Membuat respons teks menggunakan Gemini API dengan panggilan fungsi eksternal dalam skenario chat

Buat respons teks menggunakan Gemini API dengan panggilan fungsi eksternal. Contoh ini menunjukkan skenario chat dengan dua fungsi dan dua perintah berurutan.

Jelajahi lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

• Panggilan fungsi

Contoh kode

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Langkah selanjutnya

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