What the Data for This Category Looks Like
Aviation airport investment research data comes from four main sources: Civil Aviation Administration public operation reports, internal airport production systems, real-time airspace control data, and industry research reports. Update frequencies vary significantly:
- Flight schedules and real-time passenger flow data update hourly or daily
- Structured reports including quarterly revenue and takeoff/landing sorties update per natural quarter
- In-depth industry reports are released monthly or on an irregular basis
Two document structure types are used:
- Standardized structured tables with fields including flight number, takeoff/landing time, terminal, occupancy rate, and takeoff/landing sorties. Units include sorties, passenger trips, and ten thousand yuan.
- Unstructured operation analysis documents covering content such as airspace traffic analysis and passenger service improvement plans.
Constraints on Deployment and Upgrade Posed by These Characteristics
The multi-dimensional, high-frequency update characteristics of aviation airport investment research data create clear constraints for deployment and upgrade workflows. First, high-frequency real-time data requires configuring an incremental synchronization mechanism during deployment. This avoids excessive computing resource usage from full synchronization. Second, the mixed format of structured and unstructured data requires adapting multi-format parsing plugins during deployment. It also requires configuring field mapping rules to ensure accurate extraction of structured data. Third, the sensitivity of airspace and operation data requires retaining permission verification configurations during upgrades. This prevents sensitive data leaks. At the same time, core retrieval services must not be interrupted during upgrades, so a rolling upgrade strategy must be implemented.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Airport operation reports and airspace analysis documents are usually lengthy, so the parsing process takes significant time, requiring sufficient time to be reserved for parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some quarterly operation datasets include multiple years of historical takeoff and landing data, so the single-file upload limit needs to be relaxed |
maxContext | 800–1200 characters | Key investment research information is concentrated within paragraphs; overly long context will introduce irrelevant redundant content and reduce retrieval accuracy |
Recall Count | Top 8 entries | Aviation airport investment research data has multiple dimensions, so enough relevant fragments need to be recalled to support multi-dimensional analysis |
Similarity Threshold | 0.72–0.78 | Structured field matching has high accuracy, so low-relevance unstructured documents need to be filtered to avoid interfering with investment research conclusions |
RE_RANK_TOP_N | Top 3 entries | Retain the most relevant results after re-ranking to simplify the information screening process for investment research personnel |
The parameter values provided on this page are common recommendations to serve as a starting point 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 the configuration.
Three Common Misconfigurations
- The symptom is that after deploying on a Windows system and restarting, the models and application entries configured for the knowledge base disappear. The cause is failing to mount data and configuration files to a persistent storage directory; the container or local files are reset after the system restarts.
- The symptom is an error
workflow error {"message":"Dangerous behavior"}during workflow debugging, and knowledge base retrieval cannot be triggered. The cause is failing to configure a whitelist for allowing access to local data sources, or calling an unauthorized external interface in the workflow. In version 4.9.13, this error will directly block the debugging process. - The symptom is that the embedding model recall results do not match expectations, and the matching accuracy of structured fields is low. The cause is failing to configure vector matching weight rules for exclusive aviation airport data fields such as flight number and takeoff/landing sorties, resulting in embedding vectors failing to accurately match business keywords.
How to Verify the Configuration Is Correct
- Upload a PDF of an airport operation report, and check whether parsed fields are fully extracted, such as whether takeoff/landing sorties and revenue amount content are accurately displayed.
- Trigger an incremental synchronization task, and check whether new flight schedule data appears in the synchronization log, and that the update time matches the configured synchronization cycle.
- Initiate an investment research-related query such as "Airport takeoff and landing sorties this month", and check whether returned results include accurate structured data and relevant document fragments.
- Restart the deployment environment, and verify that configured models and application entries are not lost, and that the knowledge base can normally initiate retrieval requests and return expected results.
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.