What the data for this category looks like
Aviation airport investment research data originates from civil aviation regulatory authority public operation statistics, annual airport operation reports, flight schedule dispatching platforms, and ground service monitoring systems. Data update cycles cover multiple dimensions: real-time passenger flow, daily flight takeoffs and landings, monthly throughput, and annual operation reports. Available document types include structured CSV flight data, PDF annual operation white papers, and semi-structured airport service specification documents. Core fields include takeoff and landing sorties, passenger throughput, and cargo throughput. Standard units for these fields are sorties, person-times, and tons. Some documents include ICAO and IATA airport code identifiers.
Constraints on multi-turn dialogue and prompt engineering
The multi-dimensional update cadence and diverse document formats of aviation airport investment research data create multiple constraints for multi-turn dialogue and prompt configuration. The coexistence of structured flight data and unstructured operation reports requires multi-turn dialogue to retain field context from historical queries, to avoid mixing up data across types. Differences in update frequencies between real-time passenger flow, monthly throughput, and annual reports require dynamic differentiation of call priorities for static and dynamic data during multi-turn interactions. The dedicated unit system for core fields requires prompts to explicitly mandate that returned results include standard units, to prevent misunderstandings caused by inconsistent units across multi-turn dialogue.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aviation airport investment research data includes multiple long documents and structured tables. A sufficiently large context window preserves field and unit requirements across multi-turn interactions |
recallTopK | Top 6–8 results | Aviation airport data fields are concentrated and highly correlated. Too many recalled results introduce irrelevant data, while too few fail to cover complete investment research dimensions |
similarityThreshold | 0.72–0.78 | This threshold balances precision and recall coverage for the mixed characteristics of structured data and unstructured reports |
segmentLength | 1200–1500 characters | Annual airport operation reports are lengthy. This segment length supports long document splitting while preserving complete business logic units |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large airport annual reports and batch flight CSV data takes significant time. This duration covers the full parsing process |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch uploads of monthly flight data compressed packages and annual report collections, to meet batch data requirements for aviation airport investment research |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific scenarios require individual analysis. It is recommended to test against your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The AI dialogue node triggers an "input character count exceeds model limit" error, or returns an empty result directly. Cause: The
maxContextparameter is not set to limit single-round input length, and no over-length input request interception is implemented in the pre-verification process. - Phenomenon: Dialogue logs do not bind unique user identifiers, making it impossible to trace multi-turn interaction trajectories for individual users. Cause: The automatic binding configuration for user IDs in dialogue context is not enabled, and prompt requirements for labeling interaction subject information are not included.
- Phenomenon: AI dialogue cannot directly analyze JPG-format airport aerial images or flight schedule images. Cause: No image parsing node is configured to connect to image processing tools, and prompt rules supporting multimodal input calls are not specified.
How to confirm correct configuration
- Initiate a multi-turn dialogue that includes multiple field requirements. Verify that returned results include standard units, and that context retains historical query field requirements.
- Upload batch aviation airport data files. Check that parsing progress completes within the configured duration, with no parsing failure prompts.
- Trigger an over-length input request. Verify that a clear interception prompt is returned when input exceeds the limit, without truncation or empty replies.
- View the dialogue log list. Confirm that each log carries a unique user identifier field, and that interaction records can be filtered using the identifier.
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.