Extract People

class PeopleResponse(BaseModel):
    people: list[str] = Field(..., description="List of all people mentioned in the text")

extract_people_agent = Agent(name="people_extractor")

task = Task(
    description=f"Extract all the person names mentioned in the following text:\n\n{paragraph}",
    response_format=PeopleResponse,
)

# task_examples/extract_people/extract_people.py

from pydantic import BaseModel, Field
from upsonic import Agent, Task

# --- Step 1: Define response format ---
class PeopleResponse(BaseModel):
    people: list[str] = Field(..., description="List of all people mentioned in the text")

# --- Step 2: Define the agent ---
extract_people_agent = Agent(name="people_extractor")

# --- Step 3: Example usage ---
if __name__ == "__main__":
    paragraph = (
        "Last week, Elon Musk met with Bill Gates and Jeff Bezos in California to discuss the future of space exploration and AI safety. "
        "Meanwhile, Taylor Swift and Beyoncé were seen attending a charity event organized by Oprah Winfrey and Barack Obama in New York. "
        "Mark Zuckerberg and Sundar Pichai joined Tim Cook for a roundtable about responsible technology innovation, while Serena Williams and LeBron James spoke about athlete mental health. "
        "Later in the evening, Michelle Obama delivered a heartfelt speech, joined by Malala Yousafzai and Greta Thunberg, who emphasized the importance of education and climate action. "
        "Across the globe, Pope Francis and the Dalai Lama held an interfaith dialogue in Rome, and Lionel Messi congratulated Cristiano Ronaldo on his milestone goal. "
        "At the same time, Emma Watson and Leonardo DiCaprio discussed sustainable fashion with Natalie Portman and Ryan Reynolds. "
        "Finally, in a surprising turn, Warren Buffett and Charlie Munger announced new philanthropic initiatives inspired by Melinda Gates and Mackenzie Scott."
    )

    task = Task(
        description=f"Extract all the person names mentioned in the following text:\n\n{paragraph}",
        response_format=PeopleResponse,
    )

    result = extract_people_agent.do(task)
    print(result)

uv sync

uv run task_examples/extract_people/extract_people.py

PeopleResponse(
    people=[
        'Elon Musk',
        'Bill Gates',
        'Jeff Bezos',
        'Taylor Swift',
        'Beyoncé',
        'Oprah Winfrey',
        'Barack Obama',
        'Mark Zuckerberg',
        'Sundar Pichai',
        'Tim Cook',
        'Serena Williams',
        'LeBron James',
        'Michelle Obama',
        'Malala Yousafzai',
        'Greta Thunberg',
        'Pope Francis',
        'Dalai Lama',
        'Lionel Messi',
        'Cristiano Ronaldo',
        'Emma Watson',
        'Leonardo DiCaprio',
        'Natalie Portman',
        'Ryan Reynolds',
        'Warren Buffett',
        'Charlie Munger',
        'Melinda Gates',
        'Mackenzie Scott'
    ]
)

def extract_people_from_text(text: str) -> list[str]:
    """Extract people from any text input."""
    task = Task(
        description=f"Extract all person names from: {text}",
        response_format=PeopleResponse
    )
    result = extract_people_agent.do(task)
    return result.people

# Usage
text = "John met with Sarah and Mike at the conference."
people = extract_people_from_text(text)
print(people)  # ['John', 'Sarah', 'Mike']

def process_multiple_texts(texts: list[str]) -> list[list[str]]:
    """Process multiple texts and extract people from each."""
    results = []
    for text in texts:
        task = Task(
            description=f"Extract people from: {text}",
            response_format=PeopleResponse
        )
        result = extract_people_agent.do(task)
        results.append(result.people)
    return results

class EnhancedPeopleResponse(BaseModel):
    people: list[str] = Field(..., description="List of all people mentioned")
    first_names: list[str] = Field(..., description="List of first names only")
    last_names: list[str] = Field(..., description="List of last names only")
    titles: list[str] = Field(..., description="List of titles (Dr., Mr., etc.)")

task = Task(
    description="Extract people with detailed breakdown",
    response_format=EnhancedPeopleResponse
)

task_examples/extract_people/
├── extract_people.py      # Main people extraction agent
└── README.md              # Documentation