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Tasks support various response formats to structure the output according to your needs. You can specify the expected format using the response_format parameter.

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String Response (Default)

# Default string response
task = Task(
    description="Provide a simple text response",
    response_format=str
)

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Pydantic Model Response

from pydantic import BaseModel

class AnalysisResult(BaseModel):
    summary: str
    confidence: float
    recommendations: list[str]
    key_metrics: dict[str, float]

# Task with structured response
task = Task(
    description="Analyze the provided data and provide structured results",
    response_format=AnalysisResult
)

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Complex Nested Models

from pydantic import BaseModel
from typing import List, Optional

class Metric(BaseModel):
    name: str
    value: float
    unit: str

class Recommendation(BaseModel):
    title: str
    description: str
    priority: str
    estimated_impact: float

class DetailedAnalysis(BaseModel):
    summary: str
    confidence: float
    metrics: List[Metric]
    recommendations: List[Recommendation]
    risk_factors: Optional[List[str]] = None

# Task with complex structured response
task = Task(
    description="Perform comprehensive analysis with detailed metrics and recommendations",
    response_format=DetailedAnalysis
)

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Response Format Types

TypeDescriptionUse Case
strSimple text responseBasic questions, summaries
BaseModelStructured Pydantic modelComplex data, analysis results
NoneNo format constraintFlexible responses

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Dynamic Response Format

# Response format can be set dynamically
def create_analysis_task(analysis_type: str):
    if analysis_type == "simple":
        return Task(description="Simple analysis", response_format=str)
    elif analysis_type == "detailed":
        return Task(description="Detailed analysis", response_format=AnalysisResult)
    else:
        return Task(description="Flexible analysis", response_format=None)

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Best Practices

  • Structured Data: Use Pydantic models for complex, structured responses
  • Field Validation: Leverage Pydantic’s built-in validation for data integrity
  • Optional Fields: Use Optional types for fields that might not always be present
  • Nested Models: Break down complex responses into smaller, reusable models
  • Type Hints: Always provide clear type hints for better IDE support and validation
  • Default Values: Set appropriate default values for optional fields
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