What the data for this category looks like
Aviation airport research report data comes primarily from official monthly civil aviation administration bulletins, airport group annual reports, international aviation industry trade association industry reports, and brokerage firm specialized research reports. Update cycles cover monthly passenger and takeoff/landing data, quarterly revenue trends, annual full analysis reports, and ad-hoc policy impact interpretations. Document structures include core fields such as takeoff and landing sorties, passenger load factor, passenger throughput, and non-aeronautical revenue percentage. Corresponding units are sorties, percentage, passenger trips, and percentage respectively.
Constraints imposed by these characteristics during model integration and configuration
Multi-source data integration requires adapting to different formats and permission requirements, which calls for flexible data source binding rules. Content with varying update frequencies needs corresponding parameter adjustments for incremental recall, to avoid reloading outdated data. The presence of specialized fields and units requires the model to accurately identify industry-specific terms, so field rules must be clearly defined in the system prompt. The combination of long documents and detailed data increases parsing and inference time, so timeout and context window parameters need adjustment.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Aviation airport research reports include long-form revenue analysis and detailed data. Excessively large chunks cause context fragmentation, while excessively small chunks lose field associations |
recallTopK | Top 10–15 results | The same metrics in research reports may be scattered across multiple documents, so sufficient recall volume is needed to cover related data |
similarityThreshold | 0.72–0.78 | Specialized fields in aviation airport research reports have high semantic similarity. A threshold that is too low will introduce irrelevant documents |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large airport annual reports or consolidated research reports takes significant time, which exceeds the default timeout limit |
systemPrompt | Add the requirement: "Must accurately identify aviation airport-specific fields and their corresponding units, including takeoff and landing sorties, passenger load factor, throughput, non-aeronautical revenue, etc." | Research reports contain a large number of industry-specific terms and units, requiring the model to match them accurately |
maxRetryTimes | 2–3 attempts | Occasional interface timeouts occur when integrating multi-source research reports, and retries can reduce failure rates |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires specific analysis. Testing on internal samples is recommended before finalizing settings.
Three common configuration mistakes
- Phenomenon: The model returns a response after more than 5 minutes, and the interface shows a timeout. Cause: The
PARSE_FILE_TIMEOUT_SECONDSand model inference timeout parameters were not adjusted. Long document parsing and multi-field inference for aviation airport research reports consume more computing resources. - Phenomenon: The workflow conversation shows a failure, but the model backend has response logs. Cause: The workflow node is not bound to the correct model integration key, or the workflow binding rules for the model instance are not configured, preventing the workflow from calling the verified model instance.
- Phenomenon: Irrelevant road passenger transport data appears in recall results, while airport takeoff and landing data is missing. Cause: The
similarityThresholdis set too low, or aviation airport-specific fields are not specified in thesystemPrompt, causing the model to confuse similar transportation industry terms.
How to confirm successful configuration
- Upload a single airport annual report document, check if the parsed text blocks retain specialized fields such as takeoff and landing sorties and non-aeronautical revenue, and confirm that the parsing logic matches the configured requirements.
- Initiate a query that includes specific airport metrics, verify that the number of recall results matches the preset configuration, and confirm that the recall rules are in effect.
- Trigger multiple consecutive queries, record the average response time, adjust timeout and inference parameters, and ensure that the timeout rate meets business expectations.
- Switch between research report documents from different sources, check that files in different formats can be parsed normally, and confirm that multi-source integration configurations are 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.