What the data for this category looks like
Data sources for airport and airfield intelligent due diligence include monthly and quarterly operation statistics publicly released by civil aviation authorities, annual operation reports officially published by airport groups, and real-time takeoff and landing flow data from air traffic control departments. Data update frequencies are divided into three categories: real-time takeoff and landing data updates hourly, monthly passenger and cargo throughput data updates monthly, and annual operation indicators update on a calendar year basis. Documents primarily use structured tables, with fields including airport three-letter code, Chinese name, location city, annual takeoff and landing sorties, annual passenger throughput, cargo throughput, number of runways, and total terminal area. Units for these fields are sorties, person-times, tons, units, and square meters respectively.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering by These Characteristics
The multi-source update rhythm of data requires multi-turn dialogue to distinguish query logic between real-time indicators and historical statistical indicators, to avoid confusion over update frequencies. The structured feature of multiple fields requires prompt engineering to explicitly specify queried field names and corresponding units, to prevent large models from generating incorrect units or omitting key indicators. The lengthy content of annual report documents requires multi-turn dialogue to limit the length of context recall, to avoid redundant information interfering with core queries. Cross-airport comparative query requirements require the system to retain airport identifiers from historical queries, to ensure context consistency across multi-turn dialogue.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Airport and airfield due diligence data includes multiple types of fields and historical query context. This value range fully retains key information for multi-turn dialogue, and avoids truncating core indicators |
recall_top_k | Top 8–12 entries | Due diligence reports need to cover multi-dimensional data including takeoff and landing, passenger flow, and cargo throughput. This value range balances information completeness and result conciseness |
json_schema | Output in the format {"airport_code":"","metric":"","value":"","unit":"","period":""} | Meets export and analysis requirements for structured due diligence reports, and adapts to format requirements for downstream tool calls |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Airport and airfield annual operation reports have lengthy document lengths. This value ensures complete parsing of long documents, and avoids timeout errors |
prompt_template | First confirm the queried airport and statistical period, then call the corresponding data source to return results | Standardizes interaction logic for multi-turn dialogue, and prevents large models from confusing indicators across different airports or statistical dimensions |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- After calling the
AI Dialogue Node, results are directly mixed into the conversation interaction flow instead of being output as independent due diligence content. Cause: Thejson_schemaparameter is not configured, and the output format is not explicitly specified. This causes large models to generate natural language conversational responses by default. - The same basic information of an airport is repeatedly asked during multi-turn dialogue. Cause: The
maxContextconfiguration is not enabled, and the context window does not retain airport identifiers from historical queries. The system cannot identify the target object associated with subsequent user queries. - A 504 status code timeout error occurs when uploading an airport annual operation report. Cause: The set
PARSE_FILE_TIMEOUT_SECONDSvalue is too short, and cannot complete text parsing and field extraction for long documents.
How to Verify Proper Configuration
- Initiate two linked queries: first specify the monthly passenger throughput of a specific airport, then ask for the same period's cargo throughput of that airport. Confirm the system correctly associates different indicators for the same airport, with no context loss.
- Call the node to generate due diligence results. Check if the output format matches the preset
json_schemarequirements, and if fields include complete indicator names, values, and corresponding units. - Upload a standard airport annual operation report. Confirm the parsing process does not produce timeout errors, and extracted fields match the document content.
- Debug the
AI Dialogue Node, confirm output only includes structured due diligence data, with no extra conversational interaction content.
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.