What this category’s data looks like
Data sources for this category include customer surface treatment specifications, bath test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Daily generated documents include bath analysis records, process parameter cards, fixture usage records, film thickness and adhesion test reports, hazardous waste ledgers, and wastewater monitoring reports. Data is updated per bath cycle, per shift, or according to fixed monitoring requirements. Fields cover bath composition, temperature and current parameters, fixture specifications, film thickness values, emission indicators, and more. All data must match the customized surface treatment requirements of each customer.
What constraints these characteristics impose on the tool calling and plugins link
Data sources for this category are scattered, and update rhythms are inconsistent. Tool calling must support real-time data pulling across multiple sources, and adapt to bath and water quality monitoring data in different formats. Document fields must match customer customized requirements. Tool calling parameters must dynamically load coating requirements and tolerance ranges from customer specifications. Hazardous waste and emission data directly links to environmental protection cost items in bid quotations. Tool calling must verify data compliance to avoid referencing expired or non-compliant monitoring records. Bath parameters and fixture data directly affect process cost accounting. Tool calling must link historical bath cycle data to calculate material consumption.
Configuration settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | This category requires automatic selection of corresponding tools based on different customer bid document templates, to adapt to dynamic parameter requirements |
| Tool calling timeout | 120–180 seconds | Bath analysis and water quality monitoring data must be pulled across multiple systems, so sufficient response time must be reserved |
| Number of returned entries per single tool call | Top 3–5 entries | Bid quotations must prioritize matching the latest bath cycle and monitoring data. Too many entries will increase time spent on document preparation |
| Workflow/API function-call configuration | enabled | Tools must be called to automatically fill in process parameters and cost accounting fields, to improve the accuracy of bid documents |
| Data verification rules | Follow actual measured calibration | Different customers have different film thickness and emission indicator requirements. Tool returned data must be verified against corresponding customer specifications |
Three common mistakes
- When calling the image format verification tool, a 400 status code is returned, prompting that the image format does not match. The cause is that correct tool input parameters were not configured for the PNG format of the film thickness report for this category, and file encoding or path handling was not done correctly.
- When calling the bath data pulling tool, an empty response is returned. The cause is that the tool calling timeout was set too short to complete cross-system pulling of bath test data, or the model stream output was not correctly connected to the FastGPT plugin interface.
- After calling the quotation calculation tool in the workflow, the subsequent bid document filling node does not execute. The cause is that result transfer parameters after tool calling were not configured, causing downstream nodes to fail to obtain process cost data returned by the tool.
How to confirm configuration is complete
- Manually trigger tool calling, check whether the latest bath analysis records and water quality monitoring data can be pulled, and whether the data fields match the current bidding customer's specification requirements.
- Submit a test bid document template, verify whether the tool automatically fills in process parameters and cost accounting fields, and that there are no format errors.
- Simulate abnormal data input, check whether the tool triggers the verification logic and returns corresponding error prompts.
- View the FastGPT plugin calling log, confirm that the
toolChoiceandfunctionCallparameters match the configured settings, and that no parameters are missing.
The values provided here are common starting points, and should be evaluated against the reader's own samples.