LLama 3

Action ID: llama3

Description

Generate AI-powered text responses using Meta's LLama 3 language model through the PixelML platform. This node enables conversational AI, content generation, and intelligent text processing capabilities.

Connection

Name	Description	Required	Category
PixelML Connection	The PixelML connection to call PixelML API.	True	pixelml

Input Parameters

Name	Type	Required	Default	Description
background	string	✓	-	The background prompt sent to the LLama 3 model (system context or instructions)
prompt	string	✓	-	The prompt sent to the LLama 3 model (user query or task)

View JSON Schema

{
  "description": "LLama 3 node input.",
  "properties": {
    "background": {
      "title": "The background prompt sent to the LLama 3 model",
      "type": "string"
    },
    "prompt": {
      "title": "The prompt sent to the LLama 3 model",
      "type": "string"
    }
  },
  "required": [
    "background",
    "prompt"
  ],
  "title": "LLama3NodeInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
content	string	The generated text response from the LLama 3 model

View JSON Schema

{
  "description": "LLama 3 node output.",
  "properties": {
    "content": {
      "title": "Content",
      "type": "string"
    }
  },
  "required": [
    "content"
  ],
  "title": "LLama3NodeOutput",
  "type": "object"
}

How It Works

This node sends your prompt and background context to the LLama 3 language model via the PixelML API. The background parameter sets the model's behavior and role (like a system message), while the prompt contains the specific user query or task. The model processes both inputs to generate contextually appropriate text responses, returning the generated content as a string output.

Usage Examples

Example 1: Customer Support Assistant

Input:

background: "You are a helpful customer support agent for an e-commerce platform. Be friendly, professional, and provide clear solutions."
prompt: "A customer is asking about how to track their order. Their order number is #ORD-12345."

Output:

content: "I'd be happy to help you track your order #ORD-12345! You can track your order in two ways:

1. Log into your account and go to 'My Orders' section where you'll see real-time tracking information
2. Click the tracking link sent to your email when the order shipped

If you're having trouble accessing either option, please let me know and I'll look up the current status for you directly!"

Example 2: Content Summarization

Input:

background: "You are a professional content summarizer. Create concise, accurate summaries that capture the main points."
prompt: "Summarize this article: 'Artificial intelligence is transforming healthcare through improved diagnostics, personalized treatment plans, and drug discovery. Machine learning algorithms can analyze medical images faster than human radiologists, identifying patterns that might be missed. AI-powered systems are also helping predict patient outcomes and recommend optimal treatment strategies based on vast amounts of medical data.'"

Output:

content: "AI is revolutionizing healthcare in three key areas: 1) Enhanced diagnostics through faster and more accurate medical image analysis, 2) Personalized treatment recommendations powered by patient data analysis, and 3) Accelerated drug discovery processes. These AI systems can detect patterns invisible to humans and predict patient outcomes more effectively."

Example 3: Code Generation Assistant

Input:

background: "You are an expert Python programmer who writes clean, well-documented code with best practices."
prompt: "Write a function that validates email addresses using regex and returns True if valid, False otherwise."

Output:

content: "Here's a Python function to validate email addresses:

```python
import re

def validate_email(email: str) -> bool:
    """
    Validates an email address using regex pattern matching.

    Args:
        email: The email address string to validate

    Returns:
        True if email is valid, False otherwise
    """
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return bool(re.match(pattern, email))

# Example usage:
# validate_email('[email protected]')  # Returns: True
# validate_email('invalid.email')     # Returns: False

This function checks for standard email format with local part, @ symbol, domain, and top-level domain."

## Common Use Cases

- **Conversational Chatbots**: Build intelligent chatbots for customer service, technical support, or general assistance
- **Content Generation**: Create blog posts, product descriptions, marketing copy, or social media content
- **Text Summarization**: Condense long documents, articles, or reports into concise summaries
- **Code Assistance**: Generate code snippets, explain programming concepts, or debug issues
- **Data Analysis**: Interpret data patterns, generate insights, or create analytical reports from structured data
- **Translation and Localization**: Translate content between languages while maintaining context and tone
- **Creative Writing**: Generate stories, scripts, poetry, or other creative text content

## Error Handling

| Error Type | Cause | Solution |
| :--- | :--- | :--- |
| API Connection Failed | PixelML connection is invalid or expired | Verify your PixelML credentials and ensure the connection is active |
| Empty Prompt | The prompt parameter is empty or null | Provide a valid prompt string with your query or task |
| Empty Background | The background parameter is empty or null | Provide context in the background field to guide the model's behavior |
| Rate Limit Exceeded | Too many requests sent to PixelML API | Implement delays between requests or upgrade your PixelML plan |
| Token Limit Exceeded | Combined prompt and background exceed token limits | Reduce the length of your input text or split into smaller chunks |
| Model Timeout | Request took too long to process | Simplify your prompt or retry the request |
| Invalid Response Format | API returned unexpected response structure | Check PixelML service status and verify API compatibility |

## Notes

- **Background vs Prompt**: Use background for system instructions and role definition, use prompt for the actual user query or task to ensure proper context.
- **Token Awareness**: LLama 3 has token limits for input and output. Keep prompts concise and break large tasks into smaller chunks.
- **Prompt Engineering**: The quality of responses depends heavily on prompt clarity. Be specific about format, length, and style requirements.
- **Response Variability**: AI models can produce different outputs for the same prompt. Use the background field to constrain behavior for more consistent results.
- **Content Filtering**: The model may refuse inappropriate requests. Ensure prompts comply with PixelML's content policies.
- **Language Support**: While LLama 3 supports multiple languages, performance is best with English prompts and instructions.
- **Cost Management**: Each API call consumes credits or resources. Monitor usage and implement caching for repeated queries.
- **Error Recovery**: Always handle the output content field and implement fallback logic for when the model doesn't generate expected responses.