What this category of data looks like
Data sources for this category include raw datasets, ETL run logs, annotation platform export results, and sampling quality inspection sheets. Updates occur daily or per batch task. Daily output documents include cleaning rule configuration files, quality inspection sampling records, abnormal data lists, and final quality reports. Data processing includes duplicate data identification, outlier handling, and sampling quality inspection. Common fields include data unique identifier, original content, cleaned content, quality inspection mark, exception type, and processing timestamp. These fields support end-to-end quality review and traceability.
What constraints these characteristics impose on the conversation logs and auditing link
Data sources for this category are scattered. Multiple sources must be linked to complete audits. Conversation logs must support binding unique identifiers of multi-source data to ensure complete traceability of the entire data chain. Data updates daily or per batch. Logs must be archived by batch or time dimension to avoid storage redundancy. Daily output documents must be bound to logs. Audits can quickly match corresponding configurations and execution records. Full processing of abnormal data must be fully retained. This allows tracing root causes when data is unavailable, and avoids indicator distortion or customer rejection risks.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding switch | Enabled | Binds the end-to-end conversation logs of a single batch data cleaning task, matching the business requirements of batch auditing |
responseChatItemId recording scope | Only abnormal entries | Reduces log storage scale, focuses on conversations for abnormal data identification and processing, aligns with core quality inspection and auditing scenarios |
detail log detail switch | Full mode enabled | Retains complete parameters for cleaning rule calls and exception marking, facilitating traceability of details during compliance reviews |
| Log archiving cycle | Daily archiving | Matches the daily or per-batch data update frequency, prevents single log files from becoming too large |
| Exception log retention period | 90 days | Meets conventional audit retention requirements for industry security and compliance |
| Multi-source data association configuration | Bind data unique ID | Associates raw datasets, ETL logs, and sampling quality inspection sheets to enable end-to-end data traceability |
Three Common Misconfigurations
- When invoking multimodal conversations, a 400 invalid image error is returned, and no corresponding exception record exists in the logs. The cause is that the
detaillog detail switch is not enabled, so exception details for image format verification are not captured. - Console.log logs from workflow code modules cannot be viewed in the console. The cause is that the workflow module output switch for conversation logs is not enabled, and the
chatIdassociated task identifier is not bound, so corresponding log entries cannot be matched. - Conversation logs are not archived by batch, making it impossible to quickly locate the end-to-end process of a single batch data cleaning task during audits. The cause is that log splitting rules by
chatIdor batch are not configured, and logs are written to a single unified file.
How to Verify the Configuration is Complete
- Navigate to the FastGPT v4.17.0 conversation log management page, check if the
chatIdbinding switch is enabled. - Trigger a cleaning task that includes abnormal data, verify that the logs contain cleaning rule calls and exception identification details under the
detailfield. - View the log archiving directory, confirm that logs generated on the current day have been split by batch and completed daily archiving.
- Attempt to use the data unique identifier to associate corresponding log entries from raw datasets, ETL logs, and sampling quality inspection sheets, verifying that the multi-source data association configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.