What the Data for This Category Looks Like
Data sources include offline store POS transaction systems, headquarters inventory and sales ledgers, market data APIs from partner financial institutions, and regional business district consumer monitoring platforms. There are two update schedules:
- Store-level transaction and profit yield data is aggregated per store within 2 hours after daily closing.
- Headquarters-level daily reports are fully consolidated by 8 AM daily.
Associated market data is refreshed every 15 minutes. Documents use a structured table format, with fields including store ID, store name, affiliated region, daily revenue, daily profit yield, proportion of held products, and business district market fluctuation value. Corresponding units are: numeric code, Chinese characters, administrative region name, CNY, percentage, percentage, and point. The number of data entries varies with store scale, ranging from dozens to thousands per store.
Constraints for Conversation Logs and Auditing
Large numbers of stores and scattered data entries require conversation logs to be split and stored by store ID and time dimension. This prevents individual log files from exceeding storage limits. High refresh frequency of market data means logs must record the timestamp of each request and corresponding market data version number. This allows verification of data timeliness during audits. For calls to partner institutional market data APIs, logs must retain API signatures and return status codes. This meets compliance auditing requirements. Conversation context must retain store filtering conditions. This ensures audits can accurately associate transaction and profit yield data for corresponding stores, avoiding cross-store data confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_SPLIT_THRESHOLD | 500 MB | Specialty chains have a large number of data entries per store. An overly large single log file can cause slow retrieval and loading. Setting the split threshold to 500 MB balances storage and retrieval efficiency |
LOG_RETENTION_DAYS | 180 days | Meets the minimum retention period required for financial industry compliance audits |
CONTEXT_PERSIST_ENABLE | Enabled | Store filtering conditions in conversations must be retained, to ensure audits can accurately associate corresponding store transaction and profit yield data |
API_CALL_LOG_ENABLE | Enabled | Call signatures, return status codes, and request timestamps of partner market data APIs must be recorded, to meet compliance auditing requirements |
THINKING_PROCESS_LOG_LEVEL | DEBUG | Ensure the AI's thinking process is fully written to logs, avoiding issues where runtime output is not displayed in logs |
LOG_QUERY_DEFAULT_FILTER | Sorted by store ID and timestamp | Adapts to the high-frequency need of specialty chain audits to retrieve logs by store and time dimensions |
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 Configuration Mistakes
- Phenomenon: Specific stores or the user ID initiating the conversation cannot be associated in the conversation logs, and the corresponding fields are empty. Cause: The
CONTEXT_PERSIST_ENABLEconfiguration is not enabled, and store filtering conditions are not written to the context logs. - Phenomenon: When calling other deployed applications or plugins in a workflow, the context of this call is not recorded in the conversation logs. Cause: Log synchronization configuration for cross-application calls is not enabled. Only the logs of the main workflow are recorded, and call information from sub-applications is not synchronized.
- Phenomenon: The AI's thinking process is output during runtime, but this content is not displayed in the historical conversation logs. Cause: The
THINKING_PROCESS_LOG_LEVELconfiguration is set toINFOor higher, and the thinking process is not written to persistent logs.
How to Confirm Configuration Is Correct
- Enter the FastGPT log management interface, select the corresponding workflow, filter by store ID and time range, and confirm that corresponding log entries can be retrieved.
- Trigger a conversation that includes store filtering conditions, view the log details, and confirm that the context information includes the store ID and filtering parameters.
- Call the partner market data API, view the API call logs, and confirm that the API signature, return status code, and request timestamp are retained.
- Trigger a conversation that includes the AI's thinking process, view the log details, and confirm that the thinking process content has been fully recorded.
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.