Code Explanation
Use ControlFlow to generate natural language explanations of code snippets.
This example demonstrates how to use ControlFlow to create a task that explains code snippets in natural language. It showcases the use of custom types and context passing for code documentation tasks.
Code
The following code creates a function that takes a code snippet and its programming language, then returns an explanation of the code:
import controlflow as cf
from pydantic import BaseModel
class CodeExplanation(BaseModel):
code: str
explanation: str
language: str
def explain_code(code: str, language: str=None) -> CodeExplanation:
return cf.run(
f"Explain the following code snippet",
result_type=CodeExplanation,
context={"code": code, "language": language or 'auto-detect'}
)
Now we can use this function to explain a code snippet:
Key concepts
This implementation showcases several important ControlFlow features:
-
Pydantic models: We use a Pydantic model (
CodeExplanation
) to define the structure of our explanation result. This ensures that the task returns well-structured, consistent results including the original code, its explanation, and the programming language.class CodeExplanation(BaseModel): code: str explanation: str language: str
-
Context passing: We pass both the code snippet and the programming language as context to the task, providing all necessary information for the explanation process.
context={"code": code, "language": language}
By leveraging these ControlFlow features, we create an efficient and flexible code explanation tool. This example demonstrates how ControlFlow can be used to build AI-powered documentation workflows that can help developers understand and explain code snippets in natural language.