Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City name, e.g., San Francisco"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"]
                }
            },
            "required": ["location"]
        }
    }
}]

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Define your structure with Pydantic
class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

# Request structured output
response = client.beta.chat.completions.parse(
    model="openai/gpt-5",
    messages=[
        {"role": "system", "content": "Extract calendar events from text"},
        {"role": "user", "content": "Team meeting tomorrow at 2pm with Alice and Bob"}
    ],
    response_format=CalendarEvent
)

# Guaranteed to match schema
event = response.choices[0].message.parsed
print(event.name)  # "Team meeting"
print(event.participants)  # ["Alice", "Bob"]

# Define schema directly
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "email": {"type": "string", "format": "email"},
        "age": {"type": "integer", "minimum": 0},
        "interests": {
            "type": "array",
            "items": {"type": "string"}
        }
    },
    "required": ["name", "email"],
    "additionalProperties": False
}

response = client.chat.completions.create(
    model="openai/gpt-5",
    messages=[
        {"role": "user", "content": "Extract user info: John Doe, john@example.com, 30 years old, likes hiking and coding"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "user_profile",
            "strict": True,
            "schema": schema
        }
    }
)

import json
user = json.loads(response.choices[0].message.content)
# Guaranteed to match schema exactly

from pydantic import BaseModel, Field
from typing import List, Optional

class Address(BaseModel):
    street: str
    city: str
    country: str
    zipcode: str

class Company(BaseModel):
    name: str
    industry: str
    employees: int = Field(ge=1)

class Person(BaseModel):
    full_name: str = Field(description="Full name of the person")
    email: str = Field(pattern=r"^[\w\.-]+@[\w\.-]+\.\w+$")
    age: Optional[int] = Field(None, ge=0, le=150)
    address: Address
    employer: Optional[Company] = None
    skills: List[str] = Field(default_factory=list)

# Extract complex structured data
response = client.beta.chat.completions.parse(
    model="openai/gpt-5",
    messages=[{
        "role": "user",
        "content": """
        Extract: Alice Johnson, alice@example.com, 28 years old.
        Lives at 123 Main St, San Francisco, CA 94105.
        Works at TechCorp (software industry, 500 employees).
        Skills: Python, Machine Learning, Data Science.
        """
    }],
    response_format=Person
)

person = response.choices[0].message.parsed
print(f"{person.full_name} works at {person.employer.name}")
print(f"Skills: {', '.join(person.skills)}")

# Medical record extraction - TEE protected
class MedicalRecord(BaseModel):
    patient_id: str
    diagnosis: str
    medications: List[str]
    visit_date: str

response = client.beta.chat.completions.parse(
    model="phala/qwen-2.5-7b-instruct",  # Confidential model
    messages=[{
        "role": "user",
        "content": "Extract from: Patient #12345 diagnosed with hypertension, prescribed lisinopril..."
    }],
    response_format=MedicalRecord
)

# ✅ Extraction happens in TEE
# ✅ PHI never leaves hardware encryption
# ✅ Structured output guaranteed
# ✅ HIPAA-compliant processing

from pydantic import ValidationError

try:
    response = client.beta.chat.completions.parse(
        model="openai/gpt-5",
        messages=[{"role": "user", "content": "..."}],
        response_format=MySchema
    )

    # If model returns invalid JSON, OpenAI automatically refines
    # until it matches schema (up to 3 retries)

    data = response.choices[0].message.parsed
    print("Valid structured output:", data)

except ValidationError as e:
    # Pydantic validation failed
    print(f"Schema validation error: {e}")

except Exception as e:
    # Other errors
    print(f"Error: {e}")

# Good: Simple, clear schema
class User(BaseModel):
    name: str
    age: int

# Avoid: Overly complex nested schemas
class OverlyComplex(BaseModel):
    nested: dict[str, list[dict[str, Union[int, str, list[dict]]]]]