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.

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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:

code_snippet = """
def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)
"""

result = explain_code(code_snippet, "Python")
print(f"Code:\n{result.code}\n")
print(f"Explanation:\n{result.explanation}")

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Key concepts

This implementation showcases several important ControlFlow features:

  1. 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

  2. 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.

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:

code_snippet = """
def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)
"""

result = explain_code(code_snippet, "Python")
print(f"Code:\n{result.code}\n")
print(f"Explanation:\n{result.explanation}")

​
Key concepts

This implementation showcases several important ControlFlow features:

  1. 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

  2. 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.