Textantworten mit der Gemini API mit externen Funktionsaufrufen in einem Chatszenario generieren

Generieren Sie Textantworten mithilfe der Gemini API mit externen Funktionsaufrufen. Dieses Beispiel zeigt ein Chatszenario mit zwei Funktionen und zwei aufeinanderfolgenden Prompts.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

• Funktionsaufrufe

Codebeispiel

Python

Bevor Sie dieses Beispiel anwenden, folgen Sie den Python-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Python API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

import vertexai
from vertexai.generative_models import (
    FunctionDeclaration,
    GenerationConfig,
    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-001",
        generation_config=GenerationConfig(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

Nächste Schritte

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