bede/tfm_ainventory
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
51716faf8734fe2f085aeb3a5ca782f0b47e5b65
tfm_ainventory/venv/lib/python3.12/site-packages/google_genai-1.73.1.dist-info/METADATA
Daniel Bedeleanu ea49cd6e4a feat(phase1): add image storage utilities 
- Create backend/services/image_storage.py with 4 core functions:
  - sanitize_filename(): remove unsafe chars, limit to 255 chars, convert to lowercase
  - get_unique_filename(): handle collisions with UUID suffix (format: {name}_{uuid8}_{variant}.jpg)
  - ensure_image_directories(): create /images/ root and category subdirs on startup
  - save_image(): save bytes to /images/{category}/{filename}, returns relative path
- Create comprehensive test suite (22 tests) covering all functionality
- Integrate ensure_image_directories() into FastAPI startup event
- Directory structure: /images/{category}/{filename}
- Collision handling: auto-suffix with UUID if filename exists
- All tests passing, pathlib.Path for safe operations
2026-04-20 21:57:26 +03:00

2043 lines
51 KiB
Plaintext
Raw Blame History

Metadata-Version: 2.4
Name: google-genai
Version: 1.73.1
Summary: GenAI Python SDK
Author-email: Google LLC <googleapis-packages@google.com>
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/googleapis/python-genai
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: anyio<5.0.0,>=4.8.0
Requires-Dist: google-auth[requests]<3.0.0,>=2.48.1
Requires-Dist: httpx<1.0.0,>=0.28.1
Requires-Dist: pydantic<3.0.0,>=2.9.0
Requires-Dist: requests<3.0.0,>=2.28.1
Requires-Dist: tenacity<9.2.0,>=8.2.3
Requires-Dist: websockets<17.0,>=13.0.0
Requires-Dist: typing-extensions<5.0.0,>=4.14.0
Requires-Dist: distro<2,>=1.7.0
Requires-Dist: sniffio
Provides-Extra: aiohttp
Requires-Dist: aiohttp<4.0.0,>=3.10.11; extra == "aiohttp"
Provides-Extra: local-tokenizer
Requires-Dist: sentencepiece>=0.2.0; extra == "local-tokenizer"
Requires-Dist: protobuf; extra == "local-tokenizer"
Provides-Extra: pyopenssl
Requires-Dist: pyopenssl; extra == "pyopenssl"
Dynamic: license-file
# Google Gen AI SDK
[![PyPI version](https://img.shields.io/pypi/v/google-genai.svg)](https://pypi.org/project/google-genai/)
![Python support](https://img.shields.io/pypi/pyversions/google-genai)
[![PyPI - Downloads](https://img.shields.io/pypi/dw/google-genai)](https://pypistats.org/packages/google-genai)
--------
**Documentation:** https://googleapis.github.io/python-genai/
-----
Google Gen AI Python SDK provides an interface for developers to integrate
Google's generative models into their Python applications. It supports the
[Gemini Developer API](https://ai.google.dev/gemini-api/docs) and
[Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview)
APIs.
## Code Generation
Generative models are often unaware of recent API and SDK updates and may suggest outdated or legacy code.
We recommend using our Code Generation instructions [`codegen_instructions.md`](https://raw.githubusercontent.com/googleapis/python-genai/refs/heads/main/codegen_instructions.md) when generating Google Gen AI SDK code to guide your model towards using the more recent SDK features. Copy and paste the instructions into your development environment to provide the model with the necessary context.
## Installation
```sh
pip install google-genai
```
<small>With `uv`:</small>
```sh
uv pip install google-genai
```
## Imports
```python
from google import genai
from google.genai import types
```
## Create a client
Please run one of the following code blocks to create a client for
different services ([Gemini Developer API](https://ai.google.dev/gemini-api/docs) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview)).
```python
from google import genai
# Only run this block for Gemini Developer API
client = genai.Client(api_key='GEMINI_API_KEY')
```
```python
from google import genai
# Only run this block for Vertex AI API
client = genai.Client(
vertexai=True, project='your-project-id', location='us-central1'
)
```
## Using types
All API methods support Pydantic types and dictionaries, which you can access
from `google.genai.types`. You can import the types module with the following:
```python
from google.genai import types
```
Below is an example `generate_content()` call using types from the types module:
```python
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=types.Part.from_text(text='Why is the sky blue?'),
config=types.GenerateContentConfig(
temperature=0,
top_p=0.95,
top_k=20,
),
)
```
Alternatively, you can accomplish the same request using dictionaries instead of
types:
```python
response = client.models.generate_content(
model='gemini-2.5-flash',
contents={'text': 'Why is the sky blue?'},
config={
'temperature': 0,
'top_p': 0.95,
'top_k': 20,
},
)
```
**(Optional) Using environment variables:**
You can create a client by configuring the necessary environment variables.
Configuration setup instructions depends on whether you're using the Gemini
Developer API or the Gemini API in Vertex AI.
**Gemini Developer API:** Set the `GEMINI_API_KEY` or `GOOGLE_API_KEY`.
It will automatically be picked up by the client. It's recommended that you
set only one of those variables, but if both are set, `GOOGLE_API_KEY` takes
precedence.
```bash
export GEMINI_API_KEY='your-api-key'
```
**Gemini API on Vertex AI:** Set `GOOGLE_GENAI_USE_VERTEXAI`,
`GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION`, as shown below:
```bash
export GOOGLE_GENAI_USE_VERTEXAI=true
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='us-central1'
```
```python
from google import genai
client = genai.Client()
```
## Close a client
Explicitly close the sync client to ensure that resources, such as the
underlying HTTP connections, are properly cleaned up and closed.
```python
from google.genai import Client
client = Client()
response_1 = client.models.generate_content(
model=MODEL_ID,
contents='Hello',
)
response_2 = client.models.generate_content(
model=MODEL_ID,
contents='Ask a question',
)
# Close the sync client to release resources.
client.close()
```
To explicitly close the async client:
```python
from google.genai import Client
aclient = Client(
vertexai=True, project='my-project-id', location='us-central1'
).aio
response_1 = await aclient.models.generate_content(
model=MODEL_ID,
contents='Hello',
)
response_2 = await aclient.models.generate_content(
model=MODEL_ID,
contents='Ask a question',
)
# Close the async client to release resources.
await aclient.aclose()
```
## Client context managers
By using the sync client context manager, it will close the underlying
sync client when exiting the with block and avoid httpx "client has been closed" error like [issues#1763](https://github.com/googleapis/python-genai/issues/1763).
```python
from google.genai import Client
with Client() as client:
response_1 = client.models.generate_content(
model=MODEL_ID,
contents='Hello',
)
response_2 = client.models.generate_content(
model=MODEL_ID,
contents='Ask a question',
)
```
By using the async client context manager, it will close the underlying
async client when exiting the with block.
```python
from google.genai import Client
async with Client().aio as aclient:
response_1 = await aclient.models.generate_content(
model=MODEL_ID,
contents='Hello',
)
response_2 = await aclient.models.generate_content(
model=MODEL_ID,
contents='Ask a question',
)
```
### API Selection
By default, the SDK uses the beta API endpoints provided by Google to support
preview features in the APIs. The stable API endpoints can be selected by
setting the API version to `v1`.
To set the API version use `http_options`. For example, to set the API version
to `v1` for Vertex AI:
```python
from google import genai
from google.genai import types
client = genai.Client(
vertexai=True,
project='your-project-id',
location='us-central1',
http_options=types.HttpOptions(api_version='v1')
)
```
To set the API version to `v1alpha` for the Gemini Developer API:
```python
from google import genai
from google.genai import types
client = genai.Client(
api_key='GEMINI_API_KEY',
http_options=types.HttpOptions(api_version='v1alpha')
)
```
### Faster async client option: Aiohttp
By default we use httpx for both sync and async client implementations. In order
to have faster performance, you may install `google-genai[aiohttp]`. In Gen AI
SDK we configure `trust_env=True` to match with the default behavior of httpx.
Additional args of `aiohttp.ClientSession.request()` ([see `_RequestOptions` args](https://github.com/aio-libs/aiohttp/blob/v3.12.13/aiohttp/client.py#L170)) can be passed
through the following way:
```python
http_options = types.HttpOptions(
async_client_args={'cookies': ..., 'ssl': ...},
)
client=Client(..., http_options=http_options)
```
### Proxy
Both httpx and aiohttp libraries use `urllib.request.getproxies` from
environment variables. Before client initialization, you may set proxy (and
optional `SSL_CERT_FILE`) by setting the environment variables:
```bash
export HTTPS_PROXY='http://username:password@proxy_uri:port'
export SSL_CERT_FILE='client.pem'
```
If you need `socks5` proxy, httpx [supports](https://www.python-httpx.org/advanced/proxies/#socks) `socks5` proxy if you pass it via
args to `httpx.Client()`. You may install `httpx[socks]` to use it.
Then, you can pass it through the following way:
```python
http_options = types.HttpOptions(
client_args={'proxy': 'socks5://user:pass@host:port'},
async_client_args={'proxy': 'socks5://user:pass@host:port'},
)
client=Client(..., http_options=http_options)
```
### Custom base url
In some cases you might need a custom base url (for example, API gateway proxy
server) and bypass some authentication checks for project, location, or API key.
You may pass the custom base url like this:
```python
client = Client(
vertexai=True,
http_options=types.HttpOptionsDict(
base_url='https://test-api-gateway-proxy.com',
base_url_resource_scope=types.ResourceScope.COLLECTION,
),
)
response = client.models.generate_content(
model='gemini-3-pro-preview', contents='Why is the sky blue?'
)
```
If `base_url_resource_scope=types.ResourceScope.COLLECTION`, the resource name
will not include api version, project, or location.
Expected request url will be:
https://test-api-gateway-proxy.com/publishers/google/models/gemini-3-pro-preview
## Types
Parameter types can be specified as either dictionaries(`TypedDict`) or
[Pydantic Models](https://pydantic.readthedocs.io/en/stable/model.html).
Pydantic model types are available in the `types` module.
## Models
The `client.models` module exposes model inferencing and model getters.
See the 'Create a client' section above to initialize a client.
### Generate Content
#### with text content input (text output)
```python
response = client.models.generate_content(
model='gemini-2.5-flash', contents='Why is the sky blue?'
)
print(response.text)
```
#### with text content input (image output)
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash-image',
contents='A cartoon infographic for flying sneakers',
config=types.GenerateContentConfig(
response_modalities=["IMAGE"],
image_config=types.ImageConfig(
aspect_ratio="9:16",
),
),
)
for part in response.parts:
if part.inline_data:
generated_image = part.as_image()
generated_image.show()
```
#### with uploaded file (Gemini Developer API only)
Download the file in console.
```sh
!wget -q https://storage.googleapis.com/generativeai-downloads/data/a11.txt
```
python code.
```python
file = client.files.upload(file='a11.txt')
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=['Could you summarize this file?', file]
)
print(response.text)
```
#### How to structure `contents` argument for `generate_content`
The SDK always converts the inputs to the `contents` argument into
`list[types.Content]`.
The following shows some common ways to provide your inputs.
##### Provide a `list[types.Content]`
This is the canonical way to provide contents, SDK will not do any conversion.
##### Provide a `types.Content` instance
```python
from google.genai import types
contents = types.Content(
role='user',
parts=[types.Part.from_text(text='Why is the sky blue?')]
)
```
SDK converts this to
```python
[
types.Content(
role='user',
parts=[types.Part.from_text(text='Why is the sky blue?')]
)
]
```
##### Provide a string
```python
contents='Why is the sky blue?'
```
The SDK will assume this is a text part, and it converts this into the following:
```python
[
types.UserContent(
parts=[
types.Part.from_text(text='Why is the sky blue?')
]
)
]
```
Where a `types.UserContent` is a subclass of `types.Content`, it sets the
`role` field to be `user`.
##### Provide a list of strings
```python
contents=['Why is the sky blue?', 'Why is the cloud white?']
```
The SDK assumes these are 2 text parts, it converts this into a single content,
like the following:
```python
[
types.UserContent(
parts=[
types.Part.from_text(text='Why is the sky blue?'),
types.Part.from_text(text='Why is the cloud white?'),
]
)
]
```
Where a `types.UserContent` is a subclass of `types.Content`, the
`role` field in `types.UserContent` is fixed to be `user`.
##### Provide a function call part
```python
from google.genai import types
contents = types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'Boston'}
)
```
The SDK converts a function call part to a content with a `model` role:
```python
[
types.ModelContent(
parts=[
types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'Boston'}
)
]
)
]
```
Where a `types.ModelContent` is a subclass of `types.Content`, the
`role` field in `types.ModelContent` is fixed to be `model`.
##### Provide a list of function call parts
```python
from google.genai import types
contents = [
types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'Boston'}
),
types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'New York'}
),
]
```
The SDK converts a list of function call parts to a content with a `model` role:
```python
[
types.ModelContent(
parts=[
types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'Boston'}
),
types.Part.from_function_call(
name='get_weather_by_location',
args={'location': 'New York'}
)
]
)
]
```
Where a `types.ModelContent` is a subclass of `types.Content`, the
`role` field in `types.ModelContent` is fixed to be `model`.
##### Provide a non function call part
```python
from google.genai import types
contents = types.Part.from_uri(
file_uri: 'gs://generativeai-downloads/images/scones.jpg',
mime_type: 'image/jpeg',
)
```
The SDK converts all non function call parts into a content with a `user` role.
```python
[
types.UserContent(parts=[
types.Part.from_uri(
file_uri: 'gs://generativeai-downloads/images/scones.jpg',
mime_type: 'image/jpeg',
)
])
]
```
##### Provide a list of non function call parts
```python
from google.genai import types
contents = [
types.Part.from_text('What is this image about?'),
types.Part.from_uri(
file_uri: 'gs://generativeai-downloads/images/scones.jpg',
mime_type: 'image/jpeg',
)
]
```
The SDK will convert the list of parts into a content with a `user` role
```python
[
types.UserContent(
parts=[
types.Part.from_text('What is this image about?'),
types.Part.from_uri(
file_uri: 'gs://generativeai-downloads/images/scones.jpg',
mime_type: 'image/jpeg',
)
]
)
]
```
##### Mix types in contents
You can also provide a list of `types.ContentUnion`. The SDK leaves items of
`types.Content` as is, it groups consecutive non function call parts into a
single `types.UserContent`, and it groups consecutive function call parts into
a single `types.ModelContent`.
If you put a list within a list, the inner list can only contain
`types.PartUnion` items. The SDK will convert the inner list into a single
`types.UserContent`.
### System Instructions and Other Configs
The output of the model can be influenced by several optional settings
available in generate_content's config parameter. For example, increasing
`max_output_tokens` is essential for longer model responses. To make a model more
deterministic, lowering the `temperature` parameter reduces randomness, with
values near 0 minimizing variability. Capabilities and parameter defaults for
each model is shown in the
[Vertex AI docs](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash)
and [Gemini API docs](https://ai.google.dev/gemini-api/docs/models) respectively.
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='high',
config=types.GenerateContentConfig(
system_instruction='I say high, you say low',
max_output_tokens=3,
temperature=0.3,
),
)
print(response.text)
```
### List Base Models
To retrieve tuned models, see [list tuned models](#list-tuned-models).
```python
for model in client.models.list():
print(model)
```
```python
pager = client.models.list(config={'page_size': 10})
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])
```
#### List Base Models (Asynchronous)
```python
async for job in await client.aio.models.list():
print(job)
```
```python
async_pager = await client.aio.models.list(config={'page_size': 10})
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])
```
### Safety Settings
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='Say something bad.',
config=types.GenerateContentConfig(
safety_settings=[
types.SafetySetting(
category='HARM_CATEGORY_HATE_SPEECH',
threshold='BLOCK_ONLY_HIGH',
)
]
),
)
print(response.text)
```
### Function Calling
#### Automatic Python function Support
You can pass a Python function directly and it will be automatically
called and responded by default.
```python
from google.genai import types
def get_current_weather(location: str) -> str:
"""Returns the current weather.
Args:
location: The city and state, e.g. San Francisco, CA
"""
return 'sunny'
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='What is the weather like in Boston?',
config=types.GenerateContentConfig(tools=[get_current_weather]),
)
print(response.text)
```
#### Disabling automatic function calling
If you pass in a python function as a tool directly, and do not want
automatic function calling, you can disable automatic function calling
as follows:
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='What is the weather like in Boston?',
config=types.GenerateContentConfig(
tools=[get_current_weather],
automatic_function_calling=types.AutomaticFunctionCallingConfig(
disable=True
),
),
)
```
With automatic function calling disabled, you will get a list of function call
parts in the response:
```python
function_calls: Optional[List[types.FunctionCall]] = response.function_calls
```
#### Manually declare and invoke a function for function calling
If you don't want to use the automatic function support, you can manually
declare the function and invoke it.
The following example shows how to declare a function and pass it as a tool.
Then you will receive a function call part in the response.
```python
from google.genai import types
function = types.FunctionDeclaration(
name='get_current_weather',
description='Get the current weather in a given location',
parameters_json_schema={
'type': 'object',
'properties': {
'location': {
'type': 'string',
'description': 'The city and state, e.g. San Francisco, CA',
}
},
'required': ['location'],
},
)
tool = types.Tool(function_declarations=[function])
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='What is the weather like in Boston?',
config=types.GenerateContentConfig(tools=[tool]),
)
print(response.function_calls[0])
```
After you receive the function call part from the model, you can invoke the function
and get the function response. And then you can pass the function response to
the model.
The following example shows how to do it for a simple function invocation.
```python
from google.genai import types
user_prompt_content = types.Content(
role='user',
parts=[types.Part.from_text(text='What is the weather like in Boston?')],
)
function_call_part = response.function_calls[0]
function_call_content = response.candidates[0].content
try:
function_result = get_current_weather(
**function_call_part.function_call.args
)
function_response = {'result': function_result}
except (
Exception
) as e: # instead of raising the exception, you can let the model handle it
function_response = {'error': str(e)}
function_response_part = types.Part.from_function_response(
name=function_call_part.name,
response=function_response,
)
function_response_content = types.Content(
role='tool', parts=[function_response_part]
)
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=[
user_prompt_content,
function_call_content,
function_response_content,
],
config=types.GenerateContentConfig(
tools=[tool],
),
)
print(response.text)
```
#### Function calling with `ANY` tools config mode
If you configure function calling mode to be `ANY`, then the model will always
return function call parts. If you also pass a python function as a tool, by
default the SDK will perform automatic function calling until the remote calls exceed the
maximum remote call for automatic function calling (default to 10 times).
If you'd like to disable automatic function calling in `ANY` mode:
```python
from google.genai import types
def get_current_weather(location: str) -> str:
"""Returns the current weather.
Args:
location: The city and state, e.g. San Francisco, CA
"""
return "sunny"
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="What is the weather like in Boston?",
config=types.GenerateContentConfig(
tools=[get_current_weather],
automatic_function_calling=types.AutomaticFunctionCallingConfig(
disable=True
),
tool_config=types.ToolConfig(
function_calling_config=types.FunctionCallingConfig(mode='ANY')
),
),
)
```
If you'd like to set `x` number of automatic function call turns, you can
configure the maximum remote calls to be `x + 1`.
Assuming you prefer `1` turn for automatic function calling.
```python
from google.genai import types
def get_current_weather(location: str) -> str:
"""Returns the current weather.
Args:
location: The city and state, e.g. San Francisco, CA
"""
return "sunny"
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="What is the weather like in Boston?",
config=types.GenerateContentConfig(
tools=[get_current_weather],
automatic_function_calling=types.AutomaticFunctionCallingConfig(
maximum_remote_calls=2
),
tool_config=types.ToolConfig(
function_calling_config=types.FunctionCallingConfig(mode='ANY')
),
),
)
```
#### Model Context Protocol (MCP) support (experimental)
Built-in [MCP](https://modelcontextprotocol.io/introduction) support is an
experimental feature. You can pass a local MCP server as a tool directly.
```python
import os
import asyncio
from datetime import datetime
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from google import genai
client = genai.Client()
# Create server parameters for stdio connection
server_params = StdioServerParameters(
command="npx", # Executable
args=["-y", "@philschmid/weather-mcp"], # MCP Server
env=None, # Optional environment variables
)
async def run():
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
# Prompt to get the weather for the current day in London.
prompt = f"What is the weather in London in {datetime.now().strftime('%Y-%m-%d')}?"
# Initialize the connection between client and server
await session.initialize()
# Send request to the model with MCP function declarations
response = await client.aio.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config=genai.types.GenerateContentConfig(
temperature=0,
tools=[session], # uses the session, will automatically call the tool using automatic function calling
),
)
print(response.text)
# Start the asyncio event loop and run the main function
asyncio.run(run())
```
### JSON Response Schema
However you define your schema, don't duplicate it in your input prompt,
including by giving examples of expected JSON output. If you do, the generated
output might be lower in quality.
#### JSON Schema support
Schemas can be provided as standard JSON schema.
```python
user_profile = {
'properties': {
'age': {
'anyOf': [
{'maximum': 20, 'minimum': 0, 'type': 'integer'},
{'type': 'null'},
],
'title': 'Age',
},
'username': {
'description': "User's unique name",
'title': 'Username',
'type': 'string',
},
},
'required': ['username', 'age'],
'title': 'User Schema',
'type': 'object',
}
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='Give me a random user profile.',
config={
'response_mime_type': 'application/json',
'response_json_schema': user_profile
},
)
print(response.text)
```
#### Pydantic Model Schema support
Schemas can be provided as Pydantic Models.
```python
from pydantic import BaseModel
from google.genai import types
class CountryInfo(BaseModel):
name: str
population: int
capital: str
continent: str
gdp: int
official_language: str
total_area_sq_mi: int
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='Give me information for the United States.',
config=types.GenerateContentConfig(
response_mime_type='application/json',
response_json_schema=CountryInfo.model_json_schema(),
),
)
print(response.text)
```
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='Give me information for the United States.',
config=types.GenerateContentConfig(
response_mime_type='application/json',
response_json_schema={
'required': [
'name',
'population',
'capital',
'continent',
'gdp',
'official_language',
'total_area_sq_mi',
],
'properties': {
'name': {'type': 'STRING'},
'population': {'type': 'INTEGER'},
'capital': {'type': 'STRING'},
'continent': {'type': 'STRING'},
'gdp': {'type': 'INTEGER'},
'official_language': {'type': 'STRING'},
'total_area_sq_mi': {'type': 'INTEGER'},
},
'type': 'OBJECT',
},
),
)
print(response.text)
```
### Generate Content (Synchronous Streaming)
Generate content in a streaming format so that the model outputs streams back
to you, rather than being returned as one chunk.
#### Streaming for text content
```python
for chunk in client.models.generate_content_stream(
model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
):
print(chunk.text, end='')
```
#### Streaming for image content
If your image is stored in [Google Cloud Storage](https://cloud.google.com/storage),
you can use the `from_uri` class method to create a `Part` object.
```python
from google.genai import types
for chunk in client.models.generate_content_stream(
model='gemini-2.5-flash',
contents=[
'What is this image about?',
types.Part.from_uri(
file_uri='gs://generativeai-downloads/images/scones.jpg',
mime_type='image/jpeg',
),
],
):
print(chunk.text, end='')
```
If your image is stored in your local file system, you can read it in as bytes
data and use the `from_bytes` class method to create a `Part` object.
```python
from google.genai import types
YOUR_IMAGE_PATH = 'your_image_path'
YOUR_IMAGE_MIME_TYPE = 'your_image_mime_type'
with open(YOUR_IMAGE_PATH, 'rb') as f:
image_bytes = f.read()
for chunk in client.models.generate_content_stream(
model='gemini-2.5-flash',
contents=[
'What is this image about?',
types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE),
],
):
print(chunk.text, end='')
```
### Generate Content (Asynchronous Non Streaming)
`client.aio` exposes all the analogous [`async` methods](https://docs.python.org/3/library/asyncio.html)
that are available on `client`. Note that it applies to all the modules.
For example, `client.aio.models.generate_content` is the `async` version
of `client.models.generate_content`
```python
response = await client.aio.models.generate_content(
model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
)
print(response.text)
```
### Generate Content (Asynchronous Streaming)
```python
async for chunk in await client.aio.models.generate_content_stream(
model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
):
print(chunk.text, end='')
```
### Count Tokens and Compute Tokens
```python
response = client.models.count_tokens(
model='gemini-2.5-flash',
contents='why is the sky blue?',
)
print(response)
```
#### Compute Tokens
Compute tokens is only supported in Vertex AI.
```python
response = client.models.compute_tokens(
model='gemini-2.5-flash',
contents='why is the sky blue?',
)
print(response)
```
##### Async
```python
response = await client.aio.models.count_tokens(
model='gemini-2.5-flash',
contents='why is the sky blue?',
)
print(response)
```
#### Local Count Tokens
```python
tokenizer = genai.LocalTokenizer(model_name='gemini-2.5-flash')
result = tokenizer.count_tokens("What is your name?")
```
#### Local Compute Tokens
```python
tokenizer = genai.LocalTokenizer(model_name='gemini-2.5-flash')
result = tokenizer.compute_tokens("What is your name?")
```
### Embed Content
```python
response = client.models.embed_content(
model='gemini-embedding-001',
contents='why is the sky blue?',
)
print(response)
```
```python
from google.genai import types
response = client.models.embed_content(
model='gemini-embedding-001',
contents=['why is the sky blue?', 'What is your age?'],
config=types.EmbedContentConfig(output_dimensionality=10),
)
print(response)
```
### Imagen
#### Generate Images
```python
from google.genai import types
response1 = client.models.generate_images(
model='imagen-4.0-generate-001',
prompt='An umbrella in the foreground, and a rainy night sky in the background',
config=types.GenerateImagesConfig(
number_of_images=1,
include_rai_reason=True,
output_mime_type='image/jpeg',
),
)
response1.generated_images[0].image.show()
```
#### Upscale Image
Upscale image is only supported in Vertex AI.
```python
from google.genai import types
response2 = client.models.upscale_image(
model='imagen-4.0-upscale-preview',
image=response1.generated_images[0].image,
upscale_factor='x2',
config=types.UpscaleImageConfig(
include_rai_reason=True,
output_mime_type='image/jpeg',
),
)
response2.generated_images[0].image.show()
```
#### Edit Image
Edit image uses a separate model from generate and upscale.
Edit image is only supported in Vertex AI.
```python
# Edit the generated image from above
from google.genai import types
from google.genai.types import RawReferenceImage, MaskReferenceImage
raw_ref_image = RawReferenceImage(
reference_id=1,
reference_image=response1.generated_images[0].image,
)
# Model computes a mask of the background
mask_ref_image = MaskReferenceImage(
reference_id=2,
config=types.MaskReferenceConfig(
mask_mode='MASK_MODE_BACKGROUND',
mask_dilation=0,
),
)
response3 = client.models.edit_image(
model='imagen-3.0-capability-001',
prompt='Sunlight and clear sky',
reference_images=[raw_ref_image, mask_ref_image],
config=types.EditImageConfig(
edit_mode='EDIT_MODE_INPAINT_INSERTION',
number_of_images=1,
include_rai_reason=True,
output_mime_type='image/jpeg',
),
)
response3.generated_images[0].image.show()
```
### Veo
Support for generating videos is considered public preview
#### Generate Videos (Text to Video)
```python
from google.genai import types
# Create operation
operation = client.models.generate_videos(
model='veo-3.1-generate-preview',
prompt='A neon hologram of a cat driving at top speed',
config=types.GenerateVideosConfig(
number_of_videos=1,
duration_seconds=5,
enhance_prompt=True,
),
)
# Poll operation
while not operation.done:
time.sleep(20)
operation = client.operations.get(operation)
video = operation.response.generated_videos[0].video
video.show()
```
#### Generate Videos (Image to Video)
```python
from google.genai import types
# Read local image (uses mimetypes.guess_type to infer mime type)
image = types.Image.from_file("local/path/file.png")
# Create operation
operation = client.models.generate_videos(
model='veo-3.1-generate-preview',
# Prompt is optional if image is provided
prompt='Night sky',
image=image,
config=types.GenerateVideosConfig(
number_of_videos=1,
duration_seconds=5,
enhance_prompt=True,
# Can also pass an Image into last_frame for frame interpolation
),
)
# Poll operation
while not operation.done:
time.sleep(20)
operation = client.operations.get(operation)
video = operation.response.generated_videos[0].video
video.show()
```
#### Generate Videos (Video to Video)
Currently, only Gemini Developer API supports video extension on Veo 3.1 for
previously generated videos. Vertex supports video extension on Veo 2.0.
```python
from google.genai import types
# Read local video (uses mimetypes.guess_type to infer mime type)
video = types.Video.from_file("local/path/video.mp4")
# Create operation
operation = client.models.generate_videos(
model='veo-3.1-generate-preview',
# Prompt is optional if Video is provided
prompt='Night sky',
# Input video must be in GCS for Vertex or a URI for Gemini
video=types.Video(
uri="gs://bucket-name/inputs/videos/cat_driving.mp4",
),
config=types.GenerateVideosConfig(
number_of_videos=1,
duration_seconds=5,
enhance_prompt=True,
),
)
# Poll operation
while not operation.done:
time.sleep(20)
operation = client.operations.get(operation)
video = operation.response.generated_videos[0].video
video.show()
```
## Chats
Create a chat session to start a multi-turn conversations with the model. Then,
use `chat.send_message` function multiple times within the same chat session so
that it can reflect on its previous responses (i.e., engage in an ongoing
conversation). See the 'Create a client' section above to initialize a client.
### Send Message (Synchronous Non-Streaming)
```python
chat = client.chats.create(model='gemini-2.5-flash')
response = chat.send_message('tell me a story')
print(response.text)
response = chat.send_message('summarize the story you told me in 1 sentence')
print(response.text)
```
### Send Message (Synchronous Streaming)
```python
chat = client.chats.create(model='gemini-2.5-flash')
for chunk in chat.send_message_stream('tell me a story'):
print(chunk.text)
```
### Send Message (Asynchronous Non-Streaming)
```python
chat = client.aio.chats.create(model='gemini-2.5-flash')
response = await chat.send_message('tell me a story')
print(response.text)
```
### Send Message (Asynchronous Streaming)
```python
chat = client.aio.chats.create(model='gemini-2.5-flash')
async for chunk in await chat.send_message_stream('tell me a story'):
print(chunk.text)
```
## Files
Files are only supported in Gemini Developer API. See the 'Create a client'
section above to initialize a client.
```sh
!gcloud storage cp gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf .
!gcloud storage cp gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf .
```
### Upload
```python
file1 = client.files.upload(file='2312.11805v3.pdf')
file2 = client.files.upload(file='2403.05530.pdf')
print(file1)
print(file2)
```
### Get
```python
file1 = client.files.upload(file='2312.11805v3.pdf')
file_info = client.files.get(name=file1.name)
```
### Delete
```python
file3 = client.files.upload(file='2312.11805v3.pdf')
client.files.delete(name=file3.name)
```
## Caches
`client.caches` contains the control plane APIs for cached content. See the
'Create a client' section above to initialize a client.
### Create
```python
from google.genai import types
if client.vertexai:
file_uris = [
'gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf',
'gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf',
]
else:
file_uris = [file1.uri, file2.uri]
cached_content = client.caches.create(
model='gemini-2.5-flash',
config=types.CreateCachedContentConfig(
contents=[
types.Content(
role='user',
parts=[
types.Part.from_uri(
file_uri=file_uris[0], mime_type='application/pdf'
),
types.Part.from_uri(
file_uri=file_uris[1],
mime_type='application/pdf',
),
],
)
],
system_instruction='What is the sum of the two pdfs?',
display_name='test cache',
ttl='3600s',
),
)
```
### Get
```python
cached_content = client.caches.get(name=cached_content.name)
```
### Generate Content with Caches
```python
from google.genai import types
response = client.models.generate_content(
model='gemini-2.5-flash',
contents='Summarize the pdfs',
config=types.GenerateContentConfig(
cached_content=cached_content.name,
),
)
print(response.text)
```
## Interactions (Preview)
> **Warning:** The Interactions API is in **Beta**. This is a preview of an experimental feature. Features and schemas are subject to **breaking changes**.
The Interactions API is a unified interface for interacting with Gemini models and agents. It simplifies state management, tool orchestration, and long-running tasks.
See the [documentation site](https://ai.google.dev/gemini-api/docs/interactions) for more details.
### Basic Interaction
```python
interaction = client.interactions.create(
model='gemini-2.5-flash',
input='Tell me a short joke about programming.'
)
print(interaction.outputs[-1].text)
```
### Stateful Conversation
The Interactions API supports server-side state management. You can continue a conversation by referencing the `previous_interaction_id`.
```python
# 1. First turn
interaction1 = client.interactions.create(
model='gemini-2.5-flash',
input='Hi, my name is Amir.'
)
print(f"Model: {interaction1.outputs[-1].text}")
# 2. Second turn (passing previous_interaction_id)
interaction2 = client.interactions.create(
model='gemini-2.5-flash',
input='What is my name?',
previous_interaction_id=interaction1.id
)
print(f"Model: {interaction2.outputs[-1].text}")
```
### Agents (Deep Research)
You can use specialized agents like `deep-research-pro-preview-12-2025` for complex tasks.
```python
import time
# 1. Start the Deep Research Agent
initial_interaction = client.interactions.create(
input='Research the history of the Google TPUs with a focus on 2025 and 2026.',
agent='deep-research-pro-preview-12-2025',
background=True
)
print(f"Research started. Interaction ID: {initial_interaction.id}")
# 2. Poll for results
while True:
interaction = client.interactions.get(id=initial_interaction.id)
print(f"Status: {interaction.status}")
if interaction.status == "completed":
print("\nFinal Report:\n", interaction.outputs[-1].text)
break
elif interaction.status in ["failed", "cancelled"]:
print(f"Failed with status: {interaction.status}")
break
time.sleep(10)
```
### Multimodal Input
You can provide multimodal data (text, images, audio, etc.) in the input list.
```python
import base64
# Assuming you have an image loaded as bytes
# base64_image = ...
interaction = client.interactions.create(
model='gemini-2.5-flash',
input=[
{'type': 'text', 'text': 'Describe the image.'},
{'type': 'image', 'data': base64_image, 'mime_type': 'image/png'}
]
)
print(interaction.outputs[-1].text)
```
### Function Calling
You can define custom functions for the model to use. The Interactions API handles the tool selection, and you provide the execution result back to the model.
```python
# 1. Define the tool
def get_weather(location: str):
"""Gets the weather for a given location."""
return f"The weather in {location} is sunny."
weather_tool = {
'type': 'function',
'name': 'get_weather',
'description': 'Gets the weather for a given location.',
'parameters': {
'type': 'object',
'properties': {
'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}
},
'required': ['location']
}
}
# 2. Send the request with tools
interaction = client.interactions.create(
model='gemini-2.5-flash',
input='What is the weather in Mountain View, CA?',
tools=[weather_tool]
)
# 3. Handle the tool call
for output in interaction.outputs:
if output.type == 'function_call':
print(f"Tool Call: {output.name}({output.arguments})")
# Execute your actual function here
result = get_weather(**output.arguments)
# Send result back to the model
interaction = client.interactions.create(
model='gemini-2.5-flash',
previous_interaction_id=interaction.id,
input=[{
'type': 'function_result',
'name': output.name,
'call_id': output.id,
'result': result
}]
)
print(f"Response: {interaction.outputs[-1].text}")
```
### Built-in Tools
You can also use Google's built-in tools, such as **Google Search** or **Code Execution**.
#### Grounding with Google Search
```python
interaction = client.interactions.create(
model='gemini-2.5-flash',
input='Who won the last Super Bowl?',
tools=[{'type': 'google_search'}]
)
# Find the text output (not the GoogleSearchResultContent)
text_output = next((o for o in interaction.outputs if o.type == 'text'), None)
if text_output:
print(text_output.text)
```
#### Code Execution
```python
interaction = client.interactions.create(
model='gemini-2.5-flash',
input='Calculate the 50th Fibonacci number.',
tools=[{'type': 'code_execution'}]
)
print(interaction.outputs[-1].text)
```
### Multimodal Output
The Interactions API can generate multimodal outputs, such as images. You must specify the `response_modalities`.
```python
import base64
interaction = client.interactions.create(
model='gemini-3-pro-image-preview',
input='Generate an image of a futuristic city.',
response_modalities=['IMAGE']
)
for output in interaction.outputs:
if output.type == 'image':
print(f"Generated image with mime_type: {output.mime_type}")
# Save the image
with open("generated_city.png", "wb") as f:
f.write(base64.b64decode(output.data))
```
## Tunings
`client.tunings` contains tuning job APIs and supports supervised fine
tuning through `tune`. Only supported in Vertex AI. See the 'Create a client'
section above to initialize a client.
### Tune
- Vertex AI supports tuning from GCS source or from a [Vertex AI Multimodal Dataset](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/datasets)
```python
from google.genai import types
model = 'gemini-2.5-flash'
training_dataset = types.TuningDataset(
# or gcs_uri=my_vertex_multimodal_dataset
gcs_uri='gs://your-gcs-bucket/your-tuning-data.jsonl',
)
```
```python
from google.genai import types
tuning_job = client.tunings.tune(
base_model=model,
training_dataset=training_dataset,
config=types.CreateTuningJobConfig(
epoch_count=1, tuned_model_display_name='test_dataset_examples model'
),
)
print(tuning_job)
```
### Get Tuning Job
```python
tuning_job = client.tunings.get(name=tuning_job.name)
print(tuning_job)
```
```python
import time
completed_states = set(
[
'JOB_STATE_SUCCEEDED',
'JOB_STATE_FAILED',
'JOB_STATE_CANCELLED',
]
)
while tuning_job.state not in completed_states:
print(tuning_job.state)
tuning_job = client.tunings.get(name=tuning_job.name)
time.sleep(10)
```
#### Use Tuned Model
```python
response = client.models.generate_content(
model=tuning_job.tuned_model.endpoint,
contents='why is the sky blue?',
)
print(response.text)
```
### Get Tuned Model
```python
tuned_model = client.models.get(model=tuning_job.tuned_model.model)
print(tuned_model)
```
### List Tuned Models
To retrieve base models, see [list base models](#list-base-models).
```python
for model in client.models.list(config={'page_size': 10, 'query_base': False}):
print(model)
```
```python
pager = client.models.list(config={'page_size': 10, 'query_base': False})
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])
```
#### Async
```python
async for job in await client.aio.models.list(config={'page_size': 10, 'query_base': False}):
print(job)
```
```python
async_pager = await client.aio.models.list(config={'page_size': 10, 'query_base': False})
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])
```
### Update Tuned Model
```python
from google.genai import types
model = pager[0]
model = client.models.update(
model=model.name,
config=types.UpdateModelConfig(
display_name='my tuned model', description='my tuned model description'
),
)
print(model)
```
### List Tuning Jobs
```python
for job in client.tunings.list(config={'page_size': 10}):
print(job)
```
```python
pager = client.tunings.list(config={'page_size': 10})
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])
```
#### Async
```python
async for job in await client.aio.tunings.list(config={'page_size': 10}):
print(job)
```
```python
async_pager = await client.aio.tunings.list(config={'page_size': 10})
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])
```
## Batch Prediction
Only supported in Vertex AI. See the 'Create a client' section above to
initialize a client.
### Create
Vertex AI:
```python
# Specify model and source file only, destination and job display name will be auto-populated
job = client.batches.create(
model='gemini-2.5-flash',
src='bq://my-project.my-dataset.my-table', # or "gs://path/to/input/data"
)
print(job)
```
Gemini Developer API:
```python
# Create a batch job with inlined requests
batch_job = client.batches.create(
model="gemini-2.5-flash",
src=[{
"contents": [{
"parts": [{
"text": "Hello!",
}],
"role": "user",
}],
"config": {"response_modalities": ["text"]},
}],
)
job
```
In order to create a batch job with file name. Need to upload a json file.
For example `myrequests.json`:
```json
{"key":"request_1", "request": {"contents": [{"parts": [{"text":
"Explain how AI works in a few words"}]}], "generation_config": {"response_modalities": ["TEXT"]}}}
{"key":"request_2", "request": {"contents": [{"parts": [{"text": "Explain how Crypto works in a few words"}]}]}}
```
Then upload the file.
```python
# Upload the file
file = client.files.upload(
file='myrequests.json',
config=types.UploadFileConfig(display_name='test-json')
)
# Create a batch job with file name
batch_job = client.batches.create(
model="gemini-2.5-flash",
src="files/test-json",
)
```
```python
# Get a job by name
job = client.batches.get(name=job.name)
job.state
```
```python
completed_states = set(
[
'JOB_STATE_SUCCEEDED',
'JOB_STATE_FAILED',
'JOB_STATE_CANCELLED',
'JOB_STATE_PAUSED',
]
)
while job.state not in completed_states:
print(job.state)
job = client.batches.get(name=job.name)
time.sleep(30)
job
```
### List
```python
for job in client.batches.list(config=types.ListBatchJobsConfig(page_size=10)):
print(job)
```
```python
pager = client.batches.list(config=types.ListBatchJobsConfig(page_size=10))
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])
```
#### Async
```python
async for job in await client.aio.batches.list(
config=types.ListBatchJobsConfig(page_size=10)
):
print(job)
```
```python
async_pager = await client.aio.batches.list(
config=types.ListBatchJobsConfig(page_size=10)
)
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])
```
### Delete
```python
# Delete the job resource
delete_job = client.batches.delete(name=job.name)
delete_job
```
## Error Handling
To handle errors raised by the model service, the SDK provides this [`APIError`](https://github.com/googleapis/python-genai/blob/main/google/genai/errors.py) class.
```python
from google.genai import errors
try:
client.models.generate_content(
model="invalid-model-name",
contents="What is your name?",
)
except errors.APIError as e:
print(e.code) # 404
print(e.message)
```
## Extra Request Body
The `extra_body` field in `HttpOptions` accepts a dictionary of additional JSON
properties to include in the request body. This can be used to access new or
experimental backend features that are not yet formally supported in the SDK.
The structure of the dictionary must match the backend API's request structure.
- Vertex AI backend API docs: https://cloud.google.com/vertex-ai/docs/reference/rest
- Gemini API backend API docs: https://ai.google.dev/api/rest
```python
response = client.models.generate_content(
model="gemini-2.5-pro",
contents="What is the weather in Boston? and how about Sunnyvale?",
config=types.GenerateContentConfig(
tools=[get_current_weather],
http_options=types.HttpOptions(extra_body={'tool_config': {'function_calling_config': {'mode': 'COMPOSITIONAL'}}}),
),
)
```
Reference in New Issue View Git Blame Copy Permalink