채팅 시나리오에서 외부 함수 호출과 함께 Gemini API를 사용하여 텍스트 응답 생성

외부 함수 호출과 함께 Gemini API를 사용하여 텍스트 응답을 생성합니다. 이 예시에서는 함수 2개와 순차적 프롬프트 2개가 있는 채팅 시나리오를 보여줍니다.

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• 함수 호출

코드 샘플

Python

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.

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import vertexai
from vertexai.generative_models import (
    FunctionDeclaration,
    GenerativeModel,
    Part,
    Tool,
)

def generate_function_call_chat(project_id: str, location: str) -> tuple:
    prompts = []
    summaries = []

    # Initialize Vertex AI
    vertexai.init(project=project_id, location=location)

    # Specify a function declaration and parameters for an API request
    get_product_info_func = FunctionDeclaration(
        name="get_product_sku",
        description="Get the SKU for a product",
        # Function parameters are specified in OpenAPI JSON schema format
        parameters={
            "type": "object",
            "properties": {
                "product_name": {"type": "string", "description": "Product name"}
            },
        },
    )

    # Specify another function declaration and parameters for an API request
    get_store_location_func = FunctionDeclaration(
        name="get_store_location",
        description="Get the location of the closest store",
        # Function parameters are specified in OpenAPI JSON schema format
        parameters={
            "type": "object",
            "properties": {"location": {"type": "string", "description": "Location"}},
        },
    )

    # Define a tool that includes the above functions
    retail_tool = Tool(
        function_declarations=[
            get_product_info_func,
            get_store_location_func,
        ],
    )

    # Initialize Gemini model
    model = GenerativeModel(
        "gemini-1.0-pro", generation_config={"temperature": 0}, tools=[retail_tool]
    )

    # Start a chat session
    chat = model.start_chat()

    # Send a prompt for the first conversation turn that should invoke the get_product_sku function
    prompt = "Do you have the Pixel 8 Pro in stock?"
    response = chat.send_message(prompt)
    prompts.append(prompt)

    # Check the function name that the model responded with, and make an API call to an external system
    if response.candidates[0].content.parts[0].function_call.name == "get_product_sku":
        # Extract the arguments to use in your API call
        product_name = (
            response.candidates[0].content.parts[0].function_call.args["product_name"]
        )
        product_name

        # Here you can use your preferred method to make an API request to retrieve the product SKU, as in:
        # api_response = requests.post(product_api_url, data={"product_name": product_name})

        # In this example, we'll use synthetic data to simulate a response payload from an external API
        api_response = {"sku": "GA04834-US", "in_stock": "yes"}

    # Return the API response to Gemini so it can generate a model response or request another function call
    response = chat.send_message(
        Part.from_function_response(
            name="get_product_sku",
            response={
                "content": api_response,
            },
        ),
    )

    # Extract the text from the summary response
    summary = response.candidates[0].content.parts[0].text
    summaries.append(summary)

    # Send a prompt for the second conversation turn that should invoke the get_store_location function
    prompt = "Is there a store in Mountain View, CA that I can visit to try it out?"
    response = chat.send_message(prompt)
    prompts.append(prompt)

    # Check the function name that the model responded with, and make an API call to an external system
    if (
        response.candidates[0].content.parts[0].function_call.name
        == "get_store_location"
    ):
        # Extract the arguments to use in your API call
        location = (
            response.candidates[0].content.parts[0].function_call.args["location"]
        )
        location

        # Here you can use your preferred method to make an API request to retrieve store location closest to the user, as in:
        # api_response = requests.post(store_api_url, data={"location": location})

        # In this example, we'll use synthetic data to simulate a response payload from an external API
        api_response = {"store": "2000 N Shoreline Blvd, Mountain View, CA 94043, US"}

    # Return the API response to Gemini so it can generate a model response or request another function call
    response = chat.send_message(
        Part.from_function_response(
            name="get_store_location",
            response={
                "content": api_response,
            },
        ),
    )

    # Extract the text from the summary response
    summary = response.candidates[0].content.parts[0].text
    summaries.append(summary)

    return prompts, summaries

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