What the data for this category looks like
Grain reserve-related data mainly comes from inbound and outbound ledgers of grain depots, rotation plan documents, quality inspection reports, and inventory checklists. Data sources cover central and local reserve grain management depot points. The update rhythm is daily synchronization of inventory check data, monthly updates of rotation plans, and quarterly release of quality inspection reports. Documents include structured fields and unstructured content. Structured fields include grain type, storage bin number, inbound date, storage volume (unit: ton), moisture content, imperfect grain rate, and other items. Unstructured content mostly consists of paper-scanned policy documents and warehouse inspection records.
What constraints these characteristics impose on the "workflow orchestration" link
There are many structured fields with fixed units, which requires the workflow to complete field verification and unit matching first to avoid parameter errors in documents. A significant proportion of unstructured scanned content requires adding an OCR parsing node to the workflow to convert scanned documents into editable text. Data update frequency is high, so the workflow needs to support scheduled triggering to obtain the latest inventory and policy data, and accurately bind real-time data to placeholders in document templates. In addition, grain reserve documents must comply with national and local reserve grain management regulations, so the workflow needs to connect to a compliance knowledge base for pre-verification.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Trigger via scheduled task, run once at 0:00 daily and once on the 1st of each month | Matches the update rhythm of daily inventory checks and monthly rotation plans for grain reserve data |
| Variable Mapping | Map the inventory bin number and storage volume fields in variables to document template placeholders | Documents need to be filled with structured reserve data, and variables supports passing custom variables |
| Node Timeout | 600 seconds | Covers the conventional processing duration of OCR parsing and knowledge base queries |
| Knowledge Base Association | Bind the grain reserve policy knowledge base, associate the top 5 results | Ensures documents comply with reserve grain management regulations, the top 5 results can cover main compliance requirements |
| Custom Model Invocation | Specify the code running module by nodeId | Accurately call the target model to generate compliant documents, avoid node confusion |
Three common errors
- Symptom: The knowledge base search node in the workflow returns empty results, and the associated policy content fails to load. Cause: The knowledge base ID was not passed to the node configuration via
variables, and parameter binding was not completed. - Symptom: The code running module returns only full results without streaming output after calling the model. Cause: The streaming output switch was not enabled in the node configuration, or user question context was not passed correctly.
- Symptom: The storage volume unit in the generated document is incorrect, such as writing tons as kilograms. Cause: No matching verification was performed between the structured fields of the data source and the units of the template placeholders, and raw data was directly mapped.
How to confirm the configuration is complete
- Manually trigger the workflow once, check the node run logs, confirm that the custom variables in
variableshave been correctly passed to each associated node. - Check the knowledge base association configuration, confirm that the corresponding grain reserve policy knowledge base has been bound, and the number of associated results matches the set value.
- View the output logs of the code running module, confirm that the streaming output switch is enabled, and the model call parameter configuration is correct.
- Generate a test document, verify that the filled structured data is completely consistent with the fields and units in the data source.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.