This configuration guide is based on FastGPT v4.17.0.
What data for this category looks like
Data for this category comes from registered process documents, raw material batch ledgers, DCS control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Daily documents include batch production records, material balance sheets, reaction and purification records, cleaning validation records, impurity profile reports, and deviation and change control documents. Data is updated per production batch and each process step. Core fields include reaction temperature (℃), charge quantity (kg), impurity content (mg/g), solvent residue (ppm), and others. Formatting varies by production stage, with no fixed unified structure.
What constraints do these characteristics impose on workflow orchestration
Data sources for this category are scattered and have inconsistent formats. Workflow orchestration must support access nodes from multiple heterogeneous systems, and complete field mapping and format conversion for different data sources. Data updated per batch and per step requires workflow triggers to bind specific production batch or process step identifiers. This avoids issues with fixed-cycle triggers. Core business scenarios involving multi-step reactions and intermediate transfer require workflows to string together multiple process nodes and embed field validation logic. This ensures each step’s data meets process specification requirements. Since errors can lead to batch scrap or compliance risks, workflows must be configured with exception branches. These trigger specialized validation processes for deviation and change-related data.
Configuration Parameters
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger condition configuration | Bind production batch numbers and process step identifiers | Match the data feature of being updated per batch and per step, ensure workflows only trigger during corresponding production stages |
variables mapping rules | Map fields from chromatography data systems, LIMS and other data sources to fields required by process specifications | Adapt to non-uniform formatted documents from multiple heterogeneous data sources, complete data standardization conversion |
nodeId binding rules | Bind corresponding nodes to process links such as reaction, purification, and transfer | String together multi-step process flows, match core business requirements for multi-step reactions and transfers |
| Node timeout setting | 600–1200 seconds | Cover the standard duration of single-step reactions in active pharmaceutical ingredient production, avoid interruptions caused by overly long single-step execution |
| Exception branch trigger logic | Trigger validation processes when deviation or change fields are abnormal | Address business risks with severe error consequences, intercept compliance and batch scrap risks in advance |
| Context call limit | Top 8 historical records | Limit the number of documents called per session, avoid context overload that impacts execution efficiency |
Three Common Misconfigurations
- Symptom: Workflow nodes display
Premature closeerrors. Cause: Failure to adapt to format differences in multi-source heterogeneous data from pharmaceutical ingredient production. Data streams interrupt when nodes read non-standardized impurity profile reports or reaction records. - Symptom: Single workflow execution duration exceeds 1200 seconds. Cause: Failure to set reasonable node timeout and batch splitting rules. Failure to segment large volumes of batch data when stringing together multi-step process nodes leads to execution timeouts.
- Symptom: Workflows cannot call external search tools to return valid results. Cause: Failure to configure search keyword mapping rules in
variables, and failure to bind keyword parameters corresponding to process links. This causes search commands to mismatch business scenarios.
How to Verify Successful Configuration
- Trigger the workflow and bind a test production batch and process step, confirm the workflow only starts during the corresponding stage.
- View field values after
variablesmapping, confirm multi-source heterogeneous data has completed standardization conversion. - Simulate input of abnormal data, confirm the exception branch triggers the corresponding validation process.
- View workflow execution logs, confirm single-node execution duration falls within a reasonable range.
The values provided here are common starting points, and should be evaluated against the reader's own samples.