This page is written based on the configuration instructions for FastGPT v4.17.0.
What the data for this category looks like
Data sources for this category include cell banks and seed lots, process analysis data, cleanroom monitoring data, MES, LIMS systems, and equipment integrity test results. Data update frequencies fall into three categories: per batch, real-time, and according to fixed monitoring plans.
Daily documentation covers cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation records, sterility test reports, and batch release documentation. Data fields include batch identifiers, key process parameters, cleanroom environmental metrics, test results, and equipment operating status. These fields must align with metrological standards for the corresponding business scenarios.
Constraints imposed by these characteristics on workflow orchestration
The data characteristics of this category impose multiple constraints on workflow orchestration. Multi-source heterogeneous data sources require workflows to be configured with multiple nodes to connect to different systems and document types, to avoid data omissions. Differentiated update frequencies require matching trigger rules. Real-time monitoring data must be bound to event trigger nodes, and batch-level data must be linked to batch generation nodes.
Diverse document structures and fields require configuring variables to adapt to field mappings across different documents, ensuring accurate data extraction. Additionally, quality issue traceability involves high-risk scenarios. Workflows must reserve failure handling nodes to trigger alert and rollback processes when data pulling or analysis encounters exceptions.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable configuration | Bind variables to batch identifiers and core process parameter templates | Uniformly transmit core business identifiers for this category to support field mapping needs across multiple documents |
| Node trigger rules | Bind batch generation events and real-time monitoring events by nodeId | Align with trigger requirements for different data update frequencies to ensure accurate data pulling timing |
| Single data pull limit | Top 20 entries | Avoid overloading single-batch data to impact analysis efficiency, matching the data scale characteristics of this category |
| Timeout threshold | 600 seconds | Adapt to the pulling and analysis duration of complex process data to prevent unexpected workflow interruptions |
| Failure handling configuration | Trigger alert nodes and pause subsequent processes | Match the exception handling requirements of high-risk scenarios in this category to reduce quality risks |
| Document chunking rule | 800–1200 characters, calibrated based on actual testing | Adapt to structural differences across document types to optimize context extraction effectiveness |
Three common misconfigurations
- Phenomenon: When calling LIMS or MES data via an HTTP node,
{{}}format variable parsing fails, returning empty fields. Cause: Did not use/mode for variable reference replacement, causing variable compatibility issues. - Phenomenon: After configuring a global variable with a custom type of knowledge base selection, the judge node cannot read the variable value. Cause: Did not correctly associate the global variable with the workflow's
variablesconfiguration item, so the variable was not passed properly. - Phenomenon: After configuring a 300-word limit in the prompt for the large model node, the output result does not meet the requirement. Cause: Did not bind the context length to the prompt limit, so the large model did not follow the constraint.
How to confirm the configuration is complete
- Trigger a simulated batch generation event, check if each node pulls documents from the corresponding data source according to rules, and confirm that the data source aligns with the business scenario.
- Call the HTTP node to test variable references, confirm that fields are properly populated in the returned results with no parsing errors.
- Configure a judge node to test global variable reading, confirm that variable values can be transmitted normally and participate in conditional judgments.
- Trigger an exception test node, confirm that the failure handling process can normally trigger alerts and pause operations.
The values provided here are common starting points and should be evaluated against the reader's own samples.