343 Azure 67d369d3

Azure OpenAI

Overview

Azure OpenAI Service provides access to OpenAI models through Microsoft Azure's enterprise-grade infrastructure with additional security, compliance, and regional deployment options.

Supported Capabilities:

Official Documentation: https://learn.microsoft.com/azure/ai-services/openai/

Prerequisites

Account Requirements

• Azure subscription
• Azure OpenAI resource created in Azure Portal
• Deployments created for models you want to use

Setting Up Azure OpenAI

1. Create Azure OpenAI resource in Azure Portal
2. Create deployments for your desired models (e.g., "gpt-4-deployment")
3. Note your endpoint URL (e.g., https://your-resource.openai.azure.com/)
4. Get your API key from the resource's "Keys and Endpoint" section

Important: Deployment Names

In Azure OpenAI, you use deployment names, not model names. The model_name parameter in Esperanto corresponds to your Azure deployment name.

Example: If you created a deployment called "my-gpt4-deployment" for GPT-4, use:

model = AIFactory.create_language("azure", "my-gpt4-deployment")

Environment Variables

# Generic (works for all modalities)
AZURE_OPENAI_API_KEY="your-azure-api-key"
AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
AZURE_OPENAI_API_VERSION="2024-02-01"

# Modality-specific (takes precedence over generic)
# Use these when you want different deployments for different AI capabilities

# Language Models
# AZURE_OPENAI_API_KEY_LLM="your-llm-key"
# AZURE_OPENAI_ENDPOINT_LLM="https://your-llm-resource.openai.azure.com/"
# AZURE_OPENAI_API_VERSION_LLM="2024-02-01"

# Embeddings
# AZURE_OPENAI_API_KEY_EMBEDDING="your-embedding-key"
# AZURE_OPENAI_ENDPOINT_EMBEDDING="https://your-embedding-resource.openai.azure.com/"
# AZURE_OPENAI_API_VERSION_EMBEDDING="2024-12-01-preview"

# Speech-to-Text
# AZURE_OPENAI_API_KEY_STT="your-stt-key"
# AZURE_OPENAI_ENDPOINT_STT="https://your-stt-resource.openai.azure.com/"
# AZURE_OPENAI_API_VERSION_STT="2024-02-01"

# Text-to-Speech
# AZURE_OPENAI_API_KEY_TTS="your-tts-key"
# AZURE_OPENAI_ENDPOINT_TTS="https://your-tts-resource.openai.azure.com/"
# AZURE_OPENAI_API_VERSION_TTS="2024-02-01"

Variable Priority:

1. Direct parameters in code (api_key=, azure_endpoint=, api_version=)
2. Modality-specific env vars (AZURE_OPENAI_*_LLM, AZURE_OPENAI_*_EMBEDDING, etc.)
3. Generic env vars (AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, etc.)

Quick Start

Via Factory (Recommended)

from esperanto.factory import AIFactory

# Language model (using deployment name)
model = AIFactory.create_language("azure", "your-gpt4-deployment")

# Embedding model (using deployment name)
embedder = AIFactory.create_embedding("azure", "your-embedding-deployment")

# Speech-to-text (using deployment name)
transcriber = AIFactory.create_speech_to_text("azure", "your-whisper-deployment")

# Text-to-speech (using deployment name)
speaker = AIFactory.create_text_to_speech("azure", "your-tts-deployment")

Direct Instantiation

from esperanto.providers.llm.azure import AzureLanguageModel
from esperanto.providers.embedding.azure import AzureEmbeddingModel
from esperanto.providers.speech_to_text.azure import AzureSpeechToText
from esperanto.providers.text_to_speech.azure import AzureTextToSpeech

# Language model
llm = AzureLanguageModel(
    api_key="your-azure-key",
    azure_endpoint="https://your-resource.openai.azure.com/",
    api_version="2024-02-01",
    model_name="your-deployment-name"
)

# Embedding model
embedder = AzureEmbeddingModel(
    api_key="your-azure-key",
    azure_endpoint="https://your-resource.openai.azure.com/",
    api_version="2024-12-01-preview",
    model_name="your-embedding-deployment"
)

# Speech-to-text
stt = AzureSpeechToText(
    api_key="your-azure-key",
    azure_endpoint="https://your-resource.openai.azure.com/",
    api_version="2024-02-01",
    model_name="your-whisper-deployment"
)

# Text-to-speech
tts = AzureTextToSpeech(
    api_key="your-azure-key",
    azure_endpoint="https://your-resource.openai.azure.com/",
    api_version="2024-02-01",
    model_name="your-tts-deployment"
)

Capabilities

Language Models (LLM)

Available Models: Same models as OpenAI, deployed through Azure:

• GPT-4 series - gpt-4, gpt-4-turbo, gpt-4o
• GPT-3.5 series - gpt-3.5-turbo

Configuration:

from esperanto.factory import AIFactory

model = AIFactory.create_language(
    "azure",
    "your-deployment-name",  # Your Azure deployment name
    config={
        "temperature": 0.7,           # Randomness (0.0 - 2.0)
        "max_tokens": 1000,           # Maximum response length
        "top_p": 0.9,                 # Nucleus sampling
        "streaming": True,            # Enable streaming
        "structured": {"type": "json"}, # JSON mode
        "api_key": "your-key",        # Optional if using env vars
        "azure_endpoint": "https://your-resource.openai.azure.com/",
        "api_version": "2024-02-01"
    }
)

Example - Basic Chat:

from esperanto.factory import AIFactory

# Create model using environment variables
model = AIFactory.create_language("azure", "my-gpt4-deployment")

# Chat completion
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What's the capital of France?"}
]

response = model.chat_complete(messages)
print(response.choices[0].message.content)

Example - Explicit Configuration:

# Without environment variables
model = AIFactory.create_language(
    "azure",
    "my-gpt4-deployment",
    config={
        "api_key": "your-azure-key",
        "azure_endpoint": "https://your-resource.openai.azure.com/",
        "api_version": "2024-02-01",
        "temperature": 0.7,
        "structured": {"type": "json"}  # Azure supports JSON mode
    }
)

messages = [{"role": "user", "content": "Translate 'hello' to Spanish."}]
response = model.chat_complete(messages)

Example - Streaming:

# Synchronous streaming
for chunk in model.chat_complete(messages, stream=True):
    print(chunk.choices[0].delta.content, end="", flush=True)

# Async streaming
async for chunk in model.achat_complete(messages, stream=True):
    print(chunk.choices[0].delta.content, end="", flush=True)

Embeddings

Available Models: Same as OpenAI, deployed through Azure:

• text-embedding-3-small (1536 dimensions)
• text-embedding-3-large (3072 dimensions)
• text-embedding-ada-002 (1536 dimensions)

Configuration:

from esperanto.factory import AIFactory
from esperanto.common_types.task_type import EmbeddingTaskType

model = AIFactory.create_embedding(
    "azure",
    "your-embedding-deployment",
    config={
        "task_type": EmbeddingTaskType.RETRIEVAL_QUERY,  # Task optimization
        "output_dimensions": 1024,  # Reduce dimensions (3-large only)
        "api_key": "your-key",
        "azure_endpoint": "https://your-resource.openai.azure.com/",
        "api_version": "2024-12-01-preview"
    }
)

Example - Basic Embeddings:

from esperanto.factory import AIFactory

# Create embedding model
embedder = AIFactory.create_embedding("azure", "my-embedding-deployment")

# Generate embeddings
texts = ["Hello, world!", "Another text"]
response = embedder.embed(texts)

# Access embeddings
for i, embedding_obj in enumerate(response.data):
    print(f"Text {i}: {len(embedding_obj.embedding)} dimensions")

Example - Task-Optimized:

from esperanto.common_types.task_type import EmbeddingTaskType

# Query embeddings
query_model = AIFactory.create_embedding(
    "azure",
    "my-embedding-deployment",
    config={"task_type": EmbeddingTaskType.RETRIEVAL_QUERY}
)

# Document embeddings
document_model = AIFactory.create_embedding(
    "azure",
    "my-embedding-deployment",
    config={"task_type": EmbeddingTaskType.RETRIEVAL_DOCUMENT}
)

query_emb = query_model.embed(["search query"])
doc_emb = document_model.embed(["document 1", "document 2"])

Speech-to-Text

Available Models:

• whisper - Whisper model via Azure deployment

Configuration:

from esperanto.factory import AIFactory

transcriber = AIFactory.create_speech_to_text(
    "azure",
    "your-whisper-deployment",
    config={
        "timeout": 300.0,  # 5 minutes for large files
        "api_key": "your-key",
        "azure_endpoint": "https://your-resource.openai.azure.com/",
        "api_version": "2024-02-01"
    }
)

Example - Basic Transcription:

from esperanto.factory import AIFactory

# Create speech-to-text model
model = AIFactory.create_speech_to_text("azure", "my-whisper-deployment")

# Transcribe audio
response = model.transcribe("meeting.mp3")
print(response.text)

Example - With Language and Context:

# Improve accuracy
response = model.transcribe(
    "podcast.wav",
    language="en",
    prompt="This is a technical discussion about AI and machine learning"
)
print(f"Transcription: {response.text}")

Example - Async Transcription:

async def transcribe_batch():
    model = AIFactory.create_speech_to_text("azure", "my-whisper-deployment")

    files = ["audio1.mp3", "audio2.wav", "audio3.m4a"]
    for audio_file in files:
        response = await model.atranscribe(audio_file)
        print(f"{audio_file}: {response.text[:100]}...")

Text-to-Speech

Available Models:

• tts-1 - Standard quality
• tts-1-hd - High definition quality

Available Voices: Same as OpenAI:

• alloy - Balanced, neutral voice
• echo - Male voice with clarity
• fable - British accent, storytelling tone
• onyx - Deep, authoritative male voice
• nova - Young, energetic female voice
• shimmer - Warm, expressive female voice

Configuration:

from esperanto.factory import AIFactory

speaker = AIFactory.create_text_to_speech(
    "azure",
    "your-tts-deployment",
    config={
        "timeout": 300.0,
        "api_key": "your-key",
        "azure_endpoint": "https://your-resource.openai.azure.com/",
        "api_version": "2024-02-01"
    }
)

Example - Basic Speech Generation:

from esperanto.factory import AIFactory

# Create text-to-speech model
model = AIFactory.create_text_to_speech("azure", "my-tts-deployment")

# Generate speech
response = model.generate_speech(
    text="Hello from Azure Text-to-Speech!",
    voice="alloy",
    output_file="greeting.mp3"
)

print(f"Generated {len(response.audio_data)} bytes of audio")

Example - Voice Customization:

# HD quality with speed control
response = model.generate_speech(
    text="This speech has custom speed settings",
    voice="nova",
    speed=1.2,  # Faster speech
    output_file="fast_speech.mp3"
)

Example - Async Generation:

async def generate_audio_async():
    model = AIFactory.create_text_to_speech("azure", "my-tts-deployment")

    response = await model.agenerate_speech(
        text="Async speech generation",
        voice="shimmer",
        output_file="async_output.mp3"
    )
    return response

Advanced Features

Modality-Specific Configuration

Use different Azure resources for different capabilities:

# Set environment variables for different resources
export AZURE_OPENAI_API_KEY_LLM="key-for-llm"
export AZURE_OPENAI_ENDPOINT_LLM="https://llm-resource.openai.azure.com/"

export AZURE_OPENAI_API_KEY_EMBEDDING="key-for-embeddings"
export AZURE_OPENAI_ENDPOINT_EMBEDDING="https://embedding-resource.openai.azure.com/"

export AZURE_OPENAI_API_KEY_STT="key-for-stt"
export AZURE_OPENAI_ENDPOINT_STT="https://stt-resource.openai.azure.com/"

export AZURE_OPENAI_API_KEY_TTS="key-for-tts"
export AZURE_OPENAI_ENDPOINT_TTS="https://tts-resource.openai.azure.com/"

# Each capability uses its specific configuration
llm = AIFactory.create_language("azure", "gpt4-deployment")  # Uses LLM vars
embedder = AIFactory.create_embedding("azure", "embed-deployment")  # Uses EMBEDDING vars
stt = AIFactory.create_speech_to_text("azure", "whisper-deployment")  # Uses STT vars
tts = AIFactory.create_text_to_speech("azure", "tts-deployment")  # Uses TTS vars

Enterprise Features

• Private Networks: VNet integration for secure communication
• Compliance: SOC 2, HIPAA, and other certifications
• Regional Control: Deploy in specific Azure regions for data residency
• Integrated Billing: Usage appears on your Azure subscription
• Enterprise Support: SLA guarantees and Microsoft support

Timeout Configuration

Customize request timeouts:

# LLM with custom timeout
model = AIFactory.create_language(
    "azure",
    "my-deployment",
    config={"timeout": 120.0}  # 2 minutes
)

# STT with longer timeout for large files
transcriber = AIFactory.create_speech_to_text(
    "azure",
    "whisper-deployment",
    config={"timeout": 600.0}  # 10 minutes
)

LangChain Integration

Convert to LangChain models:

from esperanto.factory import AIFactory

model = AIFactory.create_language("azure", "my-gpt4-deployment")
langchain_model = model.to_langchain()

# Use with LangChain
from langchain.chains import ConversationChain
chain = ConversationChain(llm=langchain_model)

Troubleshooting

Common Errors

Authentication Error:

Error: Invalid API key

Solution: Verify your API key is correct in the Azure Portal under "Keys and Endpoint".

Deployment Not Found:

Error: The API deployment for this resource does not exist

Solution: Ensure you're using the correct deployment name (not model name). Check your deployments in Azure Portal.

Incorrect Endpoint:

Error: Failed to connect

Solution: Verify your endpoint URL is correct. It should be in the format: https://your-resource-name.openai.azure.com/

API Version Error:

Error: Invalid API version

Solution: Use a valid API version. Common versions:

• 2024-02-01 - General use
• 2024-12-01-preview - Embeddings with custom dimensions

Rate Limit Error:

Error: Rate limit exceeded

Solution: Check your Azure OpenAI quota and request an increase if needed.

Best Practices

1. Use Deployment Names: Always use your Azure deployment names, not OpenAI model names.

2. Environment Variables: Use modality-specific env vars when using multiple Azure resources.

3. API Versions: Keep API versions updated. Check Azure documentation for the latest.

4. Private Endpoints: Use VNet integration for production workloads requiring private connectivity.

5. Regional Deployment: Deploy in regions close to your users for better latency.

6. Monitoring: Use Azure Monitor to track usage and performance.

7. Cost Management: Set up Azure cost alerts to monitor OpenAI usage.

Azure vs OpenAI

Choose Azure OpenAI if you:

• Need enterprise compliance (HIPAA, SOC 2, etc.)
• Require regional data residency
• Want integrated Azure billing
• Need private network connectivity
• Already use Azure infrastructure
• Require Microsoft support SLAs

Choose OpenAI if you:

• Want latest features immediately
• Prefer simpler setup
• Don't need enterprise features
• Want direct OpenAI billing

See Also

• Language Models Guide
• Embeddings Guide
• Speech-to-Text Guide
• Text-to-Speech Guide
• OpenAI Provider
• Google Provider