What the Data for This Category Looks Like
This category’s data typically originates from raw fiber inspection reports, ERP production systems, real-time collected data from spinning machines and looms, evenness testers, test results from fabric inspection machines, and order specification documents. Daily generated documents include cotton blending lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets, and defect ledgers. Data updates sync with production batches, team shifts, and the output of each piece of fabric. Fields cover fiber composition ratio, yarn twist, fabric defect position and type, equipment uptime, and more. Some fields have clear units, such as defects per kilometer and uptime in hours.
Constraints Imposed on Tool Calling and Plugins by These Data Characteristics
These data characteristics impose multiple constraints on the tool calling and plugin workflow. First, data sources are scattered, covering offline paper records, real-time industrial device data, structured ERP data, and inspection reports. Plugins must support multi-format data source access and cross-system retrieval of heterogeneous information. Second, data update granularity is fine and update frequency is high, syncing with each batch of raw materials, team shifts, and single piece of fabric output. Tool calling must adapt to high-frequency triggers and batch data processing to avoid single-batch call timeouts. Third, fields have specific units. Plugins must automatically identify and unify unit formats when summarizing ledgers to prevent data misalignment.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-tool calling scenarios, automatically matching data retrieval and format conversion tools required for ledger summary |
| Single tool call timeout | 600 seconds | Adapts to the time requirement for single-batch multi-source data retrieval, avoiding call failure due to device data delay |
| Number of plugin return results | Top 10 entries | Matches the core data volume of a single team or single-batch production ledger, avoiding redundant information interfering with summary |
| Context window size | 8000 token | Accommodates text content from multiple process sheets and inspection records, meeting cross-document associated analysis needs |
| Workflow/API function-call configuration | Enabled | Supports precise calling of specified tools to complete ledger summary for specific fields, such as defect data statistics |
This document is written based on FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: When calling the markdown to file plugin, unauthorised access exception logs appear after exposing internal ports. Cause: No port access whitelist is configured, allowing external parties to directly trigger file generation operations.
- Phenomenon: Unable to view historical calling logs of knowledge base materials, or corresponding entries in the log list are empty. Cause: Tool calling log retention configuration is not enabled, or log retention duration does not meet the data retention requirements of high-frequency calls.
- Phenomenon: When calling the FastGPT interface with curl, a format error prompt containing the
$schemaprefix field is returned. Cause: The request body carries the$schemaprefix field that is not supported by the interface, which does not comply with parameter format specifications.
How to Confirm Configuration Is Correct
- Initiate a single-batch production ledger tool calling request, check that the returned results include core fields such as cotton blending ratio, yarn twist, and defect records, and the field units meet expectations.
- View plugin calling logs, confirm that steps such as multi-source data retrieval and format conversion are completed normally, with no timeout or error records.
- Test the markdown to file plugin, confirm that the generated file format is correct and contains the summarized ledger content, and the port access restriction configuration takes effect.
- Call the
functionCalltool, confirm that the specified statistical operation can be triggered accurately, such as single-batch defect quantity statistics, and the returned results match actual production data.
The values provided are common starting points and should be measured against the reader's own samples.