litellm/docs/my-website/docs/providers/gemini.md
2024-01-20 19:13:33 +09:00

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# Gemini - Google AI Studio
## Pre-requisites
* `pip install -q google-generativeai`
# Gemini-Pro
## Sample Usage
```python
from litellm import completion
import os
os.environ['GEMINI_API_KEY'] = ""
response = completion(
model="gemini/gemini-pro",
messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
)
```
# Gemini-Pro-Vision
LiteLLM Supports the following image types passed in `url`
- Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg
- Image in local storage - ./localimage.jpeg
## Sample Usage
```python
import os
import litellm
from dotenv import load_dotenv
# Load the environment variables from .env file
load_dotenv()
os.environ["GEMINI_API_KEY"] = os.getenv('GEMINI_API_KEY')
prompt = 'Describe the image in a few sentences.'
# Note: You can pass here the URL or Path of image directly.
image_url = 'https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg'
# Create the messages payload according to the documentation
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {"url": image_url}
}
]
}
]
# Make the API call to Gemini model
response = litellm.completion(
model="gemini/gemini-pro-vision",
messages=messages,
)
# Extract the response content
content = response.get('choices', [{}])[0].get('message', {}).get('content')
# Print the result
print(content)
```
## Chat Models
| Model Name | Function Call | Required OS Variables |
|------------------|--------------------------------------|-------------------------|
| gemini-pro | `completion('gemini/gemini-pro', messages)` | `os.environ['GEMINI_API_KEY']` |
| gemini-pro-vision | `completion('gemini/gemini-pro-vision', messages)` | `os.environ['GEMINI_API_KEY']` |