Data Profile for This Category
Data for this category originates from formulation and process libraries, raw material batches, reactor DCS systems, weighing systems, LIMS, and warehouse systems, and is updated per batch or each key step. Daily documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Data fields include feeding volume, reaction temperature, batch identifier, raw material batch number, and other items. Units include kilograms, liters, degrees Celsius, and others. Some formulation and process parameters involve confidentiality requirements.
Constraints Imposed on Workflow Orchestration by This Category’s Characteristics
The multi-variety, batch-switching nature of this category requires workflows to support triggering nodes based on batch identifiers, and associate feeding and reaction data for the corresponding batch. The per-batch update rhythm of data requires workflow nodes to support real-time pulling of the latest raw material batch and reactor DCS data. The wide variety of daily document types and subdivided fields requires workflow configurations to support batch reading of multiple document types, and filter and match based on fields such as feeding volume and reaction temperature. Confidentiality requirements require the variables configuration of workflows to distinguish sensitive parameters from public parameters, to prevent leakage of formulation data. The tender scenario requires aggregating costs across multiple links, requiring workflow nodes to associate raw material unit price data from warehouse systems, and adjust quotation coefficients based on deviation and change orders. This page provides configuration instructions based on the workflow functions of FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Batch execution node trigger rule | Group by batch data bound to nodeId | Matches the per-batch execution business logic of this category, avoiding cross-batch data confusion |
| Global variable append configuration | Write sub-item results to the variables array after each loop | Supports unified aggregation of sub-item results within loop nodes, meeting the need for splicing multi-batch data in tender documents |
| Node error capture configuration | Enable global error capture, set the timeout threshold to 600 seconds | Addresses latency risks from system calls to reactor DCS, LIMS, and other systems, aligning with the stability requirements for data calls in this category |
| Model thinking control switch | Calibrate based on actual task complexity | Reduces call time for simple feeding review tasks, ensures logical rigor for complex formulation verification tasks |
| Multi-file import configuration | Bind document types such as batch production instructions and inspection reports | Matches the batch reading demand for daily documents in this category, supports importing multiple types of documents into nodes simultaneously |
| Conditional branch trigger rule | Match corresponding cleaning confirmation logic based on product type | Adapts to the multi-variety switching characteristics of this category, avoiding missed quotations related to cross-contamination |
Three Common Configuration Mistakes
- Phenomenon: After a batch execution node completes, sub-item results within the loop are not aggregated outside the loop. Cause: The array append logic for
variableswas not configured correctly, only overwriting sub-item results instead of accumulating them. - Phenomenon: After a model node executes, it returns an error message "request timed out", and the process terminates directly without triggering a retry. Cause: Node error capture configuration was not enabled, and a reasonable timeout threshold was not set, causing the system to fail to continue running after a system call failure.
- Phenomenon: After multiple files are imported into the workflow, target documents for the corresponding batch cannot be filtered out. Cause: No field filtering rules for multiple files were configured, and the batch identifier was not bound as a filtering condition, resulting in mixed document data from other batches.
How to Verify Correct Configuration
- Trigger the workflow for a test batch, check whether all sub-item outputs from the loop node are aggregated into the global variable.
- Simulate a model request timeout scenario, verify whether the error capture node triggers the preset retry or termination logic.
- Import multiple types of test documents, check whether the workflow only filters out document data for the corresponding batch.
- Switch test data for different product types, verify whether the conditional branch node correctly matches the corresponding business logic.
The values provided here are common starting points and should be measured against samples relevant to the specific use case.