What the data for this category looks like
Data for this category primarily comes from daily business documents of cotton spinning, chemical fiber spinning, woven and knitting enterprises. These documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets and defect records. Data sources cover raw fiber inspection reports, ERP systems, spinning and loom operation data, evenness tester test results, and fabric inspection machine output data. Data updates sync with each batch of raw materials, each shift’s production, and each piece of fabric produced. Fields and units vary across different documents. For example, cotton blending lists require fiber grade and blending ratio to be specified, while process sheets need clear twist and weave parameters.
Constraints imposed on tool calling and plugins by these data characteristics
This category’s data characteristics create multiple constraints for the tool calling and plugins workflow. Multi-source, scattered data sources require plugins that can connect to multiple systems to pull fields from channels including raw fiber inspection, ERP, and spinning equipment. Frequently updated business data requires tool calling trigger logic to match production rhythms, preventing use of expired raw material grades or equipment operation parameters. Wide variation in fields and units across different documents means tool calling must support dynamic parsing and adaptation, and cannot use fixed parsing templates. Process parameters are also strongly linked to defect records, so tool calling must perform linkage verification of spinning twist, weaving weave parameters and defect data to ensure process matching for tender documents and quotations.
Configuration settings
This configuration is based on FastGPT v4.17.0 logic.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Supports large models to independently choose whether to call tools, adapting to dynamic needs for multi-source data pulling |
| Workflow/API function-call configuration | Enabled | Requires large models to strictly call preset tool parameters, avoiding generation of incorrectly formatted calling instructions |
| Tool call timeout | 30–60 seconds | Multi-source data pulling requires waiting for responses from different systems; too short a timeout will cause some data sources to fail to complete pulling |
| Tool call retry count | 2 times | Some spinning equipment data interfaces may have temporary fluctuations; retries reduce the probability of call failures |
| Context window size | 8000–12000 token | Must accommodate multiple process sheets, inspection records and order data, ensuring large models obtain complete business information |
| Number of recalled results | Top 5 | Must cover the main process parameters of a single production batch, avoiding excessive data occupying the context window |
Three common configuration errors
- Phenomenon: The large model’s output tender document contains the phrase "Citation Marker: [1]". Cause: No formatting rules for results after tool calling are configured, and citation markers from the knowledge base are directly included in the final output.
- Phenomenon: The AI does not intercept responses after triggering user-specified keywords, and returns the default reply directly. Cause: Keyword interception rules are not bound to the custom interface calling tool node, and the trigger logic is not configured correctly.
- Phenomenon: The tool call returns empty fields for grey fabric inspection data. Cause: The format of defect record fields output by the fabric inspection machine for this category is not adapted, and the tool parsing template does not match the field names and structure of actual business documents.
How to verify correct configuration
- Initiate a simulated tender demand request, check the tool call logs to confirm that cotton blending lists and process setup sheets for the corresponding batch are pulled, and verify that the data source matching meets business requirements.
- Trigger a preset keyword interception test, check whether the bound custom interface is called and the corresponding reply is returned, and confirm that the interception rules take effect.
- Review the tender document and quotation content output by the large model, confirm that no knowledge base citation markers are included, and check that the formatting rules correctly filter redundant identifiers.
- Simulate a multi-source data pull timeout scenario, check whether the tool retries according to the configured retry count, and confirm that the timeout and retry logic operates normally.
The values provided are common starting points and should be measured against the reader's own samples.