Azure OpenAI Models

A drag-and-drop component for integrating Azure OpenAI services into your workflow. Configure deployment settings and model parameters for enterprise-grade AI capabilities.

Azure OpenAI component interface and configuration

Azure Setup Required: This component requires a valid Azure OpenAI service setup. Ensure you have created the appropriate deployment and have the necessary permissions before using this component in production.

Component Inputs

Input: Text input for the model
Example: "Explain quantum computing to a high school student."
System Message: System prompt to guide model behavior
Example: "You are an educational assistant who explains complex topics using simple analogies."
Stream: Toggle for streaming responses
Example: true (for real-time token streaming) or false (for complete response)
Azure Endpoint: Your Azure OpenAI service endpoint
Example: "https://your-resource-name.openai.azure.com/"
Deployment Name: Name of your model deployment
Example: "gpt-4", "gpt-35-turbo"
API Key: Your Azure OpenAI API key
Example: "cdd78d98abcdef12345678910abcdef"
API Version: Azure OpenAI API version
Example: "2024-06-01"

Component Outputs

Text: Generated text output
Example: "Quantum computing is like a special kind of calculator that uses the weird rules of very tiny particles..."
Language Model: Model information and metadata
Example: deployment: gpt-4, usage: {prompt_tokens: 35, completion_tokens: 120, total_tokens: 155}

Model Parameters

Temperature

Controls randomness in the generated text - higher values create more diverse outputs

Default: 0.7
Range: 0.0 to 2.0
Recommendation: Lower (0.0-0.3) for factual tasks, Higher (0.7-1.0) for creative applications

Max Tokens

Maximum number of tokens to generate in the response

Default: Varies by model
Range: 1 to model maximum (e.g., 4096 for GPT-4)
Recommendation: Set according to expected response length and keep buffer for longer responses

Implementation Example

// Basic configuration
const azureOpenAI = {
  azureEndpoint: "https://your-resource-name.openai.azure.com/",
  deploymentName: "gpt-4",
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  apiVersion: "2024-06-01",
  systemMessage: "You are a helpful assistant."
};

// Advanced configuration
const advancedAzureOpenAI = {
  azureEndpoint: "https://your-resource-name.openai.azure.com/",
  deploymentName: "gpt-35-turbo",
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  apiVersion: "2024-06-01",
  maxTokens: 2000,
  temperature: 0.5,
  stream: true,
  frequencyPenalty: 0.5,
  presencePenalty: 0.0,
  topP: 0.95
};

// Usage example
async function generateResponse(input) {
  const response = await azureOpenAIComponent.generate({
    input: input,
    systemMessage: "You are an expert in finance.",
    temperature: 0.3
  });
  
  return response.text;
}

Use Cases

Enterprise Applications: Build AI solutions with Microsoft's compliance and security standards
Regulated Industries: Deploy AI in healthcare, finance, and government with proper data governance
Content Creation: Generate marketing copy, documentation, and reports with enterprise controls
Conversational Agents: Create enterprise-ready chatbots and virtual assistants
Data Processing: Extract insights and summaries from documents with proper data residency

Useful Resources

Best Practices

Secure API keys using Azure Key Vault or environment variables
Monitor usage and costs through Azure portal
Implement proper error handling for API failures
Choose appropriate deployment regions for data residency requirements
Regularly update to the latest API versions for new features

Documentation