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## Migrate from OpenAI to Swarms in 3 lines of code
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If you’ve been using GPT-3.5 or GPT-4, switching to Swarms is easy!
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Swarms VLMs are available to use through our OpenAI compatible API. Additionally, if you have been building or prototyping using OpenAI’s Python SDK you can keep your code as-is and use Swarms’s VLMs models.
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In this example, we will show you how to change just three lines of code to make your Python application use Swarms’s Open Source models through OpenAI’s Python SDK.
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## Getting Started
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Migrate OpenAI’s Python SDK example script to use Swarms’s LLM endpoints.
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These are the three modifications necessary to achieve our goal:
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Redefine OPENAI_API_KEY your API key environment variable to use your Swarms key.
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Redefine OPENAI_BASE_URL to point to `https://api.swarms.world/v1/chat/completions`
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Change the model name to an Open Source model, for example: cogvlm-chat-17b
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## Requirements
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We will be using Python and OpenAI’s Python SDK.
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## Instructions
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Set up a Python virtual environment. Read Creating Virtual Environments here.
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```sh
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python3 -m venv .venv
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source .venv/bin/activate
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```
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Install the pip requirements in your local python virtual environment
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`python3 -m pip install openai`
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## Environment setup
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To run this example, there are simple steps to take:
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Get an Swarms API token by following these instructions.
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Expose the token in a new SWARMS_API_TOKEN environment variable:
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`export SWARMS_API_TOKEN=<your-token>`
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Switch the OpenAI token and base URL environment variable
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`export OPENAI_API_KEY=$SWARMS_API_TOKEN`
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`export OPENAI_BASE_URL="https://api.swarms.world/v1/chat/completions"`
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If you prefer, you can also directly paste your token into the client initialization.
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## Example code
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Once you’ve completed the steps above, the code below will call Swarms LLMs:
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```python
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from dotenv import load_dotenv
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from openai import OpenAI
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load_dotenv()
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openai_api_key = ""
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openai_api_base = "https://api.swarms.world/v1"
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model = "internlm-xcomposer2-4khd"
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client = OpenAI(api_key=openai_api_key, base_url=openai_api_base)
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# Note that this model expects the image to come before the main text
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chat_response = client.chat.completions.create(
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model=model,
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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": "image_url",
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"image_url": {
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"url": "https://home-cdn.reolink.us/wp-content/uploads/2022/04/010345091648784709.4253.jpg",
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},
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},
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{
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"type": "text",
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"text": "What is the most dangerous object in the image?",
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},
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],
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}
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],
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temperature=0.1,
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max_tokens=5000,
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)
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print("Chat response:", chat_response)
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```
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Note that you need to supply one of Swarms’s supported LLMs as an argument, as in the example above. For a complete list of our supported LLMs, check out our REST API page.
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## Example output
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The code above produces the following object:
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```python
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ChatCompletionMessage(content=" Hello! How can I assist you today? Do you have any questions or tasks you'd like help with? Please let me know and I'll do my best to assist you.", role='assistant' function_call=None, tool_calls=None)
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```
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