What data for this category looks like
Core data for this category comes from design history files, device and material batch ledgers, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Daily documents include production process specifications, batch production records, cleanroom environment records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials.
Data updates occur once per batch or per product, with clean environment data updated per established monitoring plans. Document fields include structured content such as unique device identifiers, material batch numbers, sterilization parameters, cleanroom temperature and humidity, and software version numbers. Some records require association with cross-system data.
Constraints on workflow orchestration for this category
This category’s data updates per batch or via real-time monitoring. This requires workflows to support triggering by production batch and dynamic pulling of structured data for the corresponding batch.
Multiple heterogeneous data sources require workflows to be configured with cross-system data pull nodes, to avoid errors caused by manual entry.
Complex document structures and specialized fields such as unique device identifiers and sterilization parameters require workflows to support field-level validation and variable references, to ensure audit content complies with process requirements.
High-frequency batch-level updates require workflow execution efficiency to match single-batch processing durations, to avoid delays that disrupt production schedules.
Configuration guidelines
This page’s configuration rules are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Knowledge base selection | Bind a dedicated knowledge base created per production batch | This category’s data updates per product batch, requiring precise matching of production and inspection records for the corresponding batch |
| Variable reference rules | Use variables to reference fields in batch records and sterilization records | Structured batch-related data must be dynamically called to adapt to parameter requirements that vary per batch |
| Node trigger condition | Bind clean environment monitoring nodes by nodeId | Real-time clean environment data must be associated to ensure assembly processes comply with clean production requirements |
| Single call timeout | 300–600 seconds | Pulling data across multiple systems such as MES and sterilization equipment takes significant time, requiring sufficient execution duration |
| Multi-node context retention | Retain the first 2 rounds of audit interaction context | Inspection audits must reference prior process records to ensure consistent audit logic |
| Streaming request configuration | Enable streaming responses | Adapt to the interaction behavior requirements of FastGPT Agent nodes, avoiding node exceptions caused by incompatible requests |
Three common configuration errors
- Phenomenon: After multiple <AI Chat> nodes are called in a workflow, all nodes’ responses are displayed during workflow conversations. Cause: No node output filtering rules are configured, causing return content from all nodes to be merged into the conversation context.
- Phenomenon: Workflow nodes using the Qwen3 model experience unresponsiveness or timeout errors. Cause: Streaming requests are not enabled, and Agent nodes default to relying on streaming requests for interaction logic, resulting in incompatible requests.
- Phenomenon: Workflows cannot support multi-turn question answering and follow-up questions. Cause: No context association rules are configured, and key data from prior interactions is not retained via
variables, causing nodes to fail to retrieve historical audit information.
How to verify correct configuration
- Trigger a simulated production batch workflow, check whether associated data such as batch production records and sterilization records for the corresponding batch are automatically pulled.
- View node run logs, confirm whether fields referenced via
variablescorrectly match preset batch parameters. - Test the multi-node interaction flow, verify that only the response from the currently executing node is displayed, and that context association complies with audit logic.
- Adjust streaming request configuration, verify that Agent node interaction behavior meets expectations, with no incompatible request errors.
The values provided here are common starting points and should be evaluated against the reader’s own samples.