What Data for This Category Looks Like
Apparel and home textiles investment research data primarily comes from brand quarterly sales ledgers, fabric supplier ex-factory price databases, industry association category prosperity reports, offline store sales data, and cross-border e-commerce platform transaction records. Update cadences vary: terminal sales and fabric pricing data updates weekly. Listed company financial reports and industry research reports update quarterly. Single documents typically include fields such as SKU code, production batch, fabric composition, gram weight, retail price, and inventory quantity. Units include meters, grams per square meter, pieces, ten thousand yuan, and others. Some research reports also include analysis dimensions such as channel share and inventory turnover.
Constraints on Conversation Logs and Auditing
Dispersed data sources and inconsistent update cycles require conversation logs to fully record the data source name and version timestamp associated with each call. Audits must verify that data used in conversations is valid within its corresponding time range. The multi-field, multi-unit document structure requires logs to capture the full workflow of SKU matching, field extraction, and unit conversion, to prevent data matching errors during audits. Cross-source log correlation needs require audit processes to support tracing call links across multiple data sources, to ensure the data source for every investment research conclusion is traceable.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
log_retention_days | 180 days | Apparel and home textiles investment research data audit cycles cover quarterly financial report periods; 180 days meets full review requirements |
audit_trigger_count | Every 5 conversations | Investment research conversations associate multiple documents per call; triggering batch audits every 5 conversations balances performance and compliance |
max_log_entry_size | 2000 characters | Apparel and home textiles data includes long-text fields such as SKU codes and cost breakdowns; 2000 characters can fully store a single log entry |
field_match_log_enable | Enabled | Apparel and home textiles data requires multi-dimensional field matching; enabling this option fully records the field matching workflow |
data_version_alert_threshold | Deviation > 7 days | Core fabric pricing data updates weekly; logs with a deviation of more than 7 days must trigger audit alerts |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: The conversation log displays the token field as
fastgpt, and the chat interface reports a connection failure. Cause: The data source access token was not configured correctly. The log records the default placeholder token, which cannot pass third-party interface verification. - Phenomenon: Calling the full conversation log API only returns a small number of records, and complete logs for the specified time range cannot be obtained. Cause: The
audit_log_batch_sizeconfiguration value was not adjusted. The default rule only pulls a limited number of logs. - Phenomenon: A large number of abnormal call logs appear outside of working hours, causing abnormal resource consumption. Cause: No time period verification rules were configured, and unauthorized scheduled call requests were not blocked.
How to Confirm Proper Configuration
- Access the log management interface, check the
log_retention_daysconfiguration value, and confirm it matches the investment research audit cycle requirements. - Initiate a multi-turn conversation including fabric parameters and inventory data, view the log details, and confirm that the full workflow of SKU matching and unit conversion has been recorded.
- Call the full conversation log API, and verify that the returned results cover all conversation records within the specified time range.
- View the alert trigger rules, and confirm that the audit logic for data version deviation verification has been bound.
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.