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Structured Output

One of our favorite features is using Agents to generate structured data (i.e. a pydantic model). Use this feature to extract features, classify data, produce fake data etc. The best part is that they

Example

Let’s create an Movie Agent to write a MovieScript for us.

movie_agent.py

from typing import List
from rich.pretty import pprint
from pydantic import BaseModel, Field
from bitca.agent import Agent, RunResponse
from bitca.model.openai import OpenAIChat


class MovieScript(BaseModel):
    setting: str = Field(..., description="Provide a nice setting for a blockbuster movie.")
    ending: str = Field(..., description="Ending of the movie. If not available, provide a happy ending.")
    genre: str = Field(
        ..., description="Genre of the movie. If not available, select action, thriller or romantic comedy."
    )
    name: str = Field(..., description="Give a name to this movie")
    characters: List[str] = Field(..., description="Name of characters for this movie.")
    storyline: str = Field(..., description="3 sentence storyline for the movie. Make it exciting!")


# Agent that uses JSON mode
json_mode_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You write movie scripts.",
    response_model=MovieScript,
)
# Agent that uses structured outputs
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"),
    description="You write movie scripts.",
    response_model=MovieScript,
    structured_outputs=True,
)


# Get the response in a variable
# json_mode_response: RunResponse = json_mode_agent.run("New York")
# pprint(json_mode_response.content)
# structured_output_response: RunResponse = structured_output_agent.run("New York")
# pprint(structured_output_response.content)

json_mode_agent.print_response("New York")
structured_output_agent.print_response("New York")

Run the script to see the output.

The output is an object of the MovieScript class, here’s how it looks:

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