}},
{'type': 'text', 'text': 'The calculation of 3 to the power of 3 is 27.'}]
```
### Computer use
The Gemini 2.5 Computer Use model (`gemini-2.5-computer-use-preview-10-2025`) can interact with browser environments to automate web tasks like clicking, typing, and scrolling.
**Preview model limitations**
The Computer Use model is in preview and may produce unexpected behavior. Always supervise automated tasks and avoid use with sensitive data or critical operations. See the [Gemini API docs](https://ai.google.dev/gemini-api/docs/computer-use) for safety best practices.
```python Bind to model theme={null}
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={null}
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={null}
[{'type': 'tool_call',
'id': '08a8b175-16ab-4861-8965-b736d5d4dd7e',
'name': 'open_web_browser',
'args': {}}]
```
You can configure the environment and exclude specific UI actions:
```python Advanced configuration theme={null}
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")
```
The model returns function calls for UI actions (like `click_at`, `type_text_at`, `scroll`) with normalized coordinates. You'll need to implement the actual execution of these actions in your browser automation framework.
## Safety settings
Gemini models have default safety settings that can be overridden. If you are receiving lots of `'Safety Warnings'` from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:
```python theme={null}
from langchain_google_genai import (
ChatGoogleGenerativeAI,
HarmBlockThreshold,
HarmCategory,
)
llm = ChatGoogleGenerativeAI(
model="gemini-3.5-flash",
safety_settings={
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
},
)
```
For an enumeration of the categories and thresholds available, see Google's [safety settings guide](https://ai.google.dev/gemini-api/docs/safety-settings).
## Context caching
Context caching allows you to store and reuse content (e.g., PDFs, images) for faster processing. The `cached_content` parameter accepts a cache name created via the Google Generative AI API.
This caches a single file and queries it.
```python theme={null}
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-3.5-flash"
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])
```
This caches two files using `Part` and queries them together.
```python theme={null}
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-3.5-flash"
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])
```
See the Gemini API docs on [context caching](https://ai.google.dev/gemini-api/docs/caching?lang=python) for more information.
## Response metadata
Access response metadata from the model response.
```python theme={null}
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(model="gemini-3.5-flash")
response = llm.invoke("Hello!")
response.response_metadata
```
```text theme={null}
{'prompt_feedback': {'block_reason': 0, 'safety_ratings': []},
'finish_reason': 'STOP',
'model_name': 'gemini-3.5-flash',
'safety_ratings': [],
'model_provider': 'google_genai'}
```
***
## API reference
For detailed documentation of all features and configuration options, head to the [`ChatGoogleGenerativeAI`](https://reference.langchain.com/python/langchain-google-genai/chat_models/ChatGoogleGenerativeAI) API reference.
***
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