--- title: "Azure AI Studio" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/azure-ai source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:08:09.213Z license: CC-BY-4.0 attribution: "Azure AI Studio — Claudary (https://claudary.paisolsolutions.com/skills/azure-ai)" --- # Azure AI Studio import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Azure AI Studio LiteLLM supports all models on Azure AI Studio ## Usage ### ENV VAR ```python import os os.environ["AZURE_AI_API_KEY"] = "" os.environ["AZURE_AI_API_BASE"] = "" ``` ### Example Call ```python from litellm import completion import os ## set ENV variables os.environ["AZURE_AI_API_KEY"] = "azure ai key" os.environ["AZURE_AI_API_BASE"] = "azure ai base url" # e.g.: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/ # predibase llama-3 call response = completion( model="azure_ai/command-r-plus", messages = [{ "content": "Hello, how are you?","role": "user"}] ) ``` 1. Add models to your config.yaml ```yaml model_list: - model_name: command-r-plus litellm_params: model: azure_ai/command-r-plus api_key: os.environ/AZURE_AI_API_KEY api_base: os.environ/AZURE_AI_API_BASE ``` 2. Start the proxy ```bash $ litellm --config /path/to/config.yaml --debug ``` 3. Send Request to LiteLLM Proxy Server ```python import openai client = openai.OpenAI( api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys base_url="http://0.0.0.0:4000" # litellm-proxy-base url ) response = client.chat.completions.create( model="command-r-plus", messages = [ { "role": "system", "content": "Be a good human!" }, { "role": "user", "content": "What do you know about earth?" } ] ) print(response) ``` ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Authorization: Bearer sk-1234' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "command-r-plus", "messages": [ { "role": "system", "content": "Be a good human!" }, { "role": "user", "content": "What do you know about earth?" } ], }' ``` ## Passing additional params - max_tokens, temperature See all litellm.completion supported params [here](../completion/input.md#translated-openai-params) ```python # !uv add litellm from litellm import completion import os ## set ENV variables os.environ["AZURE_AI_API_KEY"] = "azure ai api key" os.environ["AZURE_AI_API_BASE"] = "azure ai api base" # command r plus call response = completion( model="azure_ai/command-r-plus", messages = [{ "content": "Hello, how are you?","role": "user"}], max_tokens=20, temperature=0.5 ) ``` **proxy** ```yaml model_list: - model_name: command-r-plus litellm_params: model: azure_ai/command-r-plus api_key: os.environ/AZURE_AI_API_KEY api_base: os.environ/AZURE_AI_API_BASE max_tokens: 20 temperature: 0.5 ``` 2. Start the proxy ```bash $ litellm --config /path/to/config.yaml ``` 3. Send Request to LiteLLM Proxy Server ```python import openai client = openai.OpenAI( api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys base_url="http://0.0.0.0:4000" # litellm-proxy-base url ) response = client.chat.completions.create( model="mistral", messages = [ { "role": "user", "content": "what llm are you" } ], ) print(response) ``` ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Authorization: Bearer sk-1234' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "mistral", "messages": [ { "role": "user", "content": "what llm are you" } ], }' ``` ## Function Calling ```python from litellm import completion # set env os.environ["AZURE_AI_API_KEY"] = "your-api-key" os.environ["AZURE_AI_API_BASE"] = "your-api-base" 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"], }, }, } ] messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] response = completion( model="azure_ai/mistral-large-latest", messages=messages, tools=tools, tool_choice="auto", ) # Add any assertions, here to check response args print(response) assert isinstance(response.choices[0].message.tool_calls[0].function.name, str) assert isinstance( response.choices[0].message.tool_calls[0].function.arguments, str ) ``` ```bash curl http://0.0.0.0:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer $YOUR_API_KEY" \\ -d '{ "model": "mistral", "messages": [ { "role": "user", "content": "What'\\''s the weather like in Boston today?" } ], "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"] } } } ], "tool_choice": "auto" }' ``` ## Supported Models LiteLLM supports **ALL** azure ai models. Here's a few examples: | Model Name | Function Call | |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Cohere command-r-plus | `completion(model="azure_ai/command-r-plus", messages)` | | Cohere command-r | `completion(model="azure_ai/command-r", messages)` | | mistral-large-latest | `completion(model="azure_ai/mistral-large-latest", messages)` | | AI21-Jamba-Instruct | `completion(model="azure_ai/ai21-jamba-instruct", messages)` | ## Usage - Azure Anthropic (Azure Foundry Claude) LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token. ```python import os from litellm import completion # Configure Azure credentials os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key" os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" response = completion( model="azure_ai/claude-opus-4-1", messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}], max_tokens=1200, temperature=0.7, stream=True, ) for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) ``` **1. Set environment variables** ```bash export AZURE_AI_API_KEY="your-azure-ai-api-key" export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic" ``` **2. Configure the proxy** ```yaml model_list: - model_name: claude-4-azure litellm_params: model: azure_ai/claude-opus-4-1 api_key: os.environ/AZURE_AI_API_KEY api_base: os.environ/AZURE_AI_API_BASE ``` **3. Start LiteLLM** ```bash litellm --config /path/to/config.yaml ``` **4. Test the Azure Claude route** ```bash curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --header 'Authorization: Bearer $LITELLM_KEY' \\ --data '{ "model": "claude-4-azure", "messages": [ { "role": "user", "content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?" } ], "max_tokens": 1024 }' ``` ## Rerank Endpoint ### Usage ```python from litellm import rerank import os os.environ["AZURE_AI_API_KEY"] = "sk-.." os.environ["AZURE_AI_API_BASE"] = "https://.." query = "What is the capital of the United States?" documents = [ "Carson City is the capital city of the American state of Nevada.", "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", "Washington, D.C. is the capital of the United States.", "Capital punishment has existed in the United States since before it was a country.", ] response = rerank( model="azure_ai/cohere-rerank-v3.5", query=query, documents=documents, top_n=3, ) print(response) ``` LiteLLM provides an cohere api compatible `/rerank` endpoint for Rerank calls. **Setup** Add this to your litellm proxy config.yaml ```yaml model_list: - model_name: Salesforce/Llama-Rank-V1 litellm_params: model: together_ai/Salesforce/Llama-Rank-V1 api_key: os.environ/TOGETHERAI_API_KEY - model_name: cohere-rerank-v3.5 litellm_params: model: azure_ai/cohere-rerank-v3.5 api_key: os.environ/AZURE_AI_API_KEY api_base: os.environ/AZURE_AI_API_BASE ``` Start litellm ```bash litellm --config /path/to/config.yaml # RUNNING on http://0.0.0.0:4000 ``` Test request ```bash curl http://0.0.0.0:4000/rerank \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "cohere-rerank-v3.5", "query": "What is the capital of the United States?", "documents": [ "Carson City is the capital city of the American state of Nevada.", "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", "Washington, D.C. is the capital of the United States.", "Capital punishment has existed in the United States since before it was a country." ], "top_n": 3 }' ``` --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/azure-ai) · https://claudary.paisolsolutions.com