使用系统说明

预览

本产品或功能须遵守服务专用条款的“通用服务条款”部分中的“非正式版产品条款”。 正式发布前产品和功能“按原样”提供，可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

系统说明类似于您在 LLM 接触到用户的任何进一步说明之前添加的序言。它可让用户根据其特定需求和使用情形来控制模型的行为。设置系统说明时，您可以为模型提供额外的上下文来了解任务、提供自定义程度更高的回答，并在用户与模型的完整交互中遵循特定的准则。对于开发者，可以在系统说明中指定产品级行为，与最终用户提供的提示分开。例如，您可以添加人设或角色、背景信息和格式设置指令等内容：

You are a friendly and helpful assistant.
Ensure your answers are complete, unless the user requests a more concise approach.
When generating code, offer explanations for code segments as necessary and maintain good coding practices.
When presented with inquiries seeking information, provide answers that reflect a deep understanding of the field, guaranteeing their correctness.
For any non-english queries, respond in the same language as the prompt unless otherwise specified by the user.
For prompts involving reasoning, provide a clear explanation of each step in the reasoning process before presenting the final answer.

以下 Gemini 模型支持系统指令：

• gemini-1.5-flash-001
• gemini-1.5-pro-001
• gemini-1.0-pro-002

如果您使用的是其他模型，请改为参阅分配角色。

您可以通过多种方式使用系统说明，包括：

• 定义人设或角色（例如，针对聊天机器人）
• 定义输出格式（Markdown、YAML 等）
• 定义输出风格和语气（例如详细程度、正式程度和目标阅读水平）
• 定义任务的目标或规则（例如，返回代码段而不带进一步说明）
• 为提示提供其他上下文（例如知识临界值）

如果设置了系统说明，则该说明会应用于整个请求。当提示中包含系统说明时，该说明适用于多个用户和模型轮流。虽然系统指令与提示内容是分开的，但它们仍然是整体提示的一部分，因此受标准数据使用政策的约束。

注意：系统说明有助于指导模型遵循说明操作，但并不能完全防止越狱或数据泄露。我们建议您在系统说明中添加任何敏感信息时要谨慎行事。

代码示例

以下标签页中的代码示例演示了如何在生成式 AI 应用中使用系统指令。

Python

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。 如需了解详情，请参阅 Vertex AI Python API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

import vertexai

from vertexai.generative_models import GenerativeModel

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"

vertexai.init(project=project_id, location="us-central1")

model = GenerativeModel(
    model_name="gemini-1.5-flash-001",
    system_instruction=[
        "You are a helpful language translator.",
        "Your mission is to translate text in English to French.",
    ],
)

prompt = """
User input: I like bagels.
Answer:
"""

contents = [prompt]

response = model.generate_content(contents)
print(response.text)

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。 如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

const {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function set_system_instruction(projectId = 'PROJECT_ID') {
  const vertexAI = new VertexAI({project: projectId, location: 'us-central1'});

  const generativeModel = vertexAI.getGenerativeModel({
    model: 'gemini-1.5-pro-preview-0409',
    systemInstruction: {
      parts: [
        {text: 'You are a helpful language translator.'},
        {text: 'Your mission is to translate text in English to French.'},
      ],
    },
  });

  const textPart = {
    text: `
    User input: I like bagels.
    Answer:`,
  };

  const request = {
    contents: [{role: 'user', parts: [textPart]}],
  };

  const resp = await generativeModel.generateContent(request);
  const contentResponse = await resp.response;
  console.log(JSON.stringify(contentResponse));
}

C#

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 C# 设置说明执行操作。 如需了解详情，请参阅 Vertex AI C# API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;

public class SystemInstruction
{
    public async Task<string> SetSystemInstruction(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001")
    {

        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        string prompt = @"User input: I like bagels.
Answer:";

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts =
                    {
                        new Part { Text = prompt },
                    }
                }
            },
            SystemInstruction = new()
            {
                Parts =
                {
                    new Part { Text = "You are a helpful assistant." },
                    new Part { Text = "Your mission is to translate text in English to French." },
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

提示示例

以下是使用适用于 Gemini API 的 Python SDK 设置系统指令的基本示例：

model=genai.GenerativeModel(
    model_name="gemini-1.5-pro-001",
    system_instruction="You are a cat. Your name is Neko.")

以下是定义模型预期行为的系统提示示例。

生成代码

代码生成

系统指令： You are a coding expert that specializes in rendering code for front-end interfaces. When I describe a component of a website I want to build, please return the HTML and CSS needed to do so. Do not give an explanation for this code. Also offer some UI design suggestions. 提示： Create a box in the middle of the page that contains a rotating selection of images each with a caption. The image in the center of the page should have shadowing behind it to make it stand out. It should also link to another page of the site. Leave the URL blank so that I can fill it in.

生成已设置格式的数据

生成已设置格式的数据

系统指令： You are an assistant for home cooks. You receive a list of ingredients and respond with a list of recipes that use those ingredients. Recipes which need no extra ingredients should always be listed before those that do. Your response must be a JSON object containing 3 recipes. A recipe object has the following schema: * name: The name of the recipe * usedIngredients: Ingredients in the recipe that were provided in the list * otherIngredients: Ingredients in the recipe that were not provided in the list (omitted if there are no other ingredients) * description: A brief description of the recipe, written positively as if to sell it 提示： * 1 lb bag frozen broccoli * 1 pint heavy cream * 1 lb pack cheese ends and pieces

音乐聊天机器人

音乐聊天机器人

系统指令： You will respond as a music historian, demonstrating comprehensive knowledge across diverse musical genres and providing relevant examples. Your tone will be upbeat and enthusiastic, spreading the joy of music. If a question is not related to music, the response should be, "That is beyond my knowledge." 提示： If a person was born in the sixties, what was the most popular music genre being played when they were born? List five songs by bullet point.

金融分析

金融分析

系统指令： As a financial analysis expert, your role is to interpret complex financial data, offer personalized advice, and evaluate investments using statistical methods to gain insights across different financial areas. Accuracy is the top priority. All information, especially numbers and calculations, must be correct and reliable. Always double-check for errors before giving a response. The way you respond should change based on what the user needs. For tasks with calculations or data analysis, focus on being precise and following instructions rather than giving long explanations. If you're unsure, ask the user for more information to ensure your response meets their needs. For tasks that are not about numbers: * Use clear and simple language to avoid confusion and don't use jargon. * Make sure you address all parts of the user's request and provide complete information. * Think about the user's background knowledge and provide additional context or explanation when needed. Formatting and Language: * Follow any specific instructions the user gives about formatting or language. * Use proper formatting like JSON or tables to make complex data or results easier to understand. 提示： Please summarize the key insights of given numerical tables. CONSOLIDATED STATEMENTS OF INCOME (In millions, except per share amounts) |Year Ended December 31 | 2020 | 2021 | 2022 | |--- | --- | --- | --- | |Revenues | $ 182,527| $ 257,637| $ 282,836| |Costs and expenses:| |Cost of revenues | 84,732 | 110,939 | 126,203| |Research and development | 27,573 | 31,562 | 39,500| |Sales and marketing | 17,946 | 22,912 | 26,567| |General and administrative | 11,052 | 13,510 | 15,724| |Total costs and expenses | 141,303| 178,923| 207,994| |Income from operations | 41,224 | 78,714 | 74,842| |Other income (expense), net | 6,858 | 12,020 | (3,514)| |Income before income taxes | 48,082 | 90,734 | 71,328| |Provision for income taxes | 7,813 | 14,701 | 11,356| |Net income | $40,269| $76,033 | $59,972| |Basic net income per share of Class A, Class B, and Class C stock | $2.96| $5.69| $4.59| |Diluted net income per share of Class A, Class B, and Class C stock| $2.93| $5.61| $4.56| Please list important, but no more than five, highlights from 2020 to 2022 in the given table. Please write in a professional and business-neutral tone. The summary should only be based on the information presented in the table.

后续步骤

• 在问题库中探索更多问题示例。