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Azure OpenAI

API Keys, Params

api_key, api_base, api_version etc can be passed directly to litellm.completion - see here or set as litellm.api_key params see here

import os
os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

# optional
os.environ["AZURE_AD_TOKEN"] = ""
os.environ["AZURE_API_TYPE"] = ""

Usage - LiteLLM Python SDK

Open In Colab

Completion - using .env variables

from litellm import completion

## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# azure call
response = completion(
model = "azure/<your_deployment_name>",
messages = [{ "content": "Hello, how are you?","role": "user"}]
)

Completion - using api_key, api_base, api_version

import litellm

# azure call
response = litellm.completion(
model = "azure/<your deployment name>", # model = azure/<your deployment name>
api_base = "", # azure api base
api_version = "", # azure api version
api_key = "", # azure api key
messages = [{"role": "user", "content": "good morning"}],
)

Completion - using azure_ad_token, api_base, api_version

import litellm

# azure call
response = litellm.completion(
model = "azure/<your deployment name>", # model = azure/<your deployment name>
api_base = "", # azure api base
api_version = "", # azure api version
azure_ad_token="", # azure_ad_token
messages = [{"role": "user", "content": "good morning"}],
)

Usage - LiteLLM Proxy Server

Here's how to call Azure OpenAI models with the LiteLLM Proxy Server

1. Save key in your environment

export AZURE_API_KEY=""

2. Start the proxy

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env.

3. Test it

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'

Azure OpenAI Chat Completion Models

tip

We support ALL Azure models, just set model=azure/<your deployment name> as a prefix when sending litellm requests

Model NameFunction Call
o1-miniresponse = completion(model="azure/<your deployment name>", messages=messages)
o1-previewresponse = completion(model="azure/<your deployment name>", messages=messages)
gpt-4o-minicompletion('azure/<your deployment name>', messages)
gpt-4ocompletion('azure/<your deployment name>', messages)
gpt-4completion('azure/<your deployment name>', messages)
gpt-4-0314completion('azure/<your deployment name>', messages)
gpt-4-0613completion('azure/<your deployment name>', messages)
gpt-4-32kcompletion('azure/<your deployment name>', messages)
gpt-4-32k-0314completion('azure/<your deployment name>', messages)
gpt-4-32k-0613completion('azure/<your deployment name>', messages)
gpt-4-1106-previewcompletion('azure/<your deployment name>', messages)
gpt-4-0125-previewcompletion('azure/<your deployment name>', messages)
gpt-3.5-turbocompletion('azure/<your deployment name>', messages)
gpt-3.5-turbo-0301completion('azure/<your deployment name>', messages)
gpt-3.5-turbo-0613completion('azure/<your deployment name>', messages)
gpt-3.5-turbo-16kcompletion('azure/<your deployment name>', messages)
gpt-3.5-turbo-16k-0613completion('azure/<your deployment name>', messages)

Azure OpenAI Vision Models

Model NameFunction Call
gpt-4-visioncompletion(model="azure/<your deployment name>", messages=messages)
gpt-4ocompletion('azure/<your deployment name>', messages)

Usage

import os 
from litellm import completion

os.environ["AZURE_API_KEY"] = "your-api-key"

# azure call
response = completion(
model = "azure/<your deployment name>",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "What’s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
}
]
}
],
)

Usage - with Azure Vision enhancements

Note: Azure requires the base_url to be set with /extensions

Example

base_url=https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions
# base_url="{azure_endpoint}/openai/deployments/{azure_deployment}/extensions"

Usage

import os 
from litellm import completion

os.environ["AZURE_API_KEY"] = "your-api-key"

# azure call
response = completion(
model="azure/gpt-4-vision",
timeout=5,
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://avatars.githubusercontent.com/u/29436595?v=4"
},
},
],
}
],
base_url="https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions",
api_key=os.getenv("AZURE_VISION_API_KEY"),
enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
dataSources=[
{
"type": "AzureComputerVision",
"parameters": {
"endpoint": "https://gpt-4-vision-enhancement.cognitiveservices.azure.com/",
"key": os.environ["AZURE_VISION_ENHANCE_KEY"],
},
}
],
)

Azure O1 Models

Model NameFunction Call
o1-miniresponse = completion(model="azure/<your deployment name>", messages=messages)
o1-previewresponse = completion(model="azure/<your deployment name>", messages=messages)

Set litellm.enable_preview_features = True to use Azure O1 Models with streaming support.

import litellm

litellm.enable_preview_features = True # 👈 KEY CHANGE

response = litellm.completion(
model="azure/<your deployment name>",
messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
stream=True
)

for chunk in response:
print(chunk)

Azure Instruct Models

Use model="azure_text/<your-deployment>"

Model NameFunction Call
gpt-3.5-turbo-instructresponse = completion(model="azure_text/<your deployment name>", messages=messages)
gpt-3.5-turbo-instruct-0914response = completion(model="azure_text/<your deployment name>", messages=messages)
import litellm

## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

response = litellm.completion(
model="azure_text/<your-deployment-name",
messages=[{"role": "user", "content": "What is the weather like in Boston?"}]
)

print(response)

Azure Text to Speech (tts)

LiteLLM PROXY

 - model_name: azure/tts-1
litellm_params:
model: azure/tts-1
api_base: "os.environ/AZURE_API_BASE_TTS"
api_key: "os.environ/AZURE_API_KEY_TTS"
api_version: "os.environ/AZURE_API_VERSION"

LiteLLM SDK

from litellm import completion

## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# azure call
speech_file_path = Path(__file__).parent / "speech.mp3"
response = speech(
model="azure/<your-deployment-name",
voice="alloy",
input="the quick brown fox jumped over the lazy dogs",
)
response.stream_to_file(speech_file_path)

Authentication

Entrata ID - use azure_ad_token

This is a walkthrough on how to use Azure Active Directory Tokens - Microsoft Entra ID to make litellm.completion() calls

Step 1 - Download Azure CLI Installation instructons: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli

brew update && brew install azure-cli

Step 2 - Sign in using az

az login --output table

Step 3 - Generate azure ad token

az account get-access-token --resource https://cognitiveservices.azure.com

In this step you should see an accessToken generated

{
"accessToken": "eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsIng1dCI6IjlHbW55RlBraGMzaE91UjIybXZTdmduTG83WSIsImtpZCI6IjlHbW55RlBraGMzaE91UjIybXZTdmduTG83WSJ9",
"expiresOn": "2023-11-14 15:50:46.000000",
"expires_on": 1700005846,
"subscription": "db38de1f-4bb3..",
"tenant": "bdfd79b3-8401-47..",
"tokenType": "Bearer"
}

Step 4 - Make litellm.completion call with Azure AD token

Set azure_ad_token = accessToken from step 3 or set os.environ['AZURE_AD_TOKEN']

response = litellm.completion(
model = "azure/<your deployment name>", # model = azure/<your deployment name>
api_base = "", # azure api base
api_version = "", # azure api version
azure_ad_token="", # your accessToken from step 3
messages = [{"role": "user", "content": "good morning"}],
)

Entrata ID - use tenant_id, client_id, client_secret

Here is an example of setting up tenant_id, client_id, client_secret in your litellm proxy config.yaml

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
tenant_id: os.environ/AZURE_TENANT_ID
client_id: os.environ/AZURE_CLIENT_ID
client_secret: os.environ/AZURE_CLIENT_SECRET

Test it

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'

Example video of using tenant_id, client_id, client_secret with LiteLLM Proxy Server

Azure AD Token Refresh - DefaultAzureCredential

Use this if you want to use Azure DefaultAzureCredential for Authentication on your requests

from litellm import completion
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")


response = completion(
model = "azure/<your deployment name>", # model = azure/<your deployment name>
api_base = "", # azure api base
api_version = "", # azure api version
azure_ad_token_provider=token_provider
messages = [{"role": "user", "content": "good morning"}],
)

Advanced

Azure API Load-Balancing

Use this if you're trying to load-balance across multiple Azure/OpenAI deployments.

Router prevents failed requests, by picking the deployment which is below rate-limit and has the least amount of tokens used.

In production, Router connects to a Redis Cache to track usage across multiple deployments.

Quick Start

pip install litellm
from litellm import Router

model_list = [{ # list of model deployments
"model_name": "gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
}, {
"model_name": "gpt-3.5-turbo", # openai model name
"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")
},
"tpm": 240000,
"rpm": 1800
}, {
"model_name": "gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
}]

router = Router(model_list=model_list)

# openai.chat.completions.create replacement
response = router.completion(model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}]

print(response)

Redis Queue

router = Router(model_list=model_list, 
redis_host=os.getenv("REDIS_HOST"),
redis_password=os.getenv("REDIS_PASSWORD"),
redis_port=os.getenv("REDIS_PORT"))

print(response)

Parallel Function calling

See a detailed walthrough of parallel function calling with litellm here

# set Azure env variables
import os
os.environ['AZURE_API_KEY'] = "" # litellm reads AZURE_API_KEY from .env and sends the request
os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ['AZURE_API_VERSION'] = "2023-07-01-preview"

import litellm
import json
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if "tokyo" in location.lower():
return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
elif "san francisco" in location.lower():
return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
elif "paris" in location.lower():
return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
else:
return json.dumps({"location": location, "temperature": "unknown"})

## Step 1: send the conversation and available functions to the model
messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
]

response = litellm.completion(
model="azure/chatgpt-functioncalling", # model = azure/<your-azure-deployment-name>
messages=messages,
tools=tools,
tool_choice="auto", # auto is default, but we'll be explicit
)
print("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls
print("\nTool Choice:\n", tool_calls)