What This Type of Data Looks Like
Data for packaging and printing industry yield rates primarily comes from production ERP systems, raw material purchase ledgers, and order settlement systems. Data is synced daily at midnight for production batches completed the previous day, with full monthly cumulative calculation results aggregated at the end of each month. Each data entry corresponds to a single production order batch, and includes fields such as production batch number, material code, single-sheet printing area, raw material unit price, startup duration, unit output, order delivery date, total quoted amount, total actual production cost, and actual payment received. Area is measured in square meters, duration in hours, and monetary amounts in yuan.
Constraints Imposed on Conversation Logs and Auditing
The multi-batch order association nature of packaging and printing business requires that conversation logs must be bound to specific production batch numbers and order numbers, otherwise audit traceability cannot be completed. The daily volume of new production batch data is large, so log storage must be partitioned by batch dimension to avoid single-table data overload that reduces query efficiency. Fields such as real-time fluctuating raw material unit prices and startup durations require audit logs to retain snapshots of the data source at the time of the call, to prevent ambiguity during audits caused by subsequent data updates. Monthly aggregated calculation data requires the audit function to support filtering logs by day or month, and to distinguish between real-time call and batch summary operation records.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 100–150 entries | Packaging and printing conversations require association with multi-batch order data, so sufficient context must be retained to trace business association logic |
CHAT_RECALL_MAX_NUM | 80–120 entries | Meet the need for historical conversation recall across multiple order batches, avoid losing critical business context due to entry count limits |
LOG_RETENTION_DAYS | 180 days | Covers monthly/quarterly audit cycles for the printing industry, meets compliance traceability requirements |
WORKFLOW_CODE_RUN_INPUT_ALLOW_HISTORY | Enabled and configure a whitelist | Prevent validation failures caused by redundant business data carried in historical records, adapt to the input validation rules of version v4.8.14 |
AUDIT_LOG_SNAPSHOT_ENABLE | Enabled | Retain snapshots of real-time data such as raw material unit prices and startup durations at the time of the call, resolve data version ambiguity issues during audits |
WORKFLOW_LOG_TIMEOUT_SECONDS | 600 seconds | Adapt to the time requirements of batch accounting for packaging and printing data, avoid interrupting audit log generation due to timeout |
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, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: A validation failure triggers after entering the keyword "historical records" in the input box of the workflow code run node, making execution or saving impossible. Cause: Version v4.8.14 added input keyword validation rules, and unconfigured whitelists block input content containing business context keywords.
- Issue: Empty content is occasionally returned after a conversation call, the process gets stuck for 10 seconds before throwing a timeout error, and no LLM call record appears in the corresponding log. Cause: The audit log snapshot function is not enabled, real-time data pulling times out, causing the LLM call link to interrupt and no valid logs to be generated.
- Issue: Only the most recent 50 chat records display in the conversation interface, making earlier business communication content unavailable for recall. Cause: The
CHAT_RECALL_MAX_NUMparameter was not adjusted, and the default limit of 50 entries was used.
How to Verify Proper Configuration
- Enter the FastGPT conversation log management page, filter conversations related to packaging and printing business, and check that each log is bound to the production batch number and order number fields.
- Trigger a conversation that includes historical order data, verify that the workflow code run node executes normally with no validation failure prompts.
- View the audit log list, confirm that each call record includes a timestamp of the data source snapshot taken at the time of the call.
- After adjusting the
CHAT_RECALL_MAX_NUMparameter, test recalling historical conversations exceeding the default number of entries in the conversation interface, confirm that the number of displayed entries matches the configured expectation.
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.