What This Category’s Data Looks Like
This category’s core data comes from daily production and compliance records at electroplating plants, electronic connector and hardware surface treatment workshops. It includes bath analysis records, process parameter cards, fixture usage records, film thickness and adhesion test reports, hazardous waste management ledgers, and online water quality monitoring records. Data sources include customer surface treatment specifications, bath test results, rectifier operating data, workshop online water quality monitoring equipment, and hazardous waste storage ledgers. Data update frequency is adjusted based on production rhythm: fixture and film thickness data updated per bath run, bath concentration and temperature data updated per shift, and wastewater and hazardous waste records updated at fixed monitoring cycles. Data fields include professional parameters such as bath concentration (unit: g/L), rectifier current (unit: A), film thickness (unit: μm), and wastewater heavy metal content (unit: mg/L).
Constraints Imposed on Tool Calling and Plugin Workflows
The multi-source heterogeneous data, high-frequency update rhythm, and professional parameter characteristics of this category impose multiple constraints on tool calling and plugin configuration. Multi-source production and compliance data requires plugins to support cross-system data pulling. General calling logic relying on a single data source is insufficient. High-frequency bath run and shift updates require tool calling to be real-time. Triggers must match production rhythm to avoid data lag affecting ledger summary. Fixed units and field names for professional parameters require strict parameter mapping rules in plugin configuration. General field templates cannot be used directly. The compliance attributes of hazardous waste and wastewater data require complete traceability records to be retained during tool calling. This ensures generated reports meet production specifications.
Configuration Settings
This configuration was completed using FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | "auto" | Supports automatic routing for multi-source production data, no manual tool specification required |
| Workflow/API function-call configuration | Enable parameter validation | Matches units and field names for electroplating professional parameters, avoids format errors |
| Tool call timeout setting | 300 seconds | Matches the shift update cycle for bath data, prevents summary failure due to delayed data pulling |
| Bulk data pull limit | First 20 entries | Covers the typical number of bath runs per single shift, prevents timeout from pulling too much data |
| Parameter mapping rules | Match fields by bath concentration g/L and film thickness μm | Aligns with professional units and field definitions for this category |
| Error retry count | 3 times | Addresses temporary fluctuations in online water quality monitoring data, ensures data pulling success rate |
Three Common Configuration Mistakes
- Symptom: Plugin call returns
{"result": "Failed to get the VQD for query"}. Cause: Field mapping for electroplating terminology is not configured. The tool cannot recognize specific keywords such as bath concentration and film thickness, so parameter matching cannot be completed. - Symptom: Returns
400 InternalError.Algo.InvalidParametererror. Cause: Passed parameter units do not match. For example, film thickness μm is mistakenly written as mm, and validation is not completed according to the professional units of the category’s data. - Symptom: The chat application cannot properly connect to integrated plugins. Cause:
toolChoiceis not correctly configured for automatic mode. Only manual calling is enabled, so automatic triggering of multi-source data pulling and summary is not possible.
How to Verify Correct Configuration
- Initiate a test request that includes a bath concentration query. Verify that the returned parameters include values with g/L units to confirm field mapping is effective.
- Simulate a single bath run update trigger scenario. Check if the tool completes data pulling and ledger summary within
300 secondsto confirm the timeout setting is reasonable. - Pass a parameter that does not match the required unit, such as writing a film thickness parameter as "10mm". Confirm that a parameter validation error is triggered to verify the
functionCallvalidation function works properly. - View the plugin running logs. Confirm that each call fully records the data source and update time to verify the compliance traceability configuration is effective.
All listed configuration values are common starting points. They should be evaluated against samples specific to the deployment context to ensure suitability for individual use cases.