Model Node

Definition

The Model node invokes a large language model (LLM) to process user input — natural language, uploaded files, or images — and produce a response.

Use Cases

The Model node is the core node of a Workflow. It leverages the LLM's conversation, generation, classification, and processing capabilities to handle a wide range of tasks at different stages of a workflow:

• Intent Recognition — In customer service scenarios, classify and route user questions to different downstream flows.
• Text Generation — In content creation scenarios, generate text based on topics or keywords.
• Content Classification — In email batch processing, automatically classify emails as inquiries, complaints, or spam.
• Text Transformation — Translate user-provided text into a specified language.
• Code Generation — Generate business code or write test cases based on user requirements.
• RAG — In knowledge base Q&A, combine retrieved knowledge with the user's question to generate an accurate reply.
• Image Understanding — Use a vision-capable LLM to understand and answer questions about image content.
• File Analysis — Use an LLM to identify and analyze the contents of uploaded files.

Configuration

On the canvas, right-click or click the + at the end of the previous node to add a Model node.

1. Select a Model — Choose from GPT series (Azure OpenAI), Claude series (Anthropic), Gemini series (Vertex AI), and more. Consider reasoning capability, cost, response speed, and context window size when choosing.
2. Configure Model Parameters — Control generation behavior with parameters like temperature, Top P, max tokens, and response format. Three presets are available: Creative, Balanced, and Precise. If you're unfamiliar with these settings, use the defaults. To enable image analysis, select a vision-capable model.
3. Fill in Context (Optional) — Context provides background information to the LLM, commonly used to pass in the output variable from a Knowledge Base node.
4. Write a Prompt — The Model node pro a prompt editor. For chat models, you can customize both the System prompt and the User message.

In the prompt editor, type / to open the variable insertion menu and insert upstream node variables as context.

Special Variable Types

Context Variable

The context variable is a special variable type designed for knowledge base retrieval. It can only be referenced inside a Model node. When the prompt references the output of a Knowledge Base node, that output is passed in as a context variable.

• Its value is a structured list of text segments retrieved from the knowledge base.
• Referenced in the prompt via Context.
• The LLM uses these retrieved results as background knowledge to answer the user's question, implementing RAG (Retrieval-Augmented Generation).

Image Variable

Image variables pass image content to vision-capable LLMs (e.g., GPT-4V, Claude 3).

• Supports image URLs or Base64-encoded image data.
• Typically sourced from a file upload node or an image-type input variable.
• Once referenced in the prompt, the LLM can understand, describe, or analyze the image.
• Only effective when the selected model has vision capability.

File Variable

File variables pass file content (PDF, Word, TXT, etc.) to the Model node for processing.

• Typically sourced from a file upload node or a file-type workflow input variable.
• Supports multiple file formats (within the range supported by the Start node).
• Suitable for document Q&A, document summarization, and content extraction scenarios.

Advanced Settings

Error Retry — When a node encounters certain errors, retrying usually resolves the issue. When enabled, the node will automatically retry according to a preset strategy. You can configure the maximum retry count and interval.

• Maximum retries: 10
• Maximum retry interval: 5000 ms

Exception Handling — Provides flexible error handling strategies. When an error occurs, you can throw an error without interrupting the main flow, or continue via a fallback path.