Dialogue Logs and Auditing for Textile Manufacturing Yield

What this category's data looks like

Textile manufacturing yield and market data is sourced primarily from enterprise ERP production modules and raw material commodity market APIs. Data is aggregated and updated per natural day, with full prior day data consolidated each midnight. Individual data documents use production batch and workshop as core dimensions, and include fields such as production batch number, workshop code, raw material category, unit usage, unit cost, sales unit price, daily output, and daily sales revenue. Units include kg, meter, square meter, yuan and other category-appropriate measurement standards.

What constraints these characteristics impose on dialogue logs and auditing

The daily natural-day aggregation update rule requires dialogue logs to record both request initiation time and data effective cycle. This prevents confusion between new and old data during cross-day calls. The multi-dimensional, multi-field document structure requires logs to fully record all filter parameters included in requests, such as production batch, workshop code, and raw material category. This ensures accurate matching of corresponding data ranges during audits. The parallel multi-unit field design requires logs to attach the measurement standard for each numeric field. This prevents audit deviations caused by cross-unit calculations. The batch daily report generation scenario requires logs to split and record the execution status and return results of each sub-request. This helps locate the source of single data anomalies.

How to Configure

Configuration Item	Recommended Value	Rationale
`logRetentionDays`	`90 days`	Textile manufacturing audit compliance requirements typically cover quarterly cycles. 90 days meets regular audit traceability needs
`auditLogIncludeParams`	`production batch number, workshop code, raw material category`	Matches the core filter dimensions of this category's data, ensuring accurate association with corresponding data ranges during audits
`maxContext`	`previous 4 dialogue turns + 1000 character data summary`	Textile manufacturing yield rate analysis requires associating historical context of raw material market and production data. 4 dialogue turns cover necessary logical connections, and 1000 character summary avoids context overflow
`apiRequestTimeout`	`600 seconds`	Batch generation of yield rate daily reports for multiple workshops and categories takes longer to pull and calculate data. 600 seconds covers execution cycles for most scenarios
`errorLogLevel`	`DEBUG`	This category has complex data fields. DEBUG level records complete parameter parsing and data matching processes, facilitating audit anomaly troubleshooting

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. Testing should be conducted against own samples before finalizing settings.

Three Common Misconfigurations

Symptom: Abnormal off-hours call records appear in audit logs, with abnormal balance consumption. Cause: The apiRequestWhitelist configuration is not set. Unauthorized external requests can directly call the yield rate broadcast interface, leading to invalid calls during off-hours.
Symptom: Full workshop yield rate data cannot be retrieved after calling the interface, with insufficient return result count. Cause: The batchLogSplitThreshold parameter is not adjusted. The batch request split threshold is set too low, causing data from some workshops to be excluded from return results.
Symptom: The dialogue interface prompts a token error, but the associated log platform shows the token as fastgpt. Cause: A dedicated token is not configured in FastGPT's apiAuthToken setting. Using the default token causes conflicts with tokens from other systems, leading to authentication failure.

How to Verify Configuration Completion

A dialogue request including a specified production batch and workshop code is initiated. The audit log is verified to fully record request initiation time, filter parameters, and the measurement unit of return result fields.
A request during off-hours is simulated. The apiRequestWhitelist configuration is verified to take effect, with unauthorized requests blocked and recorded in error logs.
The log retention directory is reviewed to confirm old logs older than logRetentionDays have been automatically cleaned up or archived.
A batch request is initiated. The number of return results is verified to match the filter dimensions, confirming the batchLogSplitThreshold setting aligns with current business scale.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.