Batch Processing Node Overview
The Batch Processing node extends workflow capabilities through automation, enabling FastGPT to handle batch tasks and complex data processing pipelines. It significantly improves efficiency when processing large-scale data or scenarios requiring multiple iterations.
Primary Use Case Categories
The Batch Processing node is optimized for three core categories of workflow tasks:
Batch Data Processing
This category supports scalable automated processing of uniform data items:
- Batch text translation
- Batch document summarization
- Batch content generation
Data Pipeline Processing
For workflows that require individual handling of structured result sets:
- Analyzing search results one by one
- Processing Dataset retrieval results individually
- Processing array data from HTTP responses item by item
Recursive or Iterative Tasks
For workflows needing repeated content processing or chained data transformations:
- Long text segmented processing
- Multi-round content refinement
- Chained data processing
Standard Implementation Workflow
When deploying the Batch Processing node for its intended use cases, follow this structured workflow:
- Add the Batch Processing node to your FastGPT workflow canvas
- Route a structured array input to the node, which may include dataset retrieval results, HTTP response arrays, or pre-defined text batches
- Connect a dedicated processing sub-workflow to the node’s input port, configured to handle each individual item from the input array
- Aggregate the output generated by the sub-workflow for each item into a final output array from the Batch Processing node
Source: FastGPT official source
Applicability and version scope
Use this page for the documented Workflow nodes scenario. Confirm the FastGPT, dependency, API, and deployment versions in the official source before applying a change.
Safety guardrails
Use [REDACTED_CREDENTIAL] for credentials and private data. Confirm the documented environment and version before review.
Rollback guidance
Restore the prior technical-content authority snapshot. Restore saved configuration and data snapshots, then repeat the smallest verification scenario.