借助 Gemini 2.5 电脑使用预览版模型和工具,您可以构建与浏览器互动并自动执行任务的浏览器控制代理。借助屏幕截图,计算机使用模型可以“看到”计算机屏幕,并通过生成特定的界面操作(如鼠标点击和键盘输入)来“行动”。与函数调用类似,您需要编写客户端应用代码来接收和执行“使用电脑”操作。
借助“使用电脑”,您可以构建可执行以下操作的代理:
- 自动执行网站上重复的数据输入或表单填写操作。
- 自动测试 Web 应用和用户流程
- 在各种网站上进行研究(例如,从电子商务网站收集产品信息、价格和评价,以便做出购买决策)
测试 Gemini Computer Use 模型的最简单方法是通过参考实现或 Browserbase 演示环境。
“电脑使用”功能的运作方式
如需使用计算机使用模型构建浏览器控制代理,请实现一个执行以下操作的代理循环:
-
- 将“电脑使用情况”工具以及任何自定义的用户定义函数或排除的函数添加到您的 API 请求中。
- 使用用户的请求和表示当前 GUI 状态的屏幕截图来提示“计算机使用”模型。
-
- 计算机使用模型会分析用户请求和屏幕截图,并生成包含建议
function_call
的回答,该建议表示界面操作(例如,“在坐标 (x,y) 处点击”或“输入‘文本’”。如需查看“电脑使用”模型支持的所有界面操作的说明,请参阅支持的操作。 - API 响应还可能包含来自内部安全系统的
safety_decision
,该系统会检查模型提议的操作。此safety_decision
将操作归类为:- 常规 / 允许:该操作被视为安全操作。这也可以通过不存在
safety_decision
来表示。 - 需要确认 (
require_confirmation
):模型即将执行可能存在风险的操作(例如,点击“接受 Cookie 横幅”)。
- 常规 / 允许:该操作被视为安全操作。这也可以通过不存在
- 计算机使用模型会分析用户请求和屏幕截图,并生成包含建议
-
- 您的客户端代码会收到
function_call
和任何随附的safety_decision
。- 常规 / 允许:如果
safety_decision
指示常规/允许(或者如果不存在safety_decision
),您的客户端代码可以在目标环境(例如 Web 浏览器)中执行指定的function_call
。 - 需要确认:如果
safety_decision
指示需要确认,您的应用必须在执行function_call
之前提示最终用户进行确认。如果用户确认,则继续执行相应操作。如果用户拒绝,则不执行该操作。
- 常规 / 允许:如果
- 您的客户端代码会收到
-
- 如果操作已执行,客户端会捕获 GUI 和当前网址的新屏幕截图,并将其作为
function_response
的一部分发送回 Computer Use 模型。 - 如果某项操作被安全系统阻止或被用户拒绝确认,您的应用可能会向模型发送其他形式的反馈,或者结束互动。
- 如果操作已执行,客户端会捕获 GUI 和当前网址的新屏幕截图,并将其作为
此流程会从第 2 步开始重复执行,使用新的屏幕截图和正在进行的目标,通过电脑使用模型建议下一步操作。该循环会一直持续,直到任务完成、发生错误或进程终止(例如,由于“屏蔽”安全响应或用户决定)。
如何实现电脑使用
在使用“电脑使用”模型和工具进行构建之前,您需要设置以下内容:
- 安全执行环境:出于安全考虑,您应在安全且受控的环境中运行“电脑使用情况”代理(例如,沙盒化的虚拟机、容器或权限有限的专用浏览器配置文件)。
- 客户端操作处理程序:您需要实现客户端逻辑,以执行模型生成的操作,并在每次操作后捕获环境的屏幕截图。
本部分中的示例使用浏览器作为执行环境,并使用 Playwright 作为客户端操作处理程序。如需运行这些示例,您必须安装必要的依赖项并初始化 Playwright 浏览器实例。
安装 Playwright
pip install google-genai playwright playwright install chromium
初始化 Playwright 浏览器实例
from playwright.sync_api import sync_playwright # 1. Configure screen dimensions for the target environment SCREEN_WIDTH = 1440 SCREEN_HEIGHT = 900 # 2. Start the Playwright browser # In production, utilize a sandboxed environment. playwright = sync_playwright().start() # Set headless=False to see the actions performed on your screen browser = playwright.chromium.launch(headless=False) # 3. Create a context and page with the specified dimensions context = browser.new_context( viewport={"width": SCREEN_WIDTH, "height": SCREEN_HEIGHT} ) page = context.new_page() # 4. Navigate to an initial page to start the task page.goto("https://www.google.com") # The 'page', 'SCREEN_WIDTH', and 'SCREEN_HEIGHT' variables # will be used in the steps below.
有关如何扩展到 Android 环境的示例代码包含在使用自定义用户定义函数部分中。
1. 向模型发送请求
将“电脑使用”工具添加到 API 请求中,并向“电脑使用”模型发送提示,其中包含用户的目标和 GUI 的初始屏幕截图。您必须使用 Gemini 电脑使用情况模型,即 gemini-2.5-computer-use-preview-10-2025
。如果您尝试将“电脑使用情况”工具用于其他型号,则会收到错误消息。
您还可以选择性地添加以下参数:
- 排除的操作:如果您不希望模型执行支持的界面操作列表中的任何操作,请将这些操作指定为
excluded_predefined_functions
。 - 用户定义的函数:除了“电脑使用情况”工具之外,您可能还想添加自定义的用户定义的函数。
请注意,发出请求时无需指定显示屏尺寸;模型会预测缩放到屏幕高度和宽度的像素坐标。
Python
from google import genai
from google.genai import types
from google.genai.types import Content, Part
client = genai.Client()
# Specify predefined functions to exclude (optional)
excluded_functions = ["drag_and_drop"]
generate_content_config = genai.types.GenerateContentConfig(
tools=[
# 1. Computer Use tool with browser environment
types.Tool(
computer_use=types.ComputerUse(
environment=types.Environment.ENVIRONMENT_BROWSER,
# Optional: Exclude specific predefined functions
excluded_predefined_functions=excluded_functions
)
),
# 2. Optional: Custom user-defined functions
#types.Tool(
# function_declarations=custom_functions
# )
],
)
# Create the content with user message
contents=[
Content(
role="user",
parts=[
Part(text="Search for highly rated smart fridges with touchscreen, 2 doors, around 25 cu ft, priced below 4000 dollars on Google Shopping. Create a bulleted list of the 3 cheapest options in the format of name, description, price in an easy-to-read layout."),
# Optional: include a screenshot of the initial state
#Part.from_bytes(
#data=screenshot_image_bytes,
#mime_type='image/png',
#),
],
)
]
# Generate content with the configured settings
response = client.models.generate_content(
model='gemini-2.5-computer-use-preview-10-2025',
contents=contents,
config=generate_content_config,
)
# Print the response output
print(response)
如需查看包含自定义函数的示例,请参阅使用自定义用户定义的函数。
2. 接收模型回答
如果计算机使用模型确定需要执行界面操作才能完成任务,则会使用一个或多个 FunctionCalls
进行响应。电脑使用支持并行函数调用,这意味着模型可以在单个回合中返回多个操作。
以下是模型响应示例。
{
"content": {
"parts": [
{
"text": "I will type the search query into the search bar. The search bar is in the center of the page."
},
{
"function_call": {
"name": "type_text_at",
"args": {
"x": 371,
"y": 470,
"text": "highly rated smart fridges with touchscreen, 2 doors, around 25 cu ft, priced below 4000 dollars on Google Shopping",
"press_enter": true
}
}
}
]
}
}
3. 执行收到的操作
您的应用代码需要解析模型响应、执行操作并收集结果。
以下示例代码从“电脑使用”模型回答中提取函数调用,并将其转换为可使用 Playwright 执行的操作。无论输入图像的尺寸如何,模型都会输出归一化坐标 (0-999),因此转换步骤的一部分是将这些归一化坐标转换回实际像素值。
建议将屏幕尺寸设置为 (1440, 900),以便与“电脑使用”模型搭配使用。该模型适用于任何分辨率,但结果的质量可能会受到影响。
此示例仅包含 3 种最常见的界面操作的实现:open_web_browser
、click_at
和 type_text_at
。对于生产用例,您需要实现支持的操作列表中的所有其他界面操作,除非您明确将它们添加为 excluded_predefined_functions
。
Python
from typing import Any, List, Tuple
import time
def denormalize_x(x: int, screen_width: int) -> int:
"""Convert normalized x coordinate (0-1000) to actual pixel coordinate."""
return int(x / 1000 * screen_width)
def denormalize_y(y: int, screen_height: int) -> int:
"""Convert normalized y coordinate (0-1000) to actual pixel coordinate."""
return int(y / 1000 * screen_height)
def execute_function_calls(candidate, page, screen_width, screen_height):
results = []
function_calls = []
for part in candidate.content.parts:
if part.function_call:
function_calls.append(part.function_call)
for function_call in function_calls:
action_result = {}
fname = function_call.name
args = function_call.args
print(f" -> Executing: {fname}")
try:
if fname == "open_web_browser":
pass # Already open
elif fname == "click_at":
actual_x = denormalize_x(args["x"], screen_width)
actual_y = denormalize_y(args["y"], screen_height)
page.mouse.click(actual_x, actual_y)
elif fname == "type_text_at":
actual_x = denormalize_x(args["x"], screen_width)
actual_y = denormalize_y(args["y"], screen_height)
text = args["text"]
press_enter = args.get("press_enter", False)
page.mouse.click(actual_x, actual_y)
# Simple clear (Command+A, Backspace for Mac)
page.keyboard.press("Meta+A")
page.keyboard.press("Backspace")
page.keyboard.type(text)
if press_enter:
page.keyboard.press("Enter")
else:
print(f"Warning: Unimplemented or custom function {fname}")
# Wait for potential navigations/renders
page.wait_for_load_state(timeout=5000)
time.sleep(1)
except Exception as e:
print(f"Error executing {fname}: {e}")
action_result = {"error": str(e)}
results.append((fname, action_result))
return results
# Execute function calls
candidate = response.candidates[0]
results = execute_function_calls(response.candidates[0], page, SCREEN_WIDTH, SCREEN_HEIGHT)
4. 捕获新环境状态
执行操作后,将函数执行结果发送回模型,以便模型可以使用此信息生成下一个操作。如果执行了多项操作(并行调用),您必须在后续用户回合中为每项操作发送一个 FunctionResponse
。
Python
def get_function_responses(page, results):
screenshot_bytes = page.screenshot(type="png")
current_url = page.url
function_responses = []
for name, result in results:
response_data = {"url": current_url}
response_data.update(result)
function_responses.append(
types.FunctionResponse(
name=name,
response=response_data,
parts=[types.FunctionResponsePart(
inline_data=types.FunctionResponseBlob(
mime_type="image/png",
data=screenshot_bytes))
]
)
)
return function_responses
# Capture state and return to model
function_responses = get_function_responses(page, results)
user_feedback_content = Content(
role="user",
parts=[Part(function_response=fr) for fr in function_responses])
# Append this feedback to the 'contents' history list for the next API call.
contents.append(user_feedback_content)
构建代理循环
如需实现多步互动,请将如何实现计算机使用部分中的四个步骤组合成一个循环。请务必通过附加模型响应和函数响应来正确管理对话历史记录。
如需运行此代码示例,您需要:
- 安装必要的 Playwright 依赖项。
定义步骤 (3) 执行接收到的操作和 (4) 捕获新的环境状态中的辅助函数。
Python
import time
from typing import Any, List, Tuple
from playwright.sync_api import sync_playwright
from google import genai
from google.genai import types
from google.genai.types import Content, Part
client = genai.Client()
# Constants for screen dimensions
SCREEN_WIDTH = 1440
SCREEN_HEIGHT = 900
# Setup Playwright
print("Initializing browser...")
playwright = sync_playwright().start()
browser = playwright.chromium.launch(headless=False)
context = browser.new_context(viewport={"width": SCREEN_WIDTH, "height": SCREEN_HEIGHT})
page = context.new_page()
# Define helper functions. Copy/paste from steps 3 and 4
# def denormalize_x(...)
# def denormalize_y(...)
# def execute_function_calls(...)
# def get_function_responses(...)
try:
# Go to initial page
page.goto("https://ai.google.dev/gemini-api/docs")
# Configure the model (From Step 1)
config = types.GenerateContentConfig(
tools=[types.Tool(computer_use=types.ComputerUse(
environment=types.Environment.ENVIRONMENT_BROWSER
))],
thinking_config=types.ThinkingConfig(include_thoughts=True),
)
# Initialize history
initial_screenshot = page.screenshot(type="png")
USER_PROMPT = "Go to ai.google.dev/gemini-api/docs and search for pricing."
print(f"Goal: {USER_PROMPT}")
contents = [
Content(role="user", parts=[
Part(text=USER_PROMPT),
Part.from_bytes(data=initial_screenshot, mime_type='image/png')
])
]
# Agent Loop
turn_limit = 5
for i in range(turn_limit):
print(f"\n--- Turn {i+1} ---")
print("Thinking...")
response = client.models.generate_content(
model='gemini-2.5-computer-use-preview-10-2025',
contents=contents,
config=config,
)
candidate = response.candidates[0]
contents.append(candidate.content)
has_function_calls = any(part.function_call for part in candidate.content.parts)
if not has_function_calls:
text_response = " ".join([part.text for part in candidate.content.parts if part.text])
print("Agent finished:", text_response)
break
print("Executing actions...")
results = execute_function_calls(candidate, page, SCREEN_WIDTH, SCREEN_HEIGHT)
print("Capturing state...")
function_responses = get_function_responses(page, results)
contents.append(
Content(role="user", parts=[Part(function_response=fr) for fr in function_responses])
)
finally:
# Cleanup
print("\nClosing browser...")
browser.close()
playwright.stop()
使用自定义用户定义的函数
您可以选择在请求中添加自定义的用户定义函数,以扩展模型的功能。以下示例通过包含自定义的用户定义操作(例如 open_app
、long_press_at
和 go_home
)并排除特定于浏览器的操作,使计算机使用模型和工具适应移动设备使用情形。该模型可以智能地调用这些自定义函数以及标准界面操作,以便在非浏览器环境中完成任务。
Python
from typing import Optional, Dict, Any
from google import genai
from google.genai import types
from google.genai.types import Content, Part
client = genai.Client()
SYSTEM_PROMPT = """You are operating an Android phone. Today's date is October 15, 2023, so ignore any other date provided.
* To provide an answer to the user, *do not use any tools* and output your answer on a separate line. IMPORTANT: Do not add any formatting or additional punctuation/text, just output the answer by itself after two empty lines.
* Make sure you scroll down to see everything before deciding something isn't available.
* You can open an app from anywhere. The icon doesn't have to currently be on screen.
* Unless explicitly told otherwise, make sure to save any changes you make.
* If text is cut off or incomplete, scroll or click into the element to get the full text before providing an answer.
* IMPORTANT: Complete the given task EXACTLY as stated. DO NOT make any assumptions that completing a similar task is correct. If you can't find what you're looking for, SCROLL to find it.
* If you want to edit some text, ONLY USE THE `type` tool. Do not use the onscreen keyboard.
* Quick settings shouldn't be used to change settings. Use the Settings app instead.
* The given task may already be completed. If so, there is no need to do anything.
"""
def open_app(app_name: str, intent: Optional[str] = None) -> Dict[str, Any]:
"""Opens an app by name.
Args:
app_name: Name of the app to open (any string).
intent: Optional deep-link or action to pass when launching, if the app supports it.
Returns:
JSON payload acknowledging the request (app name and optional intent).
"""
return {"status": "requested_open", "app_name": app_name, "intent": intent}
def long_press_at(x: int, y: int) -> Dict[str, int]:
"""Long-press at a specific screen coordinate.
Args:
x: X coordinate (absolute), scaled to the device screen width (pixels).
y: Y coordinate (absolute), scaled to the device screen height (pixels).
Returns:
Object with the coordinates pressed and the duration used.
"""
return {"x": x, "y": y}
def go_home() -> Dict[str, str]:
"""Navigates to the device home screen.
Returns:
A small acknowledgment payload.
"""
return {"status": "home_requested"}
# Build function declarations
CUSTOM_FUNCTION_DECLARATIONS = [
types.FunctionDeclaration.from_callable(client=client, callable=open_app),
types.FunctionDeclaration.from_callable(client=client, callable=long_press_at),
types.FunctionDeclaration.from_callable(client=client, callable=go_home),
]
#Exclude browser functions
EXCLUDED_PREDEFINED_FUNCTIONS = [
"open_web_browser",
"search",
"navigate",
"hover_at",
"scroll_document",
"go_forward",
"key_combination",
"drag_and_drop",
]
#Utility function to construct a GenerateContentConfig
def make_generate_content_config() -> genai.types.GenerateContentConfig:
"""Return a fixed GenerateContentConfig with Computer Use + custom functions."""
return genai.types.GenerateContentConfig(
system_instruction=SYSTEM_PROMPT,
tools=[
types.Tool(
computer_use=types.ComputerUse(
environment=types.Environment.ENVIRONMENT_BROWSER,
excluded_predefined_functions=EXCLUDED_PREDEFINED_FUNCTIONS,
)
),
types.Tool(function_declarations=CUSTOM_FUNCTION_DECLARATIONS),
],
)
# Create the content with user message
contents: list[Content] = [
Content(
role="user",
parts=[
# text instruction
Part(text="Open Chrome, then long-press at 200,400."),
# optional screenshot attachment
Part.from_bytes(
data=screenshot_image_bytes,
mime_type="image/png",
),
],
)
]
# Build your fixed config (from helper)
config = make_generate_content_config()
# Generate content with the configured settings
response = client.models.generate_content(
model='gemini-2.5-computer-use-preview-10-2025',
contents=contents,
config=config,
)
print(response)
支持的界面操作
计算机使用情况模型可以通过 FunctionCall
请求以下界面操作。您的客户端代码必须实现这些操作的执行逻辑。如需查看示例,请参阅参考实现。
命令名称 | 说明 | 实参(在函数调用中) | 函数调用示例 |
---|---|---|---|
open_web_browser | 打开网络浏览器。 | 无 | {"name": "open_web_browser", "args": {}} |
wait_5_seconds | 暂停执行 5 秒,以便加载动态内容或完成动画。 | 无 | {"name": "wait_5_seconds", "args": {}} |
go_back | 前往浏览器历史记录中的上一页。 | 无 | {"name": "go_back", "args": {}} |
go_forward | 前往浏览器历史记录中的下一页。 | 无 | {"name": "go_forward", "args": {}} |
search | 前往默认搜索引擎的主页(例如,Google)。有助于启动新的搜索任务。 | 无 | {"name": "search", "args": {}} |
navigate | 直接将浏览器导航到指定网址。 | url :字符串 |
{"name": "navigate", "args": {"url": "https://www.wikipedia.org"}} |
click_at | 点击网页上特定坐标处的元素。x 值和 y 值基于 1000x1000 网格,并会缩放到屏幕尺寸。 | y :整数 (0-999),x :整数 (0-999) |
{"name": "click_at", "args": {"y": 300, "x": 500}} |
hover_at | 将鼠标悬停在网页上的特定坐标处。可用于显示子菜单。x 和 y 基于 1000x1000 网格。 | y :整数 (0-999) x :整数 (0-999) |
{"name": "hover_at", "args": {"y": 150, "x": 250}} |
type_text_at | 在特定坐标处输入文字,默认情况下先清空字段,然后在输入完毕后按 Enter 键,但这些操作可以停用。x 和 y 基于 1000x1000 网格。 | y :int (0-999),x :int (0-999),text :str,press_enter :bool(可选,默认值为 True),clear_before_typing :bool(可选,默认值为 True) |
{"name": "type_text_at", "args": {"y": 250, "x": 400, "text": "search query", "press_enter": false}} |
key_combination | 按键盘按键或组合键,例如“Ctrl+C”或“Enter”。可用于触发操作(例如使用“Enter”键提交表单)或剪贴板操作。 | keys :字符串(例如“enter”“control+c”)。 |
{"name": "key_combination", "args": {"keys": "Control+A"}} |
scroll_document | 向上、向下、向左或向右滚动整个网页。 | direction :字符串(“上”“下”“左”或“右”) |
{"name": "scroll_document", "args": {"direction": "down"}} |
scroll_at | 按指定方向以一定幅度滚动坐标 (x, y) 处的特定元素或区域。坐标和大小(默认值为 800)基于 1000x1000 网格。 | y :int (0-999),x :int (0-999),direction :str(“上”“下”“左”“右”),magnitude :int(0-999,可选,默认值为 800) |
{"name": "scroll_at", "args": {"y": 500, "x": 500, "direction": "down", "magnitude": 400}} |
drag_and_drop | 从起始坐标 (x, y) 拖动元素,并将其放置在目标坐标 (destination_x, destination_y) 处。所有坐标均基于 1000x1000 网格。 | y :int (0-999),x :int (0-999),destination_y :int (0-999),destination_x :int (0-999) |
{"name": "drag_and_drop", "args": {"y": 100, "x": 100, "destination_y": 500, "destination_x": 500}} |
安全
确认安全决定
根据操作的不同,模型回答可能还会包含来自内部安全系统的 safety_decision
,该系统会检查模型提出的操作。
{
"content": {
"parts": [
{
"text": "I have evaluated step 2. It seems Google detected unusual traffic and is asking me to verify I'm not a robot. I need to click the 'I'm not a robot' checkbox located near the top left (y=98, x=95).",
},
{
"function_call": {
"name": "click_at",
"args": {
"x": 60,
"y": 100,
"safety_decision": {
"explanation": "I have encountered a CAPTCHA challenge that requires interaction. I need you to complete the challenge by clicking the 'I'm not a robot' checkbox and any subsequent verification steps.",
"decision": "require_confirmation"
}
}
}
}
]
}
}
如果 safety_decision
为 require_confirmation
,您必须先征得最终用户的确认,然后才能继续执行相应操作。根据服务条款,您不得绕过人工确认请求。
此代码示例会在执行操作之前提示最终用户进行确认。如果用户未确认该操作,则循环终止。如果用户确认该操作,则系统会执行该操作,并将 safety_acknowledgement
字段标记为 True
。
Python
import termcolor
def get_safety_confirmation(safety_decision):
"""Prompt user for confirmation when safety check is triggered."""
termcolor.cprint("Safety service requires explicit confirmation!", color="red")
print(safety_decision["explanation"])
decision = ""
while decision.lower() not in ("y", "n", "ye", "yes", "no"):
decision = input("Do you wish to proceed? [Y]es/[N]o\n")
if decision.lower() in ("n", "no"):
return "TERMINATE"
return "CONTINUE"
def execute_function_calls(candidate, page, screen_width, screen_height):
# ... Extract function calls from response ...
for function_call in function_calls:
extra_fr_fields = {}
# Check for safety decision
if 'safety_decision' in function_call.args:
decision = get_safety_confirmation(function_call.args['safety_decision'])
if decision == "TERMINATE":
print("Terminating agent loop")
break
extra_fr_fields["safety_acknowledgement"] = "true" # Safety acknowledgement
# ... Execute function call and append to results ...
如果用户确认,您必须在 FunctionResponse
中添加安全确认信息。
Python
function_response_parts.append(
FunctionResponse(
name=name,
response={"url": current_url,
**extra_fr_fields}, # Include safety acknowledgement
parts=[
types.FunctionResponsePart(
inline_data=types.FunctionResponseBlob(
mime_type="image/png", data=screenshot
)
)
]
)
)
有关安全的最佳实践
Computer Use API 是一种新颖的 API,会带来一些新的风险,开发者应注意这些风险:
- 不可信的内容和诈骗:当模型尝试实现用户目标时,可能会依赖不可信的信息来源和屏幕上的指令。例如,如果用户的目标是购买 Pixel 手机,而模型遇到“完成调查即可免费获赠 Pixel”的诈骗,则模型很有可能会完成调查。
- 偶尔会发生意外操作:模型可能会误解用户的目标或网页内容,导致其采取错误的操作,例如点击错误的按钮或填写错误的表单。这可能会导致任务失败或数据泄露。
- 违反政策:该 API 的功能可能会被有意或无意地用于违反 Google 政策(《生成式 AI 使用限制政策》和《Gemini API 附加服务条款》)的活动。这包括可能会干扰系统完整性、危害安全性、绕过安全措施、控制医疗设备等的行为。
为应对这些风险,您可以实施以下安全措施和最佳实践:
人机协同 (HITL):
- 实现用户确认:当安全响应指示
require_confirmation
时,您必须在执行之前实现用户确认。有关示例代码,请参阅确认安全决策。 提供自定义安全说明:除了内置的用户确认检查之外,开发者还可以选择添加自定义系统指令,以强制执行自己的安全政策,从而阻止某些模型操作,或者要求用户在模型采取某些高风险的不可逆操作之前进行确认。以下是一个自定义安全系统指令的示例,您可以在与模型互动时添加该指令。
安全说明示例
将自定义安全规则设置为系统指令:
## **RULE 1: Seek User Confirmation (USER_CONFIRMATION)** This is your first and most important check. If the next required action falls into any of the following categories, you MUST stop immediately, and seek the user's explicit permission. **Procedure for Seeking Confirmation:** * **For Consequential Actions:** Perform all preparatory steps (e.g., navigating, filling out forms, typing a message). You will ask for confirmation **AFTER** all necessary information is entered on the screen, but **BEFORE** you perform the final, irreversible action (e.g., before clicking "Send", "Submit", "Confirm Purchase", "Share"). * **For Prohibited Actions:** If the action is strictly forbidden (e.g., accepting legal terms, solving a CAPTCHA), you must first inform the user about the required action and ask for their confirmation to proceed. **USER_CONFIRMATION Categories:** * **Consent and Agreements:** You are FORBIDDEN from accepting, selecting, or agreeing to any of the following on the user's behalf. You must ask the user to confirm before performing these actions. * Terms of Service * Privacy Policies * Cookie consent banners * End User License Agreements (EULAs) * Any other legally significant contracts or agreements. * **Robot Detection:** You MUST NEVER attempt to solve or bypass the following. You must ask the user to confirm before performing these actions. * CAPTCHAs (of any kind) * Any other anti-robot or human-verification mechanisms, even if you are capable. * **Financial Transactions:** * Completing any purchase. * Managing or moving money (e.g., transfers, payments). * Purchasing regulated goods or participating in gambling. * **Sending Communications:** * Sending emails. * Sending messages on any platform (e.g., social media, chat apps). * Posting content on social media or forums. * **Accessing or Modifying Sensitive Information:** * Health, financial, or government records (e.g., medical history, tax forms, passport status). * Revealing or modifying sensitive personal identifiers (e.g., SSN, bank account number, credit card number). * **User Data Management:** * Accessing, downloading, or saving files from the web. * Sharing or sending files/data to any third party. * Transferring user data between systems. * **Browser Data Usage:** * Accessing or managing Chrome browsing history, bookmarks, autofill data, or saved passwords. * **Security and Identity:** * Logging into any user account. * Any action that involves misrepresentation or impersonation (e.g., creating a fan account, posting as someone else). * **Insurmountable Obstacles:** If you are technically unable to interact with a user interface element or are stuck in a loop you cannot resolve, ask the user to take over. --- ## **RULE 2: Default Behavior (ACTUATE)** If an action does **NOT** fall under the conditions for `USER_CONFIRMATION`, your default behavior is to **Actuate**. **Actuation Means:** You MUST proactively perform all necessary steps to move the user's request forward. Continue to actuate until you either complete the non-consequential task or encounter a condition defined in Rule 1. * **Example 1:** If asked to send money, you will navigate to the payment portal, enter the recipient's details, and enter the amount. You will then **STOP** as per Rule 1 and ask for confirmation before clicking the final "Send" button. * **Example 2:** If asked to post a message, you will navigate to the site, open the post composition window, and write the full message. You will then **STOP** as per Rule 1 and ask for confirmation before clicking the final "Post" button. After the user has confirmed, remember to get the user's latest screen before continuing to perform actions. # Final Response Guidelines: Write final response to the user in the following cases: - User confirmation - When the task is complete or you have enough information to respond to the user
- 实现用户确认:当安全响应指示
安全执行环境:在安全的沙盒环境中运行代理,以限制其潜在影响(例如,沙盒化虚拟机 (VM)、容器(例如,Docker),或权限有限的专用浏览器个人资料)。
输入内容清理:清理提示中的所有用户生成文本,以降低意外指令或提示注入的风险。这是一个有用的安全层,但不能替代安全执行环境。
内容安全界限:使用安全界限和内容安全 API 来评估用户输入、工具输入和输出、代理的回答是否合适,以及检测提示注入和越狱。
许可名单和屏蔽名单:实现过滤机制,以控制模型可以访问的网站以及可以执行的操作。禁止访问的网站的屏蔽名单是一个不错的起点,而限制性更强的许可名单则更加安全。
可观测性和日志记录:维护详细的日志,以便进行调试、审核和突发事件响应。您的客户端应记录提示、屏幕截图、模型建议的操作 (function_call)、安全响应以及客户端最终执行的所有操作。
环境管理:确保 GUI 环境保持一致。 意外的弹出式窗口、通知或布局变化可能会让模型感到困惑。尽可能从已知干净状态开始执行每个新任务。
模型版本
属性 | 说明 |
---|---|
模型代码 |
Gemini API
|
支持的数据类型 |
输入 图片、文字 输出 文本 |
[*] | 令牌限制
输入 token 限制 128,000 输出 token 限制 64,000 |
版本 |
|
最新更新 | 2025 年 10 月 |
后续步骤
- 在 Browserbase 演示环境中尝试使用计算机。
- 如需查看示例代码,请参阅参考实现。
- 了解其他 Gemini API 工具: