What the Data for This Category Looks Like
Baijiu investment research data mainly comes from public industry research reports, listed liquor company periodic reports, official production area statistics, national food quality standards, and e-commerce platform sales monitoring data. Update cadences vary by source: listed company annual and quarterly reports follow fixed schedules, production area capacity data is updated monthly, and industry research reports have no fixed release schedule. Document structures mostly combine structured tables and paragraphs, with clear fields and units including alcohol content (%vol), net content (ml), revenue (ten thousand yuan), production capacity (kiloliters). Some tasting reports contain unstructured sensory description content.
Constraints Imposed on Conversation Logs and Auditing
The structured nature of baijiu investment research data and clear unit requirements mean conversation logs must fully record field matching and unit verification processes, to avoid unit discrepancies during audits. Fixed-schedule released financial report data requires logs to retain data versions and release times, enabling easy tracing of analysis bases across different cycles. Long unstructured text from tasting reports increases log storage requirements, so reasonable content truncation rules must be configured. Differences in update cadences across data sources require the auditing link to mark data sources and update times, ensuring the timeliness of data used for analysis can be traced.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Baijiu investment research requires tracing quarterly and annual financial report analyses. 90 days covers at least two quarterly analysis cycles, meeting regular auditing needs |
MAX_LOG_CHARACTERS_PER_SESSION | 8000–12000 characters | Single long baijiu research report can reach thousands of characters. This range can fully save a complete round of analysis conversation, avoiding truncation of key content |
AUDIT_DATA_VERSION_ENABLE | Enabled | Baijiu data has fixed-cycle updated financial report versions. When enabled, logs can record data versions, making it easy to check the timeliness of analysis bases during audits |
LOG_STORAGE_TYPE | Structured storage | Baijiu data includes clear fields and units. Structured storage allows quick retrieval of log records for specific fields, improving auditing efficiency |
CONSOLE_LOG_OUTPUT_PATH | /var/log/fastgpt/audit/ | Classify audit log directories according to deployment specifications, facilitating centralized retrieval and backup, and meeting compliance auditing storage requirements |
API_LOG_ENABLE | Enabled | Some investment research analyses are completed via API calls. When enabled, API interaction logs can be fully recorded, covering full-scenario auditing needs |
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 specific analysis, and it is recommended to test against your own samples before finalizing.
Three Common Misconfigurations
- Issue: After deployment, the workflow
console.logoutput cannot be viewed in the console, and some requests return a500status code. Cause: The log output path is not configured correctly, and the logs from the workflow module are not directed to the directory readable by the FastGPT console. - Issue: Conversation log storage usage continues to grow, and old data cannot be automatically cleaned up. Cause: The
LOG_RETENTION_DAYSparameter is not set, or its value exceeds the current storage capacity limit. - Issue: Conversations created via API cannot be viewed on the frontend page, and different users cannot retrieve their own historical conversations. Cause: User-level log isolation configuration is not enabled, and logs are not stored divided by user identifiers, leading to cross-user log confusion.
How to Confirm the Configuration Is Correct
- Access the FastGPT backend log management page, retrieve the logs for a specified session, check whether records of data field verification and unit conversion are included, and confirm that the log content matches the actual conversation.
- View the file structure of the log storage directory, confirm that audit logs are stored classified according to the specified path, and quickly distinguish data logs from different sources via file names or tags.
- Call the API to create a test conversation, access the frontend page to verify whether the conversation can be retrieved, and confirm that the user-level log isolation configuration is effective.
- Manually trigger a workflow module that includes
console.log, verify whether the corresponding output file is generated in the specified log directory, and confirm that the log output configuration is correct.
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.