What this category of data looks like
Data sources include the formula and process library, raw material batch ledger, reactor DCS system, weighing system, LIMS laboratory information management system, and warehouse management system. Data is updated per product batch and each critical reaction step. Daily associated documents include batch production instructions, feeding verification slips, reaction process records, cleaning validation records, finished product inspection reports, and deviation and change orders. Single-batch data includes fields such as raw material batch number, reaction temperature curve, feeding weight, and cleaning validation results. Field units include degrees Celsius, kilograms, hours, and other units. Due to multi-product production with batch switching, single-batch data has strong interconnection.
What constraints these characteristics impose on workflow orchestration
The multi-batch switching production mode requires workflows to trigger per single batch to avoid cross-batch data confusion. Multi-source data access requires configuring multiple heterogeneous data source nodes in the workflow, matching the interface formats of different systems. High-frequency batch data updates require workflow nodes to support real-time pulling of the latest data, and verify data timeliness before each execution. Strong compliance requirements for formula confidentiality and cleaning validation require configuring permission check nodes and conditional branches in the workflow, only allowing authorized users to trigger analysis processes for corresponding batches.
How to set configurations
These configurations are based on FastGPT v4.17.0
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable configuration | Bind variables to current batch number and raw material batch number | Match the association logic of single-batch data, ensure each node calls the data source for the corresponding batch |
| Node ID mapping | Bind the two core data source nodes for reactor DCS and LIMS by nodeId | Clarify the call order and permissions of each data source, avoid cross-batch data calls |
| Tool call rules | Only trigger MCP tools associated with the current batch, recall the top 3 matching results | Meet the small-batch, multi-variety analysis needs of batchwise fine chemical synthesis, reduce invalid data |
| Timeout threshold | 600 seconds | Cover the data pulling and analysis duration of links such as reaction process records and cleaning validation |
| Context window | 800–1200 characters | Adapt to the length of key data for a single batch, avoid context overflow |
| Variable assignment rules | Automatically assign variables parameters according to production batches | No manual intervention required, match the high-frequency single-batch production rhythm |
The values listed above are common starting points and should be evaluated against the reader's own samples.
Three common errors
- Phenomenon: Unable to match corresponding batch feeding data when selecting an associated knowledge base. Cause: The batch number parameter in
variablesis not bound to the knowledge base filter conditions. - Phenomenon: Two duplicate thought processes are output when debugging the workflow. Cause: Both automatic trigger and manual trigger rules are configured for the tool call node.
- Phenomenon: Unable to obtain MCP tool call logs via API. Cause: The log storage node corresponding to
nodeIdis not correctly associated in the workflow configuration.
How to confirm the configuration is correct
- Manually trigger the single-batch workflow, check if only feeding and reaction process data for the corresponding batch is pulled.
- View the workflow execution log, confirm that the batch parameter in
variablesis correctly passed between each node. - After submitting a deviation change order, check if the workflow automatically triggers the subsequent cleaning validation process.
- Call the API to trigger the workflow, confirm that the returned results include quality analysis data for the current batch.