# Core Pydantic AI packages
pip install pydantic-ai

# Your preferred LLM provider
pip install openai  # or anthropic, groq, etc.

# Core OpenTelemetry packages (required for all integration methods)
pip install opentelemetry-sdk opentelemetry-exporter-otlp 

export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.orq.ai/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <ORQ_API_KEY>"
export OTEL_RESOURCE_ATTRIBUTES="service.name=pydantic-ai-app,service.version=1.0.0"

$env:OTEL_EXPORTER_OTLP_ENDPOINT = "https://api.orq.ai/v2/otel"
$env:OTEL_EXPORTER_OTLP_HEADERS = "Authorization=Bearer <ORQ_API_KEY>"
$env:OTEL_RESOURCE_ATTRIBUTES = "service.name=pydantic-ai-app,service.version=1.0.0"

OTEL_EXPORTER_OTLP_ENDPOINT=https://api.orq.ai/v2/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <ORQ_API_KEY>
OTEL_RESOURCE_ATTRIBUTES=service.name=pydantic-ai-app,service.version=1.0.0

pip install logfire

import logfire
from pydantic_ai import Agent

# Configure Logfire
logfire.configure(
    service_name='orq-traces',
    send_to_logfire=False,  # Disable sending to Logfire cloud
)

# Instrument Pydantic AI automatically
logfire.instrument_pydantic_ai()

# Your agent code works normally with automatic tracing
agent = Agent('openai:gpt-4o-mini')
result = agent.run_sync('What is the capital of France?')
print(result)

from pydantic_ai import Agent

# Setup your chosen integration above, then use Pydantic AI normally
agent = Agent('openai:gpt-4o-mini')

# Simple query
result = agent.run_sync('Explain quantum computing in simple terms')
print(result.data)

from pydantic_ai import Agent, RunContext

agent = Agent(
    'openai:gpt-4o-mini',
    system_prompt='You are a helpful assistant that always responds in a professional tone.'
)

result = agent.run_sync('What are the benefits of renewable energy?')
print(result.data)

from pydantic_ai import Agent, RunContext
import httpx

agent = Agent('openai:gpt-4o-mini')

@agent.tool
async def get_weather(ctx: RunContext[None], location: str) -> str:
    """Get current weather for a location."""
    # Simulate weather API call
    return f"The weather in {location} is sunny and 75°F"

@agent.tool
async def search_web(ctx: RunContext[None], query: str) -> str:
    """Search the web for information."""
    # Simulate web search
    return f"Search results for '{query}': Found relevant information about the topic."

# Use the agent with tools
result = agent.run_sync('What is the weather like in San Francisco and find recent news about climate change?')
print(result.data)

from pydantic_ai import Agent
from pydantic import BaseModel
from typing import List

class BookRecommendation(BaseModel):
    title: str
    author: str
    genre: str
    rating: float
    summary: str

class BookList(BaseModel):
    recommendations: List[BookRecommendation]
    total_count: int

agent = Agent('openai:gpt-4o-mini', result_type=BookList)

result = agent.run_sync('Recommend 3 science fiction books published in the last 5 years')
print(f"Found {result.data.total_count} recommendations:")
for book in result.data.recommendations:
    print(f"- {book.title} by {book.author} ({book.rating}/5)")