Local File Sink

Class: LocalFileSinkBlockV1

Source: inference.core.workflows.core_steps.sinks.local_file.v1.LocalFileSinkBlockV1

The Local File Sink block saves workflow data as files on a local file system. It allows users to configure how the data is stored, either:

• aggregating multiple entries into a single file

• or saving each entry as a separate file.

This block is useful for logging, data export, or preparing files for subsequent processing.

File Content, File Type and Output Mode

content is expected to be the output from another block producing string values of specific types denoted by file_type.

output_mode set into append_log will make the block appending single file with consecutive entries passed to content input up to max_entries_per_file. In this mode it is important that

file_type in append_log mode

Contrary to separate_files output mode, append_log mode may introduce subtle changes into the structure of the content to properly append it into existing file, hence setting proper file_type is crucial:

• file_type=json: in append_log mode, the block will create *.jsonl file in JSON Lines format - for that to be possible, each JSON document will be parsed and dumped to ensure that it will fit into single line.

• file_type=csv: in append_log mode, the block will deduct the first line from the content (making it required for CSV content to always be shipped with header row) of consecutive updates into the content of already created file.

Security considerations

The block has an ability to write to the file system. If you find this unintended in your system, you can disable the block setting environmental variable ALLOW_WORKFLOW_BLOCKS_ACCESSING_LOCAL_STORAGE=False in the environment which host Workflows Execution Engine.

If you want to restrict the directory which may be used to write data - set environmental variable WORKFLOW_BLOCKS_WRITE_DIRECTORY to the absolute path of directory which you allow to be used.

Type identifier

Use the following identifier in step "type" field: roboflow_core/local_file_sink@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
file_type	str	Type of the file.	❌
output_mode	str	Decides how to organise the content of the file.	❌
target_directory	str	Target directory.	✅
file_name_prefix	str	File name prefix.	✅
max_entries_per_file	int	Defines how many datapoints can be appended to a single file.	✅

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 Local File Sink in version v1.

• inputs: Model Monitoring Inference Aggregator, Llama 3.2 Vision, Twilio SMS Notification, Stitch OCR Detections, Single-Label Classification Model, Email Notification, LMM, Anthropic Claude, Florence-2 Model, CogVLM, Multi-Label Classification Model, Florence-2 Model, OpenAI, VLM as Classifier, Roboflow Dataset Upload, Local File Sink, Webhook Sink, Clip Comparison, Instance Segmentation Model, OCR Model, LMM For Classification, OpenAI, OpenAI, Google Gemini, Keypoint Detection Model, Object Detection Model, Roboflow Dataset Upload, VLM as Detector, Slack Notification, Roboflow Custom Metadata, Google Vision OCR, CSV Formatter
• outputs: Model Monitoring Inference Aggregator, Bounding Box Visualization, Twilio SMS Notification, Llama 3.2 Vision, Keypoint Detection Model, Stability AI Outpainting, Moondream2, Image Threshold, Model Comparison Visualization, SIFT Comparison, LMM, Gaze Detection, Corner Visualization, Background Color Visualization, Distance Measurement, Time in Zone, CogVLM, Mask Visualization, QR Code Generator, Classification Label Visualization, Trace Visualization, Polygon Visualization, Perspective Correction, Instance Segmentation Model, Florence-2 Model, Path Deviation, Local File Sink, Clip Comparison, Instance Segmentation Model, PTZ Tracking (ONVIF).md), LMM For Classification, Line Counter, Dot Visualization, Google Gemini, Ellipse Visualization, Object Detection Model, Keypoint Detection Model, Pixel Color Count, Dynamic Zone, Time in Zone, Polygon Zone Visualization, Halo Visualization, Icon Visualization, Triangle Visualization, Crop Visualization, Size Measurement, Slack Notification, Pixelate Visualization, Single-Label Classification Model, Path Deviation, CLIP Embedding Model, Single-Label Classification Model, Color Visualization, Email Notification, Blur Visualization, Anthropic Claude, Florence-2 Model, Label Visualization, Line Counter Visualization, Detections Consensus, Cache Get, Multi-Label Classification Model, Reference Path Visualization, Image Blur, Dynamic Crop, OpenAI, Roboflow Dataset Upload, Circle Visualization, Segment Anything 2 Model, Template Matching, Cache Set, Webhook Sink, YOLO-World Model, OpenAI, OpenAI, Image Preprocessing, Stability AI Inpainting, Line Counter, Keypoint Visualization, Multi-Label Classification Model, Detections Classes Replacement, Roboflow Dataset Upload, Object Detection Model, Stability AI Image Generation, Detections Stitch, Perception Encoder Embedding Model, Roboflow Custom Metadata, Google Vision OCR

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Local File Sink in version v1 has.

Bindings

• input

  • content (string): Content of the file to save.
  • target_directory (string): Target directory.
  • file_name_prefix (string): File name prefix.
  • max_entries_per_file (string): Defines how many datapoints can be appended to a single file.

• output

  • error_status (boolean): Boolean flag.
  • message (string): String value.

Example JSON definition of step Local File Sink in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/local_file_sink@v1",
    "content": "$steps.csv_formatter.csv_content",
    "file_type": "csv",
    "output_mode": "append_log",
    "target_directory": "some/location",
    "file_name_prefix": "my_file",
    "max_entries_per_file": 1024
}