Relevant configurations and practices under FastGPT v4.17.0 are as follows.
What this category of data looks like
Production data for precast concrete components comes from BIM or detailed design files, steel bar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and component QR code traceability data. Daily production documents include detailed component drawings, steel bar cutting lists, mold inspection records, pouring and vibration records, curing records, and demolding and factory inspection reports.
Data is updated per single component, per batch of materials, and per curing kiln unit. Core fields include component ID, batch number, pouring time, curing duration, strength value, demolding date, and factory inspection result. Units include cubic meters, tons, hours, megapascals, and others.
What constraints do these characteristics impose on tool calling and plugin workflows
Multi-source heterogeneous data sources require tool calling to support pulling different types of production documents across systems, and adapt to multiple formats including CAD drawings, Excel ledgers, and PDF test reports.
The fragmented update rhythm per single component, per batch, and per kiln requires tool calling to support node triggering, to avoid repeated processing of full datasets.
The uniqueness and correlation of core fields require tool calling to verify key fields such as embedded position and strength value, to prevent data misalignment or anomalies.
Additionally, the one-to-one correspondence between components and on-site hoisting positions requires ledger summary to retain the association between component ID and installation position.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | functionCall | Requires calling structured tools to extract ledger fields and generate standardized production reports |
| Data source sync trigger mode | Trigger per component/batch/kiln node | Matches the update rhythm of data per single component, per batch, per kiln |
| File parsing types | Cover CAD detailed drawings, steel bar cutting lists, curing records | Corresponds to the main types of daily production documents |
functionCall trigger timing | Automatically trigger after new production data is uploaded | Ensures ledger summary is synced in real time with production nodes |
| Call timeout | 120 seconds | Adapts to the time required for parsing and field extraction of single batch component data |
| Result verification switch | Enabled | Prevents errors in key fields such as embedded position and strength value |
Three common mistakes
- Symptom: The steel bar batch field in the ledger summary result does not update with new batch data, and displays old values. Cause: Variable update is not configured to trigger per production node, and only fixed-cycle synchronization is used, which cannot match the update rhythm per single component or per batch.
- Symptom: Calling the API to generate a production report returns no matching content. Cause: The exclusive production document knowledge base ID for precast concrete components is not specified, and an irrelevant knowledge base is called.
- Symptom: Uploaded CAD detailed drawings cannot be parsed into component and embedded part lists. Cause: Files are not uploaded in multipart/form-data format, or the file type is not correctly labeled.
How to confirm the configuration is correct
- Manually trigger a tool call, check that the generated ledger includes core fields such as component ID, pouring time, and curing duration.
- Upload a new curing record, confirm that the curing duration field for the corresponding component is automatically updated.
- Call the API with the correct knowledge base ID, check that the returned production document matching results meet expectations.
- Submit a workflow API to upload CAD files, confirm that the parsing result generates a complete component and embedded part list.
The values provided are common starting points and should be measured against the reader's own samples.