Conversation Logs and Auditing for Textile Manufacturing

What the data for this category looks like

Textile manufacturing investment research data mainly comes from industry association public reports, customs import and export ledgers, raw material spot market quotes, listed companies’ periodic financial reports, and supply chain ERP data. Update frequencies cover daily (raw material spot prices), monthly (import and export, capacity statistics), quarterly (financial reports), and annual (industry white papers). Most document structures include structured tables. For example, raw material price fields use yuan/ton as a unit, and shipment volume fields use 10,000 meters/10,000 tons as a unit. Unstructured industry trend analysis content is also interspersed.

What constraints do these characteristics impose on the conversation logs and auditing link?

The multi-frequency update and multi-structured field characteristics of textile manufacturing investment research data require that conversation logs classify and retain audit entries according to data update cycles. This supports tracing investment research conclusions from corresponding time periods. Structured raw material price and capacity fields must record matching data source field names and units in logs. This prevents unit confusion during audits. Retrieved segments from long-form research reports must mark start and end positions in logs. This ensures accurate location of original content during traceback. The reasoning process for multi-source data integration has fluctuating time costs. Logs must fully record the total time for a single question-and-answer session and the time split for each step. This supports performance verification during audits.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`LOG_RETENTION_DAYS`	`90 days`	Textile manufacturing investment research data is mostly updated quarterly or annually. A 90-day retention period covers a full quarterly audit cycle and meets compliance traceability requirements.
`enable_audit_log`	`Full enablement`	It is necessary to record data source tracing, reasoning time consumption, and retrieved segment positions for question-and-answer sessions. This covers the full link requirements of investment research audits.
`log_detail_level`	`Detailed mode`	There are many structured fields. It is necessary to record matching field names and units to avoid missing information during audits.
`query_timeout`	`600 seconds`	When integrating multi-source data, textile manufacturing investment research requires retrieving multiple data types including raw materials, capacity, and financial reports. A 600-second timeout avoids interruptions from expired time limits.
`delete_log_batch_size`	`50 entries per batch`	The conversation volume of the textile manufacturing investment research knowledge base fluctuates with the data update rhythm. Batch deletion avoids excessive system load.
`hide_agent_thought`	`Hide only in frontend display`	It meets user needs for viewing historical records without displaying thinking processes, while retaining complete logs required for audits.

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. It is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Phenomenon: AI thinking processes are not displayed in conversation history records, but complete thinking steps are retained in audit logs. Cause: Only the frontend display switch was turned off. The hide_agent_thought parameter was not configured correctly. This leads to inconsistent audit and display standards.
Phenomenon: Specific user's question-and-answer trajectories cannot be associated in conversation logs, and user-level audits cannot be completed. Cause: The enable_user_id_log configuration item is not enabled, or a valid user_id parameter is not passed when calling the interface. This causes logs to not carry user identifiers.
Phenomenon: Some entries are not cleared during batch deletion of conversation records, or automated recorded reasoning time is missing. Cause: The delete_log_batch_size parameter is set too large, exceeding the system's concurrent processing limit. The log_reasoning_time configuration item is not enabled, causing time consumption data to not be written to logs.

How to Confirm the Configuration is Effective

Initiate a test question-and-answer session. Check the frontend conversation history to confirm that the AI thinking process is not displayed. View the complete log containing thinking steps through the audit backend. Verify that the hide_agent_thought configuration takes effect.
Pass a custom user_id parameter when calling the conversation interface. View the log details to confirm that the user_id field is included in the log. Verify that the enable_user_id_log configuration takes effect.
Initiate multiple test question-and-answer sessions for multi-source data integration. Check whether the logs record reasoning time consumption, retrieved segment positions, and data source fields. Verify that the log_detail_level configuration takes effect.
Perform a batch deletion operation for conversation records. Check the system task queue and log retention list to confirm that the deletion operation matches the delete_log_batch_size setting. Verify that the batch deletion configuration takes effect.

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.