What the data for this category looks like
Data for coal chemical yield and market daily reports comes from public data of domestic bulk commodity spot trading platforms, industry association monitoring data, and futures market trend data. Updates are generated 1 to 2 hours after the close of domestic bulk commodity markets each day, producing a complete daily report for the current day. Documents use structured table format, including fields such as product name, statistical date, same-day spot price (unit: yuan/ton), futures settlement price, port inventory (unit: 10,000 tons), weekly average price change, and more. Each daily report covers market data for dozens of coal chemical derivative products.
What constraints do these characteristics impose on conversation logging and auditing?
The coal chemical category covers dozens of derivative products including methanol, polypropylene, ethylene glycol, and others. Each product has distinct market fields. Logs must split conversation records by product dimension to avoid mixing market fields across different products. Daily report data updates each day. Audits must verify that log timestamps match data generation times to prevent expired cross-day data from entering request chains. Daily reports include many fields, so logs must fully record request parameters for each call, such as specified product lists and statistical cycles. This ensures traceable query of specific request ranges during audits. Data comes from multiple public sources, so logs must also record data source identifiers to facilitate compliance checks during audits.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
chatHistoryMaxCount | Top 8 entries | Single-round conversations usually involve 2 to 3 coal chemical products. 8 historical context entries cover complete request chains and prevent log truncation caused by context overflow. |
LOG_RETENTION_DAYS | 180 days | Meets log retention compliance requirements for financial audit scenarios and covers complete monthly audit cycles. |
PARSE_FIELD_FILTER | Product Name, Statistical Date, Daily Quotation | Coal chemical daily reports have many fields. Only retain core fields required for audits to reduce log storage and query overhead. |
WORKFLOW_NODE_TIMEOUT | 600 seconds | Pulling coal chemical market data across multiple data sources may take a long time. This prevents complete recording of audit logs from being interrupted by timeouts. |
AUDIT_LOG_SAMPLING_RATE | 100% | Audit scenarios require full retention of operation logs to ensure all requests can be traced and verified. |
The parameter values provided on this page are common 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
- In workflows, after calling the Text Content Extraction node, multi-turn conversations interrupt, only single-round request results remain. Cause: History context transfer is not enabled in workflow global configuration. Only the chat history retention count is set within the node, and historical information is not passed to subsequent execution nodes.
- In version v4.8.14, code running node input containing historical record parameters triggers verification failure, cannot enter execution phase. Cause: Newly added field verification rules in this version do not support historical record input parameters for audit scenarios.
- Occasional workflow execution timeout after 10 seconds, returns
llm model response emptyerror. Cause: Network fluctuations occur when pulling coal chemical data across bulk commodity platforms, leading to incomplete recording of request chains in logs, and automatic retry mechanism is not triggered.
How to Verify Proper Configuration
- Initiate a multi-turn conversation involving 2 or more coal chemical products, check if system logs fully record request parameters and returned data fields for each round.
- Enter the audit log management page, filter log entries for a specified date, confirm all log fields comply with the
PARSE_FIELD_FILTERconfiguration rules, with no redundant fields. - Simulate workflow execution flow, trigger the code running node and pass in historical record parameters, confirm the node has no verification failure errors and can complete execution normally.
- View the log storage panel or background configuration, confirm log retention duration matches the
LOG_RETENTION_DAYSconfiguration, with no pre-cleaned historical log records.
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.