What data looks like for this category
Data for this category comes from customer color swatches, formula libraries, dye and chemical batch ledgers, dye vat control systems, colorimeters, setting machines, and environmental monitoring equipment. Data is updated once per vat, per batch, or per shift. Daily documents include dyeing formulas, chemical mixing records, dye vat temperature-time curves, proofing confirmation sheets, color difference reports, setting process sheets, and sewage discharge ledgers. Data fields include dye dosage (g/kg), dye vat heating rate (℃/min), color difference ΔE value, chemical batch number, COD emission concentration (mg/L), and other metrics, generated in real time with each processing batch.
What constraints this category’s characteristics impose on tool calling and plugins
The batch wet processing nature of this category requires tool calls to be bound to the unique identifier of a single vat or single batch, to prevent cross-batch data confusion. High-frequency real-time updates for dye vat curves and color difference data require plugin call response delays to stay within 60-120 seconds, to match the rhythm of single-vat processing. Mixed calls of multi-source heterogeneous data (directly connected device data, manually entered ledgers) require tools to support switching between structured data parsing and unstructured document reading. Fixed document formats (such as CSV for dye vat curves, PDF for color difference reports) require plugins to preset parsing rules for matching formats, lowering subsequent adaptation costs. In addition, compliant calls for environmental monitoring data require tools to retain complete call logs for traceability.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API function-call configuration | Enabled by force | In FastGPT v4.17.0, this configuration allows fixed model calls to preset plugins, avoiding format errors caused by autonomous tool selection |
| Workflow/API tool selection | Bound to the dyeing and finishing data parsing plugin ID | Ensures tool calls point to dedicated parsing plugins, matching the fixed document and data format of this category |
| Plugin timeout threshold | 60-120 seconds | Adapts to the conventional processing time of single-vat dyeing and finishing, preventing audit process interruptions due to wait timeouts |
| Number of return items per single call | Top 3 | Matches the core data scale of single-batch processing, reducing interference from redundant information |
| Log retention rule | Retain complete call records | Meets the recording requirements for quality audits and environmental traceability |
Three Common Mistakes
- Symptom: A "tool connection failed" log is returned when calling a tool in the workflow, and the database query returns no results. Cause: The deployment environment did not map the ports required for the dyeing and finishing database, causing the plugin to fail to access the local data source.
- Symptom: A 400 status code is returned when calling a custom parsing plugin. Cause: The plugin request parameters did not carry required fields such as the dye vat batch number, which does not comply with the preset interface verification rules.
- Symptom: Parsed result fields returned after tool calling are missing. Cause: The parsing template for the corresponding document format was not configured, causing unstructured data to not be extracted correctly.
How to Confirm the Configuration Is Correct
- Manually trigger a tool call, enter a known dye vat batch number, and verify whether the returned data includes the core business information of the corresponding batch.
- View the tool call log to confirm that the timeout threshold was not triggered and the number of returned items matches the preset range.
- Check the log retention function to confirm that complete records of each call have been saved.
- Switch between different document formats (such as dye vat curve CSV, color difference report PDF) and verify whether the parsing rules are triggered normally.
The values provided are common starting points and should be measured against the reader's own samples.