The Parameter Extraction Node leverages large language models (LLMs) to extract structured parameters from unstructured content. The model automatically identifies and extracts corresponding parameter values from input content, outputting structured data for use by downstream nodes. It supports plain-text extraction; when Vision-Language (VL) series models are selected, it also supports parameter extraction from images and videos. Typical use cases include form information extraction, tool parameter preparation, data format conversion, and image information structuring.
Usage Example
The Parameter Extraction Node extracts structured information from users’ natural-language inputs for use by subsequent nodes.
Node Integration
Method 1: Add via the + Button
-
Hover your mouse over any node on the canvas (e.g., the Start node); a
+button automatically appears on its right side. -
Click the
+button and select Variable Parameter Extraction from the pop-up node list. The node is automatically added to the canvas and connected to the upstream node.
Method 2: Drag & Drop from the Node Library
- In the left-side node library, locate and select Variable Parameter Extraction.
- Drag the node to an appropriate location on the canvas, then drag a connection line from the edge of an upstream node to this node.
Core Configuration
Model Selection
Select the large language model used for parameter extraction. This model interprets the semantic meaning of input content and converts unstructured information into structured parameters. Supported models include Tongyi Qwen series models and third-party models (e.g., DeepSeek). For the latest list of supported models, refer to the console.
Click the configuration icon (⚙️) next to the model selector, enable the toggle, and configure the following parameters:
| Parameter | Default Value | Description |
|---|---|---|
| Max Response Length | 1024 | Maximum number of tokens in the model’s output. |
| top_p | 0.80 | Controls output diversity — higher values yield more diverse results. |
| temperature | 0.70 | Controls output randomness — higher values yield more random results. |
| enable_thinking | Enabled | Enables deep reasoning mode; outputs step-by-step reasoning traces. |
| thinking_budget | 4000 | Maximum number of tokens allowed for chain-of-thought reasoning. |
| result_format | message | Format of the returned result. |
| enable_search | Disabled | Enables web search functionality. |
| When selecting VL-series models (e.g., Qwen-VL-Plus-Latest, Qwen-VL-Max-Latest), two additional configuration options appear: Vision Understanding Type and Model Input Parameters, enabling structured parameter extraction from images and videos. | — Vision Understanding Type | Specifies how the model interprets visual inputs. |
| Option | Model Input Parameter Name | Description |
|---|---|---|
| Image (default) | vlImageUrl | Provide one or multiple images; the model treats each as independent content and matches them to questions accordingly. |
| Video Frames | vlImageUrl | Provide ≥4 images representing consecutive frames of the same video; the model processes them sequentially as a unified sequence. |
| Full Video | vlVideoUrl | Provide a direct URL to a complete video; the model automatically parses and understands its content without manual frame extraction. |
| — Model Input Parameter | Used to pass image/video data. | Parameter name |
| Applicable Vision Understanding Type | Description | vlImageUrl |
| Image / Video Frame | List of image URLs — supports direct entry or referencing upstream variables. | vlVideoUrl |
Input
The content from which parameters need to be extracted. Supports referencing user input or outputs from upstream nodes. When using VL models, this input field remains for textual input (e.g., user queries or descriptions), while images/videos are passed separately via model input parameters.
Extracted Parameters
Define the list of parameters to extract from the input text. The model identifies and extracts corresponding values based on each parameter’s name, type, and description.
| Field | Required | Description |
|---|---|---|
| Name | Yes | Parameter name (e.g., meeting_date, city). Serves as the key name for the output variable. |
String.
Description
Yes
Natural-language description helping the model understand what value to extract. Clearer descriptions improve extraction accuracy.
Required
No
Indicates whether the parameter must be successfully extracted. Used only as a hint during model inference—not enforced as strict validation.
Prompt
Provide additional context, examples, or constraints to improve extraction accuracy and consistency.
Memory
Disabled by default. When enabled, the model retains conversation history—supporting multi-turn dialogue scenarios.
| Cache Type | Description |
|---|---|
| Node-level Cache | The model remembers only the conversation history within this node. Configure memory turns (1–50, default: 3); one input-output pair counts as one turn. |
| Custom Cache | The model retains global context. Select a context variable (e.g., built-in historyList). |
Node Output
The Parameter Extraction Node outputs all custom-extracted parameters plus built-in status variables, available for reference by downstream nodes.
-
Custom Extracted Parameters
Each parameter defined in Extracted Parameters generates an output variable with the same name. - Built-in Status Variables
| Variable Name | Type | Description |
|---|---|---|
_is_completed | Boolean | Indicates whether parameter extraction completed successfully. true means all required parameters were extracted; false indicates at least one required parameter was missing. |
_reason | String | When _is_completed is false, explains why extraction failed. Empty string if extraction succeeded. |
Common Use Cases
| Paired Node | Description |
|---|---|
| Conditional Node | Uses _is_completed to determine extraction completeness and route logic accordingly (e.g., proceed if complete; prompt user for missing info otherwise). |
| API Node | Passes extracted structured parameters as input to API calls—for example, extracting date and city before invoking a weather API. |
| LLM Node | Feeds extracted parameters to another LLM for further processing—for example, generating a meeting invitation email based on extracted meeting details. |
| Variable Processing Node | Combines multiple extracted parameters into a single JSON object for unified downstream consumption. |
| Image Parsing Node | When using VL models: first parse image content via the Image Parsing Node, then extract structured information (e.g., product name, color, specs) from the image using the Parameter Extraction Node. |
| Video Parsing Node | When using VL models: first parse video content via the Video Parsing Node, then extract structured information (e.g., scene, characters, actions) from the video using the Parameter Extraction Node. |