What this type of data looks like
Aviation airport investment research data comes from civil aviation administration public statistical reports, airport annual and quarterly operation reports, flight schedule dispatching systems, and takeoff/landing and passenger throughput monitoring data. Update cycles include real-time (takeoff and landing counts), monthly (flight plan adjustments), and quarterly or annual (revenue and infrastructure data). Documents contain structured tables (such as takeoff/landing counts, passenger throughput, unit operating costs) and unstructured analysis reports. Most fields are associated with specific flight cycles, airport zones, and operating time periods. Units include flight counts, passenger trips, yuan per passenger, and other detailed measurement terms.
Constraints imposed on conversation logs and auditing
The multi-dimensional association of aviation airport investment research data requires that conversation logs be bound to specific identifiers such as flight IDs and operating time periods. This prevents data confusion during statistics and auditing. Real-time and high-frequency updated data requires log collection granularity to be refined to the single-session level. The log retention period must match industry auditing requirements. Long documents and multi-field investment research content cause fluctuations in single-session token consumption. Abnormal consumption must be monitored specifically. Auditing in investment research scenarios requires tracing the full-link conversation of a specific project. Logs must fully record questions, replies, token usage, and associated business identifiers.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Meets civil aviation industry auditing retention compliance requirements, covers standard investment research review cycles |
TOKEN_STAT_DIMENSION | application + associated flight ID + session time | Matches the aviation airport investment research need to track resource consumption by specific projects, avoids vague statistical dimensions |
LOG_EXPORT_ALLOWED_FIELDS | session ID, request time, user input, model output, token usage, associated flight ID | Covers all full-link fields required for auditing, facilitates review of the conversation process for specific investment research projects |
AUDIT_TRIGGER_RULE | triggered when single-session token usage exceeds 50000 characters | Matches the session length characteristics of long-document investment research, provides early warning for abnormal consumption |
MAX_SESSION_LOG_SIZE | 200 MB | Limits storage usage for single-session logs, prevents storage overflow caused by large document parsing |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. Testing against local samples is recommended before finalizing values.
Three common configuration mistakes
- Workflow conversation interface shows empty session logs, and no corresponding log records exist in the background. Cause: The
ENABLE_SESSION_LOGconfiguration item is not enabled, orLOG_RETENTION_DAYSis set to0 days, causing logs to be cleaned immediately. - No call log entries appear on the model provider page, and no data is displayed in the conversation auditing panel. Cause: The
TOKEN_STATISTICS_ENABLEconfiguration item is not enabled, or log collection permission binding rules are not configured correctly. - After initiating a test conversation, the FastGPT workflow shows a failure, and the model backend receives normal response logs. Cause: The log storage volume was not mounted during Docker deployment, preventing session logs from being persisted, or the
LOG_FILE_PATHconfiguration points to a non-existent directory.
How to verify correct configuration
- Access the FastGPT log management panel, select the corresponding investment research application, and check whether token statistics data split by
application + associated flight ID + session timeexists. - Initiate a test conversation containing aviation airport investment research related content, use the log export function, and confirm that the exported file includes fields such as
session ID, request time, user input, model output, token usage, associated flight ID. - Construct a test session with token usage exceeding 50000 characters, and check whether the preset auditing alert is triggered.
- Log in to the Docker deployment server, check the directory specified by
LOG_FILE_PATH, and confirm that log files for the corresponding test session exist.
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.