Function calling

export OVERCONTROL_API_KEY="your_api_key"

import json
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OVERCONTROL_API_KEY"],
    base_url="https://api.overcontrolgroup.com/v1",
)

def get_temperature(city):
    if not isinstance(city, str) or city.casefold() != "lisbon":
        raise ValueError("This demonstration only supports Lisbon.")
    return {"city": "Lisbon", "celsius": 18, "source": "demo"}

tools = [{
    "type": "function",
    "function": {
        "name": "get_temperature",
        "description": "Look up demonstration temperature data for Lisbon.",
        "strict": True,
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
            "additionalProperties": False,
        },
    },
}]
messages = [{"role": "user", "content": "Look up the demonstration temperature for Lisbon."}]

first = client.chat.completions.create(
    model="lume-3.5",
    max_tokens=256,
    messages=messages,
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "get_temperature"}},
    parallel_tool_calls=False,
)
choice = first.choices[0]
if choice.finish_reason != "tool_calls" or not choice.message.tool_calls:
    raise RuntimeError("No complete function call was returned.")

assistant = choice.message
messages.append({
    "role": "assistant",
    "content": assistant.content,
    "tool_calls": [call.model_dump(exclude_none=True) for call in assistant.tool_calls],
})
for call in assistant.tool_calls:
    if call.function.name != "get_temperature":
        raise ValueError("Unknown function.")
    arguments = json.loads(call.function.arguments)
    if not isinstance(arguments, dict) or set(arguments) != {"city"}:
        raise ValueError("Unexpected function arguments.")
    result = get_temperature(arguments["city"])
    messages.append({
        "role": "tool",
        "tool_call_id": call.id,
        "content": json.dumps(result),
    })

final = client.chat.completions.create(
    model="lume-3.5",
    max_tokens=256,
    messages=messages,
    tools=tools,
    tool_choice="none",
)
if final.choices[0].finish_reason != "stop":
    raise RuntimeError("The final answer did not complete.")
print(final.choices[0].message.content)

import json
import os
from anthropic import Anthropic

client = Anthropic(
    api_key=os.environ["OVERCONTROL_API_KEY"],
    base_url="https://api.overcontrolgroup.com",
)

def get_temperature(city):
    if not isinstance(city, str) or city.casefold() != "lisbon":
        raise ValueError("This demonstration only supports Lisbon.")
    return {"city": "Lisbon", "celsius": 18, "source": "demo"}

tools = [{
    "name": "get_temperature",
    "description": "Look up demonstration temperature data for Lisbon.",
    "strict": True,
    "input_schema": {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
        "additionalProperties": False,
    },
}]
messages = [{"role": "user", "content": "Look up the demonstration temperature for Lisbon."}]

first = client.messages.create(
    model="lume-3.5",
    max_tokens=256,
    messages=messages,
    tools=tools,
    tool_choice={"type": "tool", "name": "get_temperature", "disable_parallel_tool_use": True},
)
calls = [block for block in first.content if block.type == "tool_use"]
if first.stop_reason != "tool_use" or not calls:
    raise RuntimeError("No complete function call was returned.")

messages.append({
    "role": "assistant",
    "content": [block.model_dump(exclude_none=True) for block in first.content],
})
results = []
for call in calls:
    if call.name != "get_temperature":
        raise ValueError("Unknown function.")
    if not isinstance(call.input, dict) or set(call.input) != {"city"}:
        raise ValueError("Unexpected function arguments.")
    result = get_temperature(call.input["city"])
    results.append({
        "type": "tool_result",
        "tool_use_id": call.id,
        "content": json.dumps(result),
    })
messages.append({"role": "user", "content": results})

final = client.messages.create(
    model="lume-3.5",
    max_tokens=256,
    messages=messages,
    tools=tools,
    tool_choice={"type": "none"},
)
if final.stop_reason != "end_turn":
    raise RuntimeError("The final answer did not complete.")
for block in final.content:
    if block.type == "text":
        print(block.text)