LLM

Action ID: llm

Description

Use a large language model such as GPT

Input Parameters

Name	Type	Required	Default	Description
human_message	string	✓	-	The prompt that is fed to the model (max 640,000 characters)
system_message	string	-	null	Instructions for the AI assistant on how to behave and respond (max 640,000 characters)
chat_history_id	string	-	null	The chat history ID used to retrieve conversation history. If not provided, a new chat history will be created
model	dropdown	-	DeepSeek V3	The AI model to use for generating the response
temperature	number	-	0.5	Controls randomness in responses (0.0-1.0). Higher values result in more creative responses

View JSON Schema

{
  "description": "LLM node input.",
  "properties": {
    "human_message": {
      "description": "The prompt that is fed to the model.",
      "maxLength": 640000,
      "title": "Prompt",
      "type": "string"
    },
    "system_message": {
      "anyOf": [
        {
          "maxLength": 640000,
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "This represents a system message, which tells the model how to behave.",
      "title": "System Message"
    },
    "chat_history_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "The chat history id used to retrieve the chat history. If not provided, a new chat history will be created.",
      "title": "Chat History ID"
    },
    "model": {
      "default": "DeepSeek V3",
      "description": "The AI model to use to generate the response.",
      "enum": [
        "OpenAI GPT-4o Mini",
        "DeepSeek V3",
        "Google Gemini 2.0 Flash-Lite",
        "Google Gemini 2.0 Flash",
        "Google Gemini 1.5 Flash",
        "Google Gemini 1.5 Pro",
        "Anthropic Claude 3 Haiku"
      ],
      "title": "Model",
      "type": "string"
    },
    "temperature": {
      "default": 0.5,
      "description": "The temperature to use when generating the response. Higher temperatures will result in more creative responses.",
      "maximum": 1,
      "minimum": 0,
      "title": "Temperature",
      "type": "number"
    }
  },
  "required": [
    "human_message"
  ],
  "title": "LLMQuestionAnsweringInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
content	string	The AI-generated response text
chat_history_id	string	The chat history ID used to maintain conversation context

View JSON Schema

{
  "description": "LLM node output.",
  "properties": {
    "content": {
      "title": "Content",
      "type": "string"
    },
    "chat_history_id": {
      "title": "The chat history id used to retrieve the chat history.",
      "type": "string"
    }
  },
  "required": [
    "content",
    "chat_history_id"
  ],
  "title": "LLMQuestionAnsweringNodeOutput",
  "type": "object"
}

How It Works

This node processes your text prompts through powerful large language models to generate intelligent responses. When you provide a human message (your question or request), the node sends it to the selected AI model along with any optional system instructions that define the model's behavior. The model analyzes the input, considers any chat history for context continuity, and generates a relevant response. The temperature parameter controls the randomness of outputs, with lower values producing more focused and deterministic responses, while higher values enable more creative and varied outputs.

Usage Examples

Example 1: Simple Question Answering

Input:

human_message: "What are the three primary colors and why are they important in art?"
model: "DeepSeek V3"
temperature: 0.3

Output:

content: "The three primary colors are red, blue, and yellow. They are important in art because they cannot be created by mixing other colors, and all other colors can be created by combining these primaries in various proportions. This fundamental principle forms the basis of color theory and allows artists to create a full spectrum of colors from just these three base colors."
chat_history_id: "chat_abc123def456"

Example 2: Content Generation with System Instructions

Input:

human_message: "Write a professional email apologizing for a delayed shipment"
system_message: "You are a professional customer service representative. Write concise, empathetic emails that acknowledge issues and provide solutions."
model: "OpenAI GPT-4o Mini"
temperature: 0.7

Output:

content: "Subject: Sincere Apologies for Your Delayed Shipment\n\nDear Valued Customer,\n\nI sincerely apologize for the delay in your recent order. We understand how frustrating this must be, and we take full responsibility for not meeting our delivery commitment.\n\nYour package is now en route and should arrive within 2-3 business days. To make up for this inconvenience, we've applied a 15% discount to your next purchase.\n\nThank you for your patience and understanding.\n\nBest regards,\nCustomer Service Team"
chat_history_id: "chat_789ghi012jkl"

Example 3: Continuing a Conversation

Input:

human_message: "Can you elaborate on the mixing ratios?"
chat_history_id: "chat_abc123def456"
model: "Google Gemini 2.0 Flash"
temperature: 0.5

Output:

content: "Certainly! When mixing primary colors, the ratios determine which secondary or tertiary color you'll create. For example:\n\n- Equal parts red + blue = purple\n- Equal parts blue + yellow = green\n- Equal parts red + yellow = orange\n\nBy adjusting the ratios (like 2 parts red to 1 part blue), you can create variations like violet or burgundy. The possibilities are virtually endless, which is why understanding primary colors is so fundamental to color mixing."
chat_history_id: "chat_abc123def456"

Common Use Cases

• Customer Support Automation: Generate intelligent responses to customer inquiries, support tickets, and frequently asked questions

• Content Creation: Write blog posts, product descriptions, marketing copy, social media content, and advertising text

• Data Analysis: Analyze text data, extract insights, summarize reports, and identify patterns in large datasets

• Code Assistance: Generate code snippets, explain technical concepts, debug issues, and provide programming guidance

• Translation and Localization: Translate content between languages while maintaining context and cultural nuances

• Educational Content: Create explanations, tutorials, study guides, and answer student questions on various topics

• Conversational AI: Build chatbots, virtual assistants, and interactive conversation systems with maintained context

Error Handling

Error Type	Cause	Solution
Empty Message Error	human_message parameter is empty or missing	Ensure human_message contains text before sending to the model
Token Limit Exceeded	Input message exceeds 640,000 character limit	Reduce the length of your prompt or split it into multiple requests
Invalid Chat History	chat_history_id doesn't exist or is expired	Start a new conversation by omitting chat_history_id or verify the ID is correct
Model Unavailable	Selected model is temporarily down or unavailable	Switch to an alternative model or retry after a few minutes
Temperature Out of Range	Temperature value is not between 0.0 and 1.0	Set temperature to a value within the valid range (0.0-1.0)
Rate Limit Error	Too many requests sent in a short time period	Implement request throttling or wait before sending additional requests
Invalid System Message	System message contains unsupported content or formatting	Simplify system message and ensure it contains valid instructions only

Notes

• Model Selection: DeepSeek V3 offers excellent performance for most tasks. Use GPT-4o Mini for cost efficiency, Gemini for multimodal capabilities, and Claude for detailed analysis

• Temperature Control: Use 0.0-0.3 for factual, deterministic responses (data analysis, extraction). Use 0.7-1.0 for creative tasks (content generation, brainstorming)

• System Messages: Define the model's role, tone, and behavior. Examples: "You are a technical expert", "Respond concisely", "Use a friendly tone"

• Chat History: Reuse chat_history_id to maintain conversation context across multiple turns. Essential for building conversational experiences

• Prompt Engineering: Clear, specific prompts produce better results. Include context, desired format, and any constraints in your prompt

• Character Limits: Both human_message and system_message support up to 640,000 characters, allowing for extensive context and detailed instructions

• Response Quality: More advanced models (Gemini Pro, Claude) provide better reasoning but cost more. Balance quality needs with budget constraints

• Workflow Integration: Chain multiple LLM nodes together for complex tasks like draft-review-refine workflows or multi-step analysis