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