Azure AI provider implementation.

Supports Azure's AI services for accessing models from multiple families:

OpenAI Models

• GPT-4o, GPT-4, GPT-3.5 Turbo
• Reasoning models (o1, o3 series)
• Text embedding models

Anthropic Claude Models

• Claude 3 Opus, Sonnet, Haiku
• Claude 3.5 Sonnet
• Extended thinking/reasoning support

Capabilities

• Text generation (chat completions / messages)
• Streaming responses with usage tracking
• Tool calling (function calling)
• Embeddings generation (OpenAI models only)
• Multi-modal inputs (text and images)
• Structured output generation
• Extended thinking (Claude models)

Key Differences from Direct Provider APIs

1. Custom endpoints: Each Azure resource has a unique base URL. Azure supports two endpoint formats, auto-detected from the domain:

   • Azure OpenAI Service (.cognitiveservices.azure.com or .openai.azure.com): URL: /deployments/{deployment}/chat/completions?api-version={version} Model determined by deployment name in URL path.

2. API key authentication: Uses api-key header for all model families

3. Bearer token authentication: Prefix api_key with "Bearer " to use Authorization: Bearer header

4. Deployment names: The deployment name is used either in the URL path (traditional) or in the request body (Foundry format)

5. No model field in body: The deployment ID in the URL determines the model

   • Azure AI Foundry (.services.ai.azure.com): URL: /models/chat/completions?api-version={version} Model specified in request body (deployment name used).

Authentication

Environment variables are resolved by model family:

# For OpenAI models (GPT, o1, o3, etc.)
export AZURE_OPENAI_API_KEY=your-api-key
export AZURE_OPENAI_BASE_URL=https://your-openai-resource.openai.azure.com/openai

# For Anthropic models (Claude)
export AZURE_ANTHROPIC_API_KEY=your-api-key
export AZURE_ANTHROPIC_BASE_URL=https://your-anthropic-resource.openai.azure.com/openai

# Universal fallbacks (if all models share the same Azure resource)
export AZURE_API_KEY=your-api-key
export AZURE_BASE_URL=https://your-resource.openai.azure.com/openai

# Or pass directly in options (Azure OpenAI Service format)
ReqLLM.generate_text(
  "azure:gpt-4o",
  "Hello!",
  api_key: "your-api-key",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-gpt4-deployment"
)

# Using Bearer token authentication (e.g., Entra ID / Azure AD tokens)
ReqLLM.generate_text(
  "azure:gpt-4o",
  "Hello!",
  api_key: "Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-gpt4-deployment"
)

# Azure AI Foundry format (auto-detected from domain)
ReqLLM.generate_text(
  "azure:deepseek-v3",
  "Hello!",
  api_key: "your-api-key",
  base_url: "https://my-resource.services.ai.azure.com",
  deployment: "deepseek-v3"
)

Examples

# Basic usage
{:ok, response} = ReqLLM.generate_text(
  "azure:gpt-4o",
  "What is Elixir?",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-gpt4-deployment"
)

# Streaming
{:ok, response} = ReqLLM.stream_text(
  "azure:gpt-4o",
  "Tell me a story",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-gpt4-deployment"
)

# With tools
tools = [%ReqLLM.Tool{name: "get_weather", ...}]
{:ok, response} = ReqLLM.generate_text(
  "azure:gpt-4o",
  "What's the weather?",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-gpt4-deployment",
  tools: tools
)

# Embeddings
{:ok, embedding} = ReqLLM.generate_embedding(
  "azure:text-embedding-3-small",
  "Hello world",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-embedding-deployment"
)

# OpenAI reasoning models (o1, o3, o4-mini)
{:ok, response} = ReqLLM.generate_text(
  "azure:o1",
  "Solve this complex math problem step by step...",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-o1-deployment",
  max_tokens: 8000,
  provider_options: [reasoning_effort: "high"]
)

# Claude with extended thinking
{:ok, response} = ReqLLM.generate_text(
  "azure:claude-3-5-sonnet-20241022",
  "Analyze this complex problem...",
  base_url: "https://my-resource.openai.azure.com/openai",
  deployment: "my-claude-deployment",
  thinking: %{type: "enabled", budget_tokens: 10000},
  max_tokens: 4096
)

Deployment Configuration

Azure uses deployment names to route requests to specific model instances. If no deployment is specified, the model ID is used as a default (with a warning).

To find your deployment name:

1. Go to Azure OpenAI Studio (https://oai.azure.com/)
2. Navigate to "Deployments"
3. Copy the deployment name (e.g., "gpt-4o-prod", "claude-sonnet")

Error Handling

Common error scenarios:

• Missing API key: Set AZURE_API_KEY (or family-specific: AZURE_OPENAI_API_KEY, AZURE_ANTHROPIC_API_KEY)
• Missing base URL: Set AZURE_BASE_URL (or family-specific: AZURE_OPENAI_BASE_URL, AZURE_ANTHROPIC_BASE_URL)
• Invalid deployment: Ensure the deployment name matches your Azure resource
• Unsupported API version: Check Azure documentation for supported versions

Extending for New Model Families

Azure hosts multiple model families (OpenAI GPT, Anthropic Claude). To add support for a new model family:

1. Create a formatter module under ReqLLM.Providers.Azure.* (see Azure.OpenAI or Azure.Anthropic as examples)
2. Add the model prefix to @model_families map in this module
3. Handle any family-specific endpoint paths in get_chat_endpoint_path/3
4. Add family-specific headers in get_anthropic_headers/2 if needed