OpenAI Models

A drag-and-drop component for integrating OpenAI's language models. Configure model parameters and connect inputs/outputs to leverage OpenAI's powerful AI capabilities.

OpenAI component interface and configuration

API Key Notice: A valid OpenAI API key is required to use this component. Ensure your API key has sufficient quota and appropriate rate limits for your application needs.

Component Inputs

Input: Text input for the model
Example: "Explain the concept of machine learning in simple terms."
System Message: System prompt to guide model behavior
Example: "You are a helpful AI assistant that explains complex topics in simple language."
Stream: Toggle for streaming responses
Example: true (for real-time token streaming) or false (for complete response)
Model Name: The OpenAI model to use
Example: "gpt-4o-mini", "gpt-4o", "gpt-4-turbo"
OpenAI API Key: Your API authentication key
Example: "sk-abcdefghijklmnopqrstuvwxyz123456789"
OpenAI API Base: Custom API endpoint (optional)
Example: "https://custom-openai-endpoint.com/v1"
JSON Mode: Toggle for JSON output format
Example: true (forces model to return valid JSON)

Component Outputs

Text: Generated text output
Example: "Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed..."
Language Model: Model information and metadata
Example: model: gpt-4o-mini, usage: {prompt_tokens: 42, completion_tokens: 128, total_tokens: 170}

Generation Parameters

Max Tokens

Maximum tokens to generate in the response

Default: Model-dependent (e.g., 4096 for most GPT-4 models)
Range: 1 to model maximum
Recommendation: Set based on expected response length

Temperature

Controls randomness in the output - higher values increase creativity

Default: 0.10
Range: 0.0 to 2.0
Recommendation: Lower (0.0-0.3) for factual/consistent responses, Higher (0.7-1.0) for creative tasks

Seed

Random seed for reproducible results

Default: 1
Range: Integer values
Recommendation: Use consistent seed values when reproducibility is important

Implementation Example

// Basic configuration
const openAI = {
  modelName: "gpt-4o-mini",
  openAIApiKey: process.env.OPENAI_API_KEY,
  systemMessage: "You are a helpful assistant."
};

// Advanced configuration
const advancedOpenAI = {
  modelName: "gpt-4o",
  openAIApiKey: process.env.OPENAI_API_KEY,
  openAIApiBase: "https://api.openai.com/v1",
  temperature: 0.7,
  maxTokens: 1000,
  jsonMode: true,
  stream: true,
  seed: 12345,
  modelKwargs: {
    response_format: { type: "json_object" }
  }
};

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

Use Cases

Chatbots and Virtual Assistants: Create conversational agents using streaming responses
Content Generation: Create articles, blog posts, and creative writing
Data Extraction: Use JSON mode to extract structured information from unstructured text
RAG Applications: Generate responses based on retrieved context
Code Generation: Create code snippets and programming solutions

Useful Resources

Best Practices

Use environment variables for API keys in production environments
Monitor token usage to manage costs
Implement proper error handling for API failures
Use system messages effectively to guide model behavior
Set appropriate temperature values based on your specific use case

Documentation