结构化输出

我们最喜欢的功能之一是使用 Agent 生成结构化数据（即 pydantic 模型）。此功能可用于提取特征、分类数据、生成假数据等。最棒的是，它们可以与函数调用、知识库以及其他所有功能协同工作。

示例

让我们创建一个电影 Agent 来为我们编写 MovieScript。

movie_agent.py

from typing import List
from rich.pretty import pprint
from pydantic import BaseModel, Field
from agno.agent import Agent, RunResponse
from agno.models.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!")

# 使用 JSON 模式的 Agent
json_mode_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You write movie scripts.",
    response_model=MovieScript,
    use_json_mode=True,
)
json_mode_agent.print_response("New York")

# 使用结构化输出的 Agent
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You write movie scripts.",
    response_model=MovieScript,
)

structured_output_agent.print_response("New York")

运行脚本即可查看输出。

pip install -U agno openai

python movie_agent.py

输出是 MovieScript 类的一个对象，看起来是这样的：

# 使用 JSON 模式
MovieScript(
│   setting='The bustling streets of New York City, filled with skyscrapers, secret alleyways, and hidden underground passages.',
│   ending='The protagonist manages to thwart an international conspiracy, clearing his name and winning the love of his life back.',
│   genre='Thriller',
│   name='Shadows in the City',
│   characters=['Alex Monroe', 'Eva Parker', 'Detective Rodriguez', 'Mysterious Mr. Black'],
│   storyline="When Alex Monroe, an ex-CIA operative, is framed for a crime he didn't commit, he must navigate the dangerous streets of New York to clear his name. As he uncovers a labyrinth of deceit involving the city's most notorious crime syndicate, he enlists the help of an old flame, Eva Parker. Together, they race against time to expose the true villain before it's too late."
)

# 使用结构化输出
MovieScript(
│   setting='In the bustling streets and iconic skyline of New York City.',
│   ending='Isabella and Alex, having narrowly escaped the clutches of the Syndicate, find themselves standing at the top of the Empire State Building. As the glow of the setting sun bathes the city, they share a victorious kiss. Newly emboldened and as an unstoppable duo, they vow to keep NYC safe from any future threats.',
│   genre='Action Thriller',
│   name='The NYC Chronicles',
│   characters=['Isabella Grant', 'Alex Chen', 'Marcus Kane', 'Detective Ellie Monroe', 'Victor Sinclair'],
│   storyline='Isabella Grant, a fearless investigative journalist, uncovers a massive conspiracy involving a powerful syndicate plotting to control New York City. Teaming up with renegade cop Alex Chen, they must race against time to expose the culprits before the city descends into chaos. Dodging danger at every turn, they fight to protect the city they love from imminent destruction.'
)

使用解析器模型

您可以使用额外的模型来解析和构建主要模型输出的结构。当主要模型针对推理任务进行了优化，但可能无法始终产生详细的结构化响应时，此方法尤其有效。

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    description="You write movie scripts.",
    response_model=MovieScript,
    parser_model=OpenAIChat(id="gpt-4o"),
)

您还可以为 Parser Model 提供自定义的 parser_model_prompt。

流式结构化输出

流式传输可以与 response_model 结合使用。这会将结构化输出作为事件流中的单个事件返回。

streaming_agent.py

import asyncio
from typing import Dict, List

from agno.agent import Agent
from agno.models.openai.chat import OpenAIChat
from pydantic import BaseModel, Field


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!"
    )
    rating: Dict[str, int] = Field(
        ...,
        description="Your own rating of the movie. 1-10. Return a dictionary with the keys 'story' and 'acting'.",
    )


# 使用带有流式传输的结构化输出 Agent
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You write movie scripts.",
    response_model=MovieScript,
)

structured_output_agent.print_response(
    "New York", stream=True, stream_intermediate_steps=True
)

开发者资源

• 查看 Cookbook
• 查看 Parser Model Cookbook
• 查看 Streaming Structured Output