What Data for This Category Looks Like
Aviation airport financial report data primarily comes from civil aviation regional administration public disclosure documents, listed airport group annual/half-year reports, and industry association statistical briefings. The primary update rhythm follows annual reports. Some quarterly operational data is updated monthly. Document structures mostly include core fields such as takeoff and landing sorties, passenger throughput, cargo and mail throughput, aviation and non-aviation business revenue, and operating costs. Units mostly use person-times, tons, ten thousand yuan, and sorties. Some segmented items include exclusive indicators such as single runway operation duration and per-passenger cost.
What Constraints Do These Characteristics Impose on Model Access and Configuration
Aviation airport financial reports contain a large number of exclusive operational indicators, which differ significantly from the fields in general manufacturing and retail financial reports. Custom field mapping rules must be configured to adapt to these exclusive indicators. Multi-source data is scattered across public documents and industry briefings. Bulk upload of multi-format documents and access to multiple API data sources must be supported. Monthly updated operational data and annual financial report update rhythms differ. Timed data synchronization tasks must be configured to match the update cycles. Long-text financial reports may contain multi-dimensional operational and financial data. The context window must be adjusted to adapt to long-text processing needs, preventing key information from being truncated due to overly long text.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
CUSTOM_FIELD_MAPPING | Map takeoff and landing sorties, passenger throughput, and cargo and mail throughput as exclusive identification fields, and configure unit (person-times, tons, sorties) recognition rules | Aviation airport financial reports include general financial fields and exclusive operational indicators. Clear mapping prevents the model from confusing field meanings |
MAX_CONTEXT | 12000-18000 characters | Single annual financial report text is lengthy, containing multi-dimensional operational and financial data. The long-text context window must be adapted |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | When bulk uploading multiple financial report documents, long-text parsing takes longer. This prevents parsing tasks from being interrupted by timeouts |
RERANK_MODEL_API | Set based on actual testing | Field relevance sorting for aviation airport financial reports must be adapted. Adaptation effects vary across different reranking models |
SYNC_INTERVAL | 720 hours (monthly) or 8760 hours (annual) | Quarterly aviation airport operational data is updated monthly, and annual financial reports are updated annually. This matches the data update rhythm |
MODEL_PROVIDER | Select the corresponding LLM service provider, or configure a custom transit channel | Stable connection to LLM models is required. This prevents interaction exceptions caused by incorrect channel configuration |
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. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- After deploying an LLM model via Ollama, interaction results are unrelated to knowledge base content, and test prompts throw exceptions. Cause: Context window matching parameters for the model are not correctly configured, or the transit channel does not correctly forward model response content.
- Unable to find the API configuration entry for the rerank model, or configuration has no effect. Cause: Advanced configuration options are not enabled, or the rerank model is not bound in knowledge base settings. Adding it only in global configuration will not take effect.
- After accessing the grok-3 model, test prompts throw errors but forced execution works. Field recognition errors occur during formal calls. Cause: The model interface return format does not match the FastGPT preset format. Custom response parsing rules are not configured, leading to empty fields or abnormal formats.
How to Confirm Configuration is Successful
- Upload a single aviation airport annual financial report document, check whether exclusive operational fields are correctly identified in the parsing results, and confirm that the field mapping rules are in effect.
- Run a model test conversation, input a query targeting exclusive operational indicators, check whether the model output accurately associates corresponding fields and data, and confirm that the context window configuration adapts to long text.
- After configuring a timed synchronization task, view the data synchronization log, confirm that the task triggers automatically at the set interval, and there are no timeout or interruption records.
- After configuring the rerank model, bulk upload multiple financial report documents, check whether the sorting of fields related to the query in the recall results meets expectations, and confirm that the rerank model is in effect.
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.