Gemini 함수 호출 가이드

from google import genai
from google.genai import types

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options={"base_url": "https://api.apiyi.com"}
)

# 1. Define tools
tools = [{
    "function_declarations": [{
        "name": "get_weather",
        "description": "Get current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name, e.g. Beijing"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["city"]
        }
    }]
}]

# 2. First request: the model decides to call
contents = [types.Content(role="user", parts=[types.Part(text="How hot is it in Beijing right now?")])]

r1 = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=contents,
    config={"tools": tools}
)

call = r1.candidates[0].content.parts[0].function_call
print(f"Model wants: {call.name}, args: {dict(call.args)}")

# 3. Execute locally (fake data standing in for a real lookup)
weather = {"city": call.args["city"], "temp": 26, "condition": "sunny"}

# 4. Return the result: append the model's reply (function_call + thought signature)
#    and the function result to the history
contents.append(r1.candidates[0].content)
contents.append(types.Content(
    role="user",
    parts=[types.Part(
        function_response=types.FunctionResponse(name=call.name, response=weather)
    )]
))

r2 = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=contents,
    config={"tools": tools}
)
print(r2.text)

config = {
    "tools": tools,
    "tool_config": {"function_calling_config": {"mode": "AUTO"}}
}

# Force get_weather only
config = {
    "tools": tools,
    "tool_config": {
        "function_calling_config": {
            "mode": "ANY",
            "allowed_function_names": ["get_weather"]
        }
    }
}

# Generic agent loop skeleton
MAX_ROUNDS = 5
for _ in range(MAX_ROUNDS):
    response = client.models.generate_content(
        model="gemini-3.5-flash", contents=contents, config={"tools": tools}
    )
    parts = response.candidates[0].content.parts
    calls = [p.function_call for p in parts if getattr(p, "function_call", None)]
    if not calls:
        print(response.text)  # no more calls — final answer
        break

    contents.append(response.candidates[0].content)  # includes thought signatures
    result_parts = [
        types.Part(function_response=types.FunctionResponse(
            name=c.name, response=execute(c.name, dict(c.args))
        ))
        for c in calls
    ]
    contents.append(types.Content(role="user", parts=result_parts))