}},
{'type': 'text', 'text': 'The calculation of 3 to the power of 3 is 27.'}]
```
### 计算机使用
Gemini 2.5 Computer Use 模型 (`gemini-2.5-computer-use-preview-10-2025`) 可以与浏览器环境交互以自动化 Web 任务，如点击、键入和滚动。
**预览模型限制**
Computer Use 模型处于预览阶段，可能会产生意外行为。始终监督自动化任务，避免用于敏感数据或关键操作。详见 [Gemini API 文档](https://ai.google.dev/gemini-api/docs/computer-use) 了解安全最佳实践。
```python Bind to model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import ChatGoogleGenerativeAI
model = ChatGoogleGenerativeAI(model="gemini-2.5-computer-use-preview-10-2025") # [!code highlight]
model_with_computer = model.bind_tools([{"computer_use": {}}]) # [!code highlight]
response = model_with_computer.invoke("Please navigate to example.com")
response.content_blocks
```
```python Use on invocation theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import ChatGoogleGenerativeAI
model = ChatGoogleGenerativeAI(model="gemini-2.5-computer-use-preview-10-2025") # [!code highlight]
response = model.invoke(
"Please navigate to example.com",
tools=[{"computer_use": {}}], # [!code highlight]
)
response.content_blocks
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[{'type': 'tool_call',
'id': '08a8b175-16ab-4861-8965-b736d5d4dd7e',
'name': 'open_web_browser',
'args': {}}]
```
您可以配置环境并排除特定的 UI 操作：
```python Advanced configuration theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import ChatGoogleGenerativeAI, Environment
model = ChatGoogleGenerativeAI(model="gemini-2.5-computer-use-preview-10-2025") # [!code highlight]
# Specify the environment (browser is default)
model_with_computer = model.bind_tools(
[{"computer_use": {"environment": Environment.ENVIRONMENT_BROWSER}}] # [!code highlight]
)
# Exclude specific UI actions
model_with_computer = model.bind_tools(
[
{
"computer_use": {
"environment": Environment.ENVIRONMENT_BROWSER,
"excludedPredefinedFunctions": [ # [!code highlight]
"drag_and_drop", # [!code highlight]
"key_combination", # [!code highlight]
], # [!code highlight]
}
}
]
)
response = model_with_computer.invoke("Search for Python tutorials")
```
模型返回 UI 操作的函数调用（如 `click_at`, `type_text_at`, `scroll`），带有标准化坐标。您需要在浏览器自动化框架中实现这些实际操作的实际执行。
## 安全设置
Gemini 模型具有默认的安全设置，可以覆盖。如果您收到大量 `'Safety Warnings'`，可以尝试调整模型的安全设置属性。例如，要关闭危险内容的阻止，您可以按以下方式构建 LLM：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import (
ChatGoogleGenerativeAI,
HarmBlockThreshold,
HarmCategory,
)
llm = ChatGoogleGenerativeAI(
model="gemini-3.1-pro-preview",
safety_settings={
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
},
)
```
有关可用类别和阈值的枚举，请参见 Google 的 [安全设置类型](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)。
## 上下文缓存
上下文缓存允许您存储和重用内容（例如 PDF、图像）以加快处理速度。`cached_content` 参数接受通过 Google Generative AI API 创建的缓存名称。
此示例缓存单个文件并对其进行查询。
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import time
from google import genai
from google.genai import types
from langchain.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
client = genai.Client()
# Upload file
file = client.files.upload(file="path/to/your/file")
while file.state.name == "PROCESSING":
time.sleep(2)
file = client.files.get(name=file.name)
# Create cache
model = "gemini-3.1-pro-preview"
cache = client.caches.create(
model=model,
config=types.CreateCachedContentConfig(
display_name="Cached Content",
system_instruction=(
"You are an expert content analyzer, and your job is to answer "
"the user's query based on the file you have access to."
),
contents=[file],
ttl="300s",
),
)
# Query with LangChain
llm = ChatGoogleGenerativeAI(
model=model,
cached_content=cache.name,
)
message = HumanMessage(content="Summarize the main points of the content.")
llm.invoke([message])
```
此示例使用 `Part` 缓存两个文件并一起查询它们。
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import time
from google import genai
from google.genai.types import CreateCachedContentConfig, Content, Part
from langchain.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
client = genai.Client()
# Upload files
file_1 = client.files.upload(file="./file1")
while file_1.state.name == "PROCESSING":
time.sleep(2)
file_1 = client.files.get(name=file_1.name)
file_2 = client.files.upload(file="./file2")
while file_2.state.name == "PROCESSING":
time.sleep(2)
file_2 = client.files.get(name=file_2.name)
# Create cache with multiple files
contents = [
Content(
role="user",
parts=[
Part.from_uri(file_uri=file_1.uri, mime_type=file_1.mime_type),
Part.from_uri(file_uri=file_2.uri, mime_type=file_2.mime_type),
],
)
]
model = "gemini-3.1-pro-preview"
cache = client.caches.create(
model=model,
config=CreateCachedContentConfig(
display_name="Cached Contents",
system_instruction=(
"You are an expert content analyzer, and your job is to answer "
"the user's query based on the files you have access to."
),
contents=contents,
ttl="300s",
),
)
# Query with LangChain
llm = ChatGoogleGenerativeAI(
model=model,
cached_content=cache.name,
)
message = HumanMessage(
content="Provide a summary of the key information across both files."
)
llm.invoke([message])
```
有关更多信息，请参见 Gemini API 文档上的 [上下文缓存](https://ai.google.dev/gemini-api/docs/caching?lang=python)。
## 响应元数据
从模型响应中访问响应元数据。
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")
response = llm.invoke("Hello!")
response.response_metadata
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{'prompt_feedback': {'block_reason': 0, 'safety_ratings': []},
'finish_reason': 'STOP',
'model_name': 'gemini-3.1-pro-preview',
'safety_ratings': [],
'model_provider': 'google_genai'}
```
***
## API 参考
有关所有功能和配置选项的详细文档，请前往 [`ChatGoogleGenerativeAI`](https://reference.langchain.com/python/langchain-google-genai/chat_models/ChatGoogleGenerativeAI) API 参考。
***
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/i18n\zh-CN\oss\python\integrations\chat\google_generative_ai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
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