This example demonstrates how to use ControlFlow to create a task that translates text from one language to another. It showcases the use of custom types and context passing for language translation tasks.

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Code

The following code creates a function that takes a text string and a target language, then returns a translation result: 
import controlflow as cf
from pydantic import BaseModel

class TranslationResult(BaseModel):
    translated: str
    target_language: str

def translate_text(text: str, target_language: str) -> TranslationResult:
    return cf.run(
        f"Translate the given text to {target_language}",
        result_type=TranslationResult,
        context={"text": text, "target_language": target_language}
    )
Now we can use this function to translate text: 
original_text = "Hello, how are you?"
target_language = "French"

result = translate_text(original_text, target_language)
print(f"Original: {original_text}")
print(f"Translated ({result.target_language}): {result.translated}")

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Key concepts

This implementation showcases several important ControlFlow features:
  1. Pydantic models: We use a Pydantic model (TranslationResult) to define the structure of our translation result. This ensures that the translation task returns well-structured, consistent results. 
    class TranslationResult(BaseModel):
    original: str
    translated: str
    target_language: str
  2. Context passing: We pass both the original text and the target language as context to the task, providing all necessary information for the translation. 
    context={"text": text, "target_language": target_language}
By leveraging these ControlFlow features, we create an efficient and flexible text translation tool. This example demonstrates how ControlFlow can be used to build AI-powered language processing workflows that can handle translation tasks with ease.