.
As an example, if you need to create a new Lambda function handler, a simple message to the AI like “Let’s start writing a handler for processing image uploads” can trigger the create_file function to set up a boilerplate Python file on your behalf, complete with comments and initial code structure.
When the OpenAI AI deems a function call necessary, it requests that specific action, effectively delegating tasks just as a human pair programmer would. Whether it’s generating new files (create_file), reading existing ones (read_file), or listing all files for an overview (list_files), the AI ensures you’re always on top of your application’s structure.
The integration of OpenAI’s Function Calling feature is made possible by handle_required_actions, which processes the outputs needed by the AI and executes the corresponding Python functions:
def handle_required_actions(run: Run) -> list[dict]:
...
for tool_call in run.required_action.submit_tool_outputs.tool_calls:
...
output = function_to_call(**arguments)
...
return tool_outputsBeyond just fetching and executing commands, the project creates a learning and feedback loop. Using the AI’s ability to interface directly with the testing suite via the run_tests function, every development choice can be immediately validated, ensuring that new code additions or changes don’t introduce errors:
def run_tests() -> str:
...
pytest.main([])
...
return captured_outputThis feature emulates the feedback you’d get from a human partner, confirming if the path you’re taking is correct or if adjustments are needed.
This project not only demonstrates Python and AWS Lambda’s flexibility but also highlights how AI can transform the traditional development process. OpenAI’s AI becomes more than just a tool—it’s an intelligent companion that aids in coding, debugging, and deploying serverless applications.
As developers explore the boundaries of what it means to ‘work alongside’ AI, projects like this become tangible examples of the future of collaborative coding. While the AI doesn’t replace a human colleague, it extends capabilities, offering insights and actions at the speed of thought. Whether you’re new to AWS Lambda or an experienced cloud practitioner, integrating AI into your development workflow can open doors to unparalleled efficiency and innovation.
Discover this unfolding synergy between developers and AI—embrace it, experiment with it, and watch as it revolutionizes the way we code.