Gemini - Google AI Studio

Pre-requisites

• pip install -q google-generativeai

Gemini-Pro

Sample Usage

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"}]
)

Specifying Safety Settings

In certain use-cases you may need to make calls to the models and pass safety settigns different from the defaults. To do so, simple pass the safety_settings argument to completion or acompletion. For example:

response = completion(
    model="gemini/gemini-pro", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
    safety_settings=[
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE",
        },
    ]
)

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

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-1.5-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']
gemini-1.5-pro-vision	completion('gemini/gemini-pro-vision', messages)	os.environ['GEMINI_API_KEY']