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Overview

AzureLLMService provides access to Azure OpenAI’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService and supports streaming responses, function calling, and context management.

API Reference

Complete API documentation and method details

Azure OpenAI Docs

Official Azure OpenAI documentation and setup

Example Code

Working example with function calling

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Installation

To use Azure OpenAI services, install the required dependency: 
pip install "pipecat-ai[azure]"
You’ll need to set up your Azure OpenAI credentials:
  • AZURE_CHATGPT_API_KEY - Your Azure OpenAI API key
  • AZURE_CHATGPT_ENDPOINT - Your Azure OpenAI endpoint URL
  • AZURE_CHATGPT_MODEL - Your model deployment name
Get your credentials from the Azure Portal under your Azure OpenAI resource.

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Frames

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Input

  • OpenAILLMContextFrame - Conversation context and history
  • LLMMessagesFrame - Direct message list
  • VisionImageRawFrame - Images for vision processing
  • LLMUpdateSettingsFrame - Runtime parameter updates

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Output

  • LLMFullResponseStartFrame / LLMFullResponseEndFrame - Response boundaries
  • LLMTextFrame - Streamed completion chunks
  • FunctionCallInProgressFrame / FunctionCallResultFrame - Function call lifecycle
  • ErrorFrame - API or processing errors

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Azure vs OpenAI Differences

FeatureAzure OpenAIStandard OpenAI
AuthenticationAPI key + endpointAPI key only
DeploymentCustom deployment namesModel names directly
ComplianceEnterprise SOC, HIPAAStandard compliance
RegionalMultiple Azure regionsOpenAI regions only
PricingAzure billing integrationOpenAI billing

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Function Calling

Function Calling Guide

Learn how to implement function calling with standardized schemas, register handlers, manage context properly, and control execution flow in your conversational AI applications.

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Context Management

Context Management Guide

Learn how to manage conversation context, handle message history, and integrate context aggregators for consistent conversational experiences.

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Usage Example

import os
from pipecat.services.azure.llm import AzureLLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema

# Configure Azure OpenAI service
llm = AzureLLMService(
    api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
    endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
    model=os.getenv("AZURE_CHATGPT_MODEL"),  # Your deployment name
    params=AzureLLMService.InputParams(
        temperature=0.7,
        max_completion_tokens=1000
    )
)

# Define function for tool calling
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get current weather information",
    properties={
        "location": {
            "type": "string",
            "description": "City and state, e.g. San Francisco, CA"
        },
        "format": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Temperature unit to use"
        }
    },
    required=["location", "format"]
)

tools = ToolsSchema(standard_tools=[weather_function])

# Create context
context = OpenAILLMContext(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. Keep responses concise for voice output."
        }
    ],
    tools=tools
)

# Create context aggregators
context_aggregator = llm.create_context_aggregator(context)

# Register function handler with event callback
async def fetch_weather(params):
    location = params.arguments["location"]
    await params.result_callback({"conditions": "sunny", "temperature": "75°F"})

llm.register_function("get_current_weather", fetch_weather)

# Optional: Add function call feedback
@llm.event_handler("on_function_calls_started")
async def on_function_calls_started(service, function_calls):
    await tts.queue_frame(TTSSpeakFrame("Let me check on that."))

# Use in pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant()
])

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Metrics

Inherits all OpenAI metrics capabilities:
  • Time to First Byte (TTFB) - Response latency measurement
  • Processing Duration - Total request processing time
  • Token Usage - Prompt tokens, completion tokens, and totals
Learn how to enable Metrics in your Pipeline.

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Additional Notes

  • OpenAI Compatibility: Full compatibility with OpenAI API features and parameters
  • Regional Deployment: Deploy in your preferred Azure region for compliance and latency
  • Deployment Names: Use your Azure deployment name as the model parameter, not OpenAI model names
  • Automatic Retries: Built-in retry logic handles transient Azure service issues