The Image Parsing Node extracts structured parameters from input images. After configuring a parser, it generates structured output variables based on image content—ideal for use cases such as image content extraction, image information statistics, and optical character recognition (OCR).
Usage Example
For example, to parse an image and generate a content summary:This workflow receives a URL pointing to an image, uses the Image Parsing Node to extract structured content from it, and then passes the result to a Large Language Model (LLM) Node to generate a concise summary of the image.
Adding the Node
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, then select Data → Image Parsing from the pop-up node list. The node is automatically added to the canvas and connected to the upstream node.
Method 2: Drag from the Node Library
- In the left-side node library, locate and select Data → Image Parsing.
- Drag the node onto the canvas at your desired location, then drag a connection line from the edge of an upstream node to the Image Parsing Node.
Core Configuration
Input
Specify the image(s) to be parsed. Supports either direct URL input or referencing variables from upstream nodes. Choose the input method using the dropdown:
| Method | Description | Use Case |
|---|---|---|
| Reference | Reference a variable from an upstream node | Image URL (String type) from an upstream node, or File-type image uploaded via the Start Node. |
| Input | Directly enter an image URL | Fixed or externally sourced image URLs. |
- Maximum file size: 20 MB per file
- Supports both single-file and batch (list-based) parsing
- Supported formats:
.png,.jpg,.jpeg,.bmp,.gif
Parser Selection
Select the parser used to analyze the image. Different parsers are optimized for different image types and vary in parsing performance and billing model.
| Parser Type | Applicable Scenario | Billing | Description |
|---|---|---|---|
| LLM Document Parser | Document-style images (e.g., containing text, tables) | Free (currently) | Uses built-in large language models to extract hierarchical document structure and layout information; supports Markdown output. Prompt customization is not supported. |
| Qwen-VL Parser | General-purpose images (e.g., photos, screenshots, illustrations) | Incurs charges for Qwen-VL model inference | Leverages Qwen-VL multimodal vision-language models. Requires custom prompts to guide parsing behavior. |
- Model Selection:
| Option | Description |
|---|---|
qwen-vl-max | Highest-performing model in the Qwen2.5-VL series. |
qwen-vl-plus | Faster variant in the Qwen2.5-VL series—offers a balanced trade-off between speed, accuracy, and cost. |
- Prompt: A prompt guiding how the model should interpret the image. Supports both static text and dynamic variable insertion.
Node Output
The Image Parsing Node outputs structured results based on image content, including total image count and an array of per-image parsing results.
| Variable Name | Type | Description |
|---|---|---|
total | Number | Total number of images processed. |
images | Array<Object> | Array of parsing results—one object per image. |
images array:
| Field Name | Type | Description |
|---|---|---|
uniqueId | String | Unique identifier for this parsing result. |
markdownContent | String | Markdown-formatted content: • For LLM Document Parser: extracted document structure. • For Qwen-VL Parser: Markdown-formatted image link. |
text | String | Plain-text content: • For LLM Document Parser: extracted plain text. • For Qwen-VL Parser: typically empty. |
llmResult | String | LLM-generated description: • For Qwen-VL Parser: descriptive caption generated by the model. • For LLM Document Parser: may be empty. |
- With LLM Document Parser, prefer
markdownContentandtextfor document content. - With Qwen-VL Parser, prefer
llmResultfor image descriptions.
Common Integration Patterns
| Paired Node | Typical Use Case |
|---|---|
| Start Node → Image Parsing Node | Start Node provides either an image URL (String) or uploaded image file (File), which the Image Parsing Node processes. |
| Image Parsing Node → LLM Node | Image Parsing Node outputs structured data (e.g., llmResult or markdownContent from images), and the LLM Node generates summaries, answers questions, or extracts insights based on those results. |
| Image Parsing Node → Conditional Node | Branch logic based on parsing outcomes—e.g., image count (total) or presence/absence of specific content. |
| Image Parsing Node → Variable Assignment Node | Extract and transform specific fields from parsing results (e.g., format conversion or data cleaning). |
Frequently Asked Questions
Why does the LLM Document Parser return an empty images array?
The LLM Document Parser is specifically designed for document-style images (e.g., scanned PDF pages, slides, forms). If you input non-document images—such as photographs, illustrations, or screenshots—the parser cannot extract structured document elements, resulting in total=1 (indicating one image was detected) but an empty images array. In such cases, switch to the Qwen-VL Parser instead.