What the Data for This Category Looks Like
Aviation airport investment research data mainly comes from civil aviation regulatory agency public announcements, airport annual/monthly operational reports, flight schedule API interfaces, real-time airspace traffic monitoring data, and other channels. Update rhythms vary significantly: annual reports follow the calendar year, monthly operational data releases monthly, and real-time flight on-time rate and takeoff/landing count data updates hourly.
Document structures include structured statistical tables, semi-structured PDF reports, and JSON-formatted API response data. Core fields include passenger throughput, takeoff/landing counts, flight on-time rate, airspace traffic peak, and more. Each field has a dedicated unit, such as passenger trips, sorties, percentage, and sorties per hour. Some data includes a unique official release number.
Constraints These Characteristics Impose on Citation Source and Traceability
The multi-source nature, differentiated update rhythms, and dedicated field units of aviation airport investment research data create clear constraints for the citation traceability process. Data with different update cycles must be bound to corresponding timestamps to avoid confusing real-time data and historical statistics. Structured table fields and units must be fully retained, otherwise the original statistical basis of investment research cannot be accurately restored. Multi-channel sourced data must be associated with unique identifiers to prevent incorrect cross-referencing of similar data released by different institutions.
Additionally, traceability for real-time data requires precise recording of collection time to ensure the referenced content matches the time range of the current investment research scenario.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
ENABLE_SOURCE_TRACING | Enabled | Aviation airport investment research data has scattered sources, so it is necessary to forcibly record the original data source information for each knowledge fragment |
SOURCE_DISPLAY_MODE | Full data source path + collection timestamp | Real-time flight data and monthly statistical data require clear time identifiers to avoid referencing expired content or confusing content across different cycles |
PARSE_TABLE_KEEP_UNIT | Enabled | Aviation airport data includes specific units such as passenger trips and sorties, so field unit information must be retained in traceability metadata |
UPLOAD_FILE_MAX_SIZE | 1800 MB | Airport annual report PDFs usually contain dozens of pages of data charts and detailed operational descriptions, so large file upload and parsing must be supported |
EMBEDDING_METADATA_FIELDS | Original file name, releasing institution, collection time | Bind core traceability metadata to each embedding vector to facilitate accurate location of original documents in subsequent steps |
RECALL_TOP_K | Top 8 entries | Aviation airport investment research has many data sources, so enough candidate sources must be retained for precise matching and traceability |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test against your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Local FastGPT knowledge base disk usage exceeds expectations, and a large number of metadata files not associated with knowledge fragments exist in the storage directory. Cause: The
SOURCE_TRACING_CLEANUPparameter is not enabled, resulting in parsed traceability metadata not being automatically cleaned up, which occupies additional storage resources. - Phenomenon: Source field information in recall results lacks unit details, making it impossible to accurately understand the statistical caliber of the referenced data. Cause: The
PARSE_TABLE_KEEP_UNITconfiguration is not enabled, so units of structured data are not written to traceability metadata. - Phenomenon: Referenced historical flight data is incorrectly marked as real-time data. Cause: The
SOURCE_TIMESTAMP_SYNCparameter is not configured, causing the collection time to overwrite the release time of the original document, confusing the timeliness of the data.
How to Confirm Proper Configuration
- Upload an airport monthly operational statistics table, view the metadata details of the parsed fragments, and confirm that they include releasing institution, collection time and field unit information.
- Initiate an investment research query, check the source display area of recall results, and confirm that each entry is marked with a complete data source path and corresponding timestamp.
- Check the local FastGPT storage directory, and confirm that only original files, split knowledge fragments and embedding vectors bound with traceability metadata are retained, with no redundant unassociated metadata files.
- Import 2023 airport annual report data, verify that recall results clearly mark the data's release year, which forms a distinction from the time identifiers of real-time data.
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.