Response Format

from upsonic import Agent, Task

# Create agent
agent = Agent(model="openai/gpt-4o")

# Default string response
task = Task(
    description="What is the capital of Japan?",
    response_format=str
)

# Execute and print result
result = agent.do(task)
print(result)

from upsonic import Agent, Task
from pydantic import BaseModel

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

# Create agent
agent = Agent(model="openai/gpt-4o")

# Task with structured response
task = Task(
    description="Analyze the current state of electric vehicle market and provide structured results",
    response_format=AnalysisResult
)

# Execute and access structured result
result = agent.do(task)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence}")
print(f"Recommendations: {result.recommendations}")
print(f"Key Metrics: {result.key_metrics}")

from upsonic import Agent, Task
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

# Create agent
agent = Agent(model="openai/gpt-4o")

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

# Execute and access nested structured result
result = agent.do(task)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence}")
print(f"Metrics: {result.metrics}")
print(f"Recommendations: {result.recommendations}")

from upsonic import Agent, Task
from pydantic import BaseModel

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

# Response format can be set dynamically
def create_analysis_task(analysis_type: str):
    if analysis_type == "simple":
        return Task(description="Provide a brief summary of AI trends", response_format=str)
    elif analysis_type == "detailed":
        return Task(description="Provide detailed analysis of AI trends", response_format=AnalysisResult)
    else:
        return Task(description="Analyze AI trends", response_format=None)  # defaults to str

# Create agent
agent = Agent(model="openai/gpt-4o")

# Use dynamic task creation
task = create_analysis_task("detailed")
result = agent.do(task)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence}")