What the data for this category looks like
The data for this category primarily comes from production feeding logs, quality inspection reports, warehouse inbound and outbound documents, and R&D formula archives. Data synchronization occurs in real time after a single production batch is completed, or via daily batch pulls. Documents fall into two categories: structured ledger tables and unstructured attachments. Structured fields include batch number, raw material grade, feeding quantity, reaction temperature, product purity index, storage time, and storage conditions. Their respective units are unique identifier string, grade code, kilogram, degree Celsius, purity index, date, and temperature range.
What constraints these characteristics impose on workflow orchestration
The large number of structured fields with dedicated units requires strict adherence to field mapping rules during workflow orchestration, to avoid ledger summary errors caused by misplaced fields. The high proportion of unstructured attachments requires separate configuration of document parsing nodes, and measures must be taken to prevent repeated loading of historical documents. Data updates have two modes: real-time synchronization and daily batch pulls. The workflow must support corresponding trigger conditions. The batch number acts as the unique identifier, and must be associated with context via variables to ensure independent processing of data from different batches and avoid cross-batch data confusion.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Condition | Trigger on batch completion events, or batch trigger at 2:00 AM daily | Matches the real-time/batch update rhythm of advanced chemical material ledgers |
| Field Mapping Rules | One-to-one mapping for fields such as batch number, feeding quantity, reaction temperature | Matches the dedicated fields and units of structured ledgers |
variables | Bind batch number as the context key | Ensure independent processing of data from different batches and avoid confusion |
| Document Parsing Node | Only parse currently uploaded attachments, exclude historical documents | Prevent repeated loading of historical documents |
nodeId | Set a unique identifier for the document parsing node | Facilitate precise calling of the node during workflow orchestration |
| Number of Failure Retries | 3 times, 60-second interval | Address temporary exceptions during single data pulls or parsing |
Three common mistakes
- Symptom: The workflow returns empty fields or format errors during runtime. Cause: Field mapping rules were not configured strictly per the dedicated fields and units of advanced chemical material ledgers, leading to misplaced fields.
- Symptom: The document parsing node loads all previously uploaded attachments each time it runs. Cause: The rule to only parse currently uploaded attachments was not configured, and the context of the current parsing node was not locked via
nodeId. - Symptom: Preset logic cannot be triggered when calling tools. Cause: The trigger condition does not match the update rhythm of advanced chemical material ledgers, or the context key bound via
variablesis not correctly associated with the batch number.
How to confirm the configuration is correct
This verification process is performed on FastGPT v4.17.0:
- Manually trigger the workflow for a single batch ledger, check that structured fields are fully mapped with no misplacement or omission.
- Upload a single unstructured attachment, confirm that only the current attachment is parsed and no historical attachments are loaded.
- Simulate an exception trigger scenario, check that the workflow retries according to the preset number of times and records failure logs.
- Verify that the batch number bound via
variablestakes effect independently across runs of different batches, with no cross-batch data confusion.
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.