Expression¶
Class: ExpressionBlockV1
Source: inference.core.workflows.core_steps.formatters.expression.v1.ExpressionBlockV1
Create conditional logic and business rules in workflows using switch-case statements that evaluate conditions on input variables, optionally transform data with operations, and return different outputs based on which condition matches, enabling conditional execution, business logic implementation, rule-based decision making, and dynamic output generation workflows.
How This Block Works¶
This block implements conditional logic similar to switch-case or if-else-if statements in programming. The block:
- Receives input data as a dictionary of named variables from workflow steps
- Optionally applies data transformations using operations:
- Performs operations on data variables before condition evaluation
- Uses the same operation system as Property Definition block
- Transforms data (e.g., extract properties, filter, select) to prepare variables for conditions
- Stores transformed values as variables for use in conditions
- Evaluates switch-case statements sequentially:
- Tests each case condition in order until one matches
- Stops at the first matching case and returns its result
- If no case matches, returns the default result
- Evaluates conditions using a flexible expression system:
- Binary Statements: Compare two values using operators (==, !=, >, <, >=, <=, contains, startsWith, endsWith, in, any in, all in)
- Unary Statements: Test single values (Exists, DoesNotExist, is True, is False, is empty, is not empty)
- Statement Groups: Combine multiple statements with AND/OR operators for complex conditions
- Conditions can reference variables by name (DynamicOperand) or use literal values (StaticOperand)
- Returns results based on matched case:
Static Results: - Returns a fixed value defined in the case (e.g., "PASS", "FAIL", numeric values, strings)
Dynamic Results: - Returns a value from a variable (can reference any input variable) - Optionally applies operations to transform the variable before returning - Enables returning computed or extracted values as output
- Handles default case:
- If no case condition matches, returns the default result
- Default can be static or dynamic, just like case results
The block enables complex conditional logic by combining data transformation operations with flexible condition evaluation. Conditions can compare variables, test existence, check membership, perform string operations, and combine multiple conditions with logical operators. This makes it powerful for implementing business rules, validation logic, classification based on multiple criteria, and conditional data transformation.
Common Use Cases¶
- Business Logic Implementation: Implement conditional business rules and validation logic (e.g., validate detection matches reference, implement quality checks, enforce business rules), enabling business logic workflows
- Conditional Classification: Classify data based on multiple conditions and criteria (e.g., classify detections based on properties, categorize results by conditions, implement multi-criteria classification), enabling conditional classification workflows
- Validation and Quality Control: Validate data or predictions against reference values or thresholds (e.g., validate predictions match expected classes, check quality thresholds, verify compliance), enabling validation workflows
- Rule-Based Decision Making: Make decisions based on complex rule sets (e.g., approve/reject based on multiple criteria, route data based on conditions, make decisions using rule sets), enabling rule-based decision workflows
- Dynamic Output Generation: Generate different outputs based on input conditions (e.g., return different values based on conditions, generate conditional outputs, create dynamic results), enabling dynamic output workflows
- Multi-Condition Filtering: Implement complex filtering logic with multiple conditions (e.g., filter based on multiple criteria, apply complex conditional filters, implement multi-factor filtering), enabling conditional filtering workflows
Connecting to Other Blocks¶
This block receives data from workflow steps and produces conditional output:
- After model or analytics blocks to implement conditional logic on predictions or results (e.g., validate predictions, classify results, apply conditional rules), enabling conditional logic workflows
- After Property Definition blocks to use extracted properties in conditions (e.g., use extracted values in conditions, compare extracted properties, implement logic on extracted data), enabling property-to-condition workflows
- Before logic blocks like Continue If to provide conditional inputs (e.g., provide conditional values for filtering, supply conditional inputs for decisions), enabling expression-to-logic workflows
- Before data storage blocks to conditionally format or transform data for storage (e.g., conditionally format for storage, apply conditional transformations, prepare conditional outputs), enabling conditional storage workflows
- Before notification blocks to send conditional notifications (e.g., send conditional alerts, notify based on conditions, trigger conditional notifications), enabling conditional notification workflows
- In workflow outputs to provide conditional final outputs (e.g., conditional workflow outputs, dynamic result generation, conditional output formatting), enabling conditional output workflows
Requirements¶
This block requires input data as a dictionary where keys are variable names and values are data from workflow steps. The switch parameter defines cases with conditions and results. Conditions support binary comparisons (==, !=, >, <, >=, <=, contains, in, etc.), unary tests (Exists, is empty, etc.), and logical combinations (AND/OR). Data operations are optional and use the same operation system as Property Definition block. The block evaluates cases in order and returns the result of the first matching case, or the default result if no cases match. Results can be static values or dynamic values from variables (optionally with operations applied).
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/expression@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | โ |
data_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
Optional dictionary of operations to transform data variables before condition evaluation. Keys are variable names from data, values are lists of operations (same as Property Definition block). Operations are applied to transform variables before they are used in conditions. Useful for extracting properties, filtering, or transforming data before evaluation. Empty dictionary (default) means no transformations are applied.. | โ |
switch |
CasesDefinition |
Switch-case logic definition containing cases with conditions and results. Each case has a condition (StatementGroup with binary/unary statements) and a result (static value or dynamic variable). Cases are evaluated in order - first matching case's result is returned. Default result is returned if no cases match. Supports complex conditions with AND/OR operators, comparison operators (==, !=, >, <, >=, <=), string operations (contains, startsWith, endsWith), membership tests (in, any in, all in), and existence tests (Exists, is empty).. | โ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Expression in version v1.
- inputs:
Detections Filter,Absolute Static Crop,VLM As Classifier,Qwen-VL,Gaze Detection,Cache Get,Byte Tracker,Triangle Visualization,EasyOCR,Model Comparison Visualization,Detection Event Log,Stitch OCR Detections,Track Class Lock,YOLO-World Model,OC-SORT Tracker,Llama 3.2 Vision,Anthropic Claude,Heatmap Visualization,Stitch Images,Keypoint Visualization,Webhook Sink,Detections Stabilizer,Continue If,Dominant Color,Google Gemini,MoonshotAI Kimi,GLM-OCR,Instance Segmentation Model,VLM As Detector,QR Code Generator,SAM2 Video Tracker,Multi-Label Classification Model,Twilio SMS/MMS Notification,Line Counter,PLC ModbusTCP,Distance Measurement,Ellipse Visualization,Single-Label Classification Model,SmolVLM2,Keypoint Detection Model,Pixelate Visualization,Detections Transformation,Corner Visualization,Overlap Analysis,Rate Limiter,Switch Case,Buffer,Bounding Rectangle,Template Matching,Llama 3.2 Vision,Qwen2.5-VL,JSON Parser,Qwen 3.5 API,Per-Class Confidence Filter,LMM,Inner Workflow,Velocity,Roboflow Visual Search,Semantic Segmentation Model,OpenAI-Compatible LLM,Morphological Transformation,Color Visualization,Roboflow Dataset Upload,Dimension Collapse,Morphological Transformation,Camera Calibration,Motion Detection,Detection Offset,Google Vision OCR,Clip Comparison,Bounding Box Visualization,OpenAI,Halo Visualization,SAM3 Video Tracker,Object Detection Model,Identify Changes,First Non Empty Or Default,Detections Combine,Contrast Equalization,CSV Formatter,Environment Secrets Store,Mask Area Measurement,Expression,Detections Classes Replacement,Instance Segmentation Model,Model Monitoring Inference Aggregator,Multi-Label Classification Model,MQTT Writer,Cache Set,Delta Filter,PLC Reader,Image Threshold,Blur Visualization,Line Counter Visualization,Google Gemini,Microsoft SQL Server Sink,Single-Label Classification Model,Background Subtraction,VLM As Classifier,Object Detection Model,Identify Outliers,Path Deviation,QR Code Detection,Polygon Zone Visualization,Instance Segmentation Model,SAM 3,Twilio SMS Notification,Google Gemma API,OPC UA Writer Sink,Keypoint Detection Model,Image Stack,Reference Path Visualization,SAM 3 Interactive,Qwen3-VL,Stability AI Inpainting,Multi-Label Classification Model,Perception Encoder Embedding Model,Crop Visualization,Anthropic Claude,Dynamic Zone,Image Convert Grayscale,Camera Focus,Image Slicer,Overlap Filter,Florence-2 Model,Polygon Visualization,Time in Zone,MoonshotAI Kimi,Dynamic Crop,Qwen 3.6 API,Event Writer,Perspective Correction,Segment Anything 2 Model,Line Counter,Polygon Visualization,Clip Comparison,OCR Model,Roboflow Visual Search Classifier,Circle Visualization,Slack Notification,CogVLM,Contrast Enhancement,PLC EthernetIP,Grid Visualization,Mask Edge Snap,Barcode Detection,Byte Tracker,LMM For Classification,Florence-2 Model,OpenAI,Time in Zone,Roboflow Custom Metadata,Local File Sink,SORT Tracker,Image Blur,Label Visualization,ByteTrack Tracker,Qwen3.5-VL,Seg Preview,Path Deviation,Object Detection Model,Anthropic Claude,Cosine Similarity,Pixel Color Count,Roboflow Vision Events,Image Preprocessing,Trace Visualization,Email Notification,SIFT Comparison,Stability AI Outpainting,Detections Consensus,PTZ Tracking (ONVIF),Current Time,Dot Visualization,Roboflow Asset Library Attributes,Keypoint Detection Model,Halo Visualization,SAM 3,Classification Label Visualization,Property Definition,Image Contours,Camera Focus,Single-Label Classification Model,OpenRouter,Data Aggregator,S3 Sink,Stability AI Image Generation,VLM As Detector,CLIP Embedding Model,Relative Static Crop,Roboflow Dataset Upload,Google Gemma,OpenAI,Moondream2,Image Slicer,Detections Merge,Stitch OCR Detections,Google Gemini,BoT-SORT Tracker,Email Notification,Byte Tracker,SAM 3,Detections Stitch,Text Display,Semantic Segmentation Model,Size Measurement,Qwen3.5,Depth Estimation,Icon Visualization,OpenAI,Time in Zone,GeoTag Detection,PLC Writer,SIFT Comparison,SIFT,Instance Segmentation Model,Background Color Visualization,Detections List Roll-Up,Mask Visualization - outputs:
Detections Filter,Absolute Static Crop,VLM As Classifier,Qwen-VL,Gaze Detection,Cache Get,Triangle Visualization,Byte Tracker,EasyOCR,Model Comparison Visualization,Detection Event Log,Stitch OCR Detections,OC-SORT Tracker,Track Class Lock,YOLO-World Model,Llama 3.2 Vision,Stitch Images,Keypoint Visualization,Webhook Sink,Heatmap Visualization,Anthropic Claude,Detections Stabilizer,Continue If,Dominant Color,Google Gemini,GLM-OCR,QR Code Generator,Instance Segmentation Model,VLM As Detector,SAM2 Video Tracker,PLC ModbusTCP,Twilio SMS/MMS Notification,Line Counter,Multi-Label Classification Model,Distance Measurement,Ellipse Visualization,Single-Label Classification Model,SmolVLM2,Keypoint Detection Model,Pixelate Visualization,Detections Transformation,Corner Visualization,Overlap Analysis,Buffer,Template Matching,Rate Limiter,Bounding Rectangle,Llama 3.2 Vision,Switch Case,Qwen2.5-VL,JSON Parser,Qwen 3.5 API,Per-Class Confidence Filter,LMM,Inner Workflow,Velocity,Semantic Segmentation Model,Roboflow Visual Search,OpenAI-Compatible LLM,Morphological Transformation,Color Visualization,Roboflow Dataset Upload,Dimension Collapse,Morphological Transformation,Camera Calibration,Motion Detection,Detection Offset,Google Vision OCR,Clip Comparison,Bounding Box Visualization,OpenAI,Halo Visualization,SAM3 Video Tracker,Object Detection Model,Identify Changes,Detections Combine,First Non Empty Or Default,Contrast Equalization,CSV Formatter,Mask Area Measurement,Expression,Instance Segmentation Model,Detections Classes Replacement,Model Monitoring Inference Aggregator,MQTT Writer,Multi-Label Classification Model,Cache Set,PLC Reader,Delta Filter,Image Threshold,Blur Visualization,Line Counter Visualization,Google Gemini,Background Color Visualization,Microsoft SQL Server Sink,Single-Label Classification Model,Background Subtraction,Identify Outliers,Object Detection Model,VLM As Classifier,Instance Segmentation Model,Twilio SMS Notification,SAM 3,OPC UA Writer Sink,Path Deviation,Google Gemma API,Polygon Zone Visualization,QR Code Detection,Keypoint Detection Model,Image Stack,Reference Path Visualization,SAM 3 Interactive,Qwen3-VL,Stability AI Inpainting,Multi-Label Classification Model,Perception Encoder Embedding Model,Crop Visualization,Anthropic Claude,Dynamic Zone,Image Convert Grayscale,Camera Focus,Image Slicer,Overlap Filter,Florence-2 Model,Polygon Visualization,Time in Zone,MoonshotAI Kimi,Dynamic Crop,Qwen 3.6 API,Event Writer,Perspective Correction,Segment Anything 2 Model,Line Counter,Polygon Visualization,Clip Comparison,OCR Model,Roboflow Visual Search Classifier,Circle Visualization,Slack Notification,CogVLM,PLC EthernetIP,Contrast Enhancement,Grid Visualization,Mask Edge Snap,Barcode Detection,Time in Zone,Local File Sink,Qwen3.5-VL,LMM For Classification,Florence-2 Model,OpenAI,Byte Tracker,SORT Tracker,ByteTrack Tracker,Image Blur,Label Visualization,Roboflow Custom Metadata,Seg Preview,Object Detection Model,Path Deviation,Anthropic Claude,Cosine Similarity,Pixel Color Count,Roboflow Vision Events,Image Preprocessing,Trace Visualization,Email Notification,SIFT Comparison,Stability AI Outpainting,Detections Consensus,PTZ Tracking (ONVIF),Keypoint Detection Model,Dot Visualization,Roboflow Asset Library Attributes,Current Time,Halo Visualization,SAM 3,Classification Label Visualization,Property Definition,Mask Visualization,Image Contours,Camera Focus,Single-Label Classification Model,OpenRouter,Moondream2,S3 Sink,Stability AI Image Generation,VLM As Detector,Google Gemma,CLIP Embedding Model,Roboflow Dataset Upload,Data Aggregator,OpenAI,Relative Static Crop,Image Slicer,Detections Merge,Stitch OCR Detections,Google Gemini,BoT-SORT Tracker,Email Notification,Byte Tracker,SAM 3,Detections Stitch,Text Display,Semantic Segmentation Model,Size Measurement,Qwen3.5,Depth Estimation,Icon Visualization,OpenAI,Time in Zone,GeoTag Detection,SIFT Comparison,SIFT,Instance Segmentation Model,PLC Writer,Detections List Roll-Up,MoonshotAI Kimi
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Expression in version v1 has.
Bindings
-
input
data(*): Dictionary of named variables containing data from workflow steps. Variable names are used in conditions and results. Keys are variable names, values are selectors referencing workflow step outputs. Variables can be referenced in conditions and dynamic results. Example: {'predictions': '$steps.model.predictions', 'reference': '$inputs.reference_class_names'} creates variables 'predictions' and 'reference'..
-
output
output(*): Equivalent of any element.
Example JSON definition of step Expression in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/expression@v1",
"data": {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names"
},
"data_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"switch": {
"cases": [
{
"condition": {
"statements": [
{
"comparator": {
"type": "=="
},
"left_operand": {
"operand_name": "class_name",
"type": "DynamicOperand"
},
"right_operand": {
"operand_name": "reference",
"type": "DynamicOperand"
},
"type": "BinaryStatement"
}
],
"type": "StatementGroup"
},
"result": {
"type": "StaticCaseResult",
"value": "PASS"
},
"type": "CaseDefinition"
}
],
"default": {
"type": "StaticCaseResult",
"value": "FAIL"
},
"type": "CasesDefinition"
}
}