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@ -77,45 +77,8 @@ for part in response:
print(part.choices[0].delta.content or "")
```
# Router - load balancing([Docs](https://docs.litellm.ai/docs/routing))
LiteLLM allows you to load balance between multiple deployments (Azure, OpenAI). It picks the deployment which is below rate-limit and has the least amount of tokens used.
```python
from litellm import Router
model_list = [{ # list of model deployments
"model_name": "gpt-3.5-turbo", # model alias
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2", # actual model name
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
}
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
}
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
}
}]
router = Router(model_list=model_list)
# openai.ChatCompletion.create replacement
response = router.completion(model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}])
print(response)
```
## OpenAI Proxy - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
LiteLLM Proxy manages:
* Calling 100+ LLMs Huggingface/Bedrock/TogetherAI/etc. in the OpenAI ChatCompletions & Completions format
* Load balancing - between Multiple Models + Deployments of the same model LiteLLM proxy can handle 1k+ requests/second during load tests