What data for this category consists of
Data for this category comes from public statistics released by the Civil Aviation Administration of China, official announcements from listed airports, and specialized research reports from aviation-focused consulting firms. Update cycles fall into three categories:
- Core operational data such as monthly flights and throughput is updated each month.
- Quarterly business research reports are released at the end of each quarter alongside listed airports’ financial reports.
- Annual full industry reports are updated once per year.
Document structures typically include three parts: core operational data tables, route layout analysis, and industry trend assessments. Fields covered include flight takeoff and landing counts, passenger throughput (person-times), cargo and mail throughput (tons), revenue per passenger (yuan per person-time), and some reports include detailed progress updates for airport renovation and expansion projects.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Monthly high-frequency updated operational data requires prioritizing the most recently released datasets in dialogue responses. Limit the context window length to prevent old data from overriding latest information. Fields with clear units require prompts to mandate attaching corresponding units to returned results. This avoids unit confusion for metrics like passenger throughput and cargo and mail throughput. Unstructured details such as temporary flight adjustments and renovation project progress require guiding users to supplement specific airports, time ranges and other qualifying conditions gradually during multi-turn dialogue. This prevents overly generalized answers. The long document structure requires setting reasonable segmented retrieval lengths. It also requires retaining user-specified filtering parameters during multi-turn dialogue to reduce repeated questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Individual aviation industry research reports are typically 5000–8000 characters long. Reserve context space for multi-turn dialogue history messages. |
| Number of retrieved entries | Top 6–8 entries | Core data of aviation industry research reports is scattered across 3–5 sections. Too many retrieved entries introduce irrelevant content; too few fail to cover core metrics. |
| Similarity threshold | 0.72–0.78 | Professional terminology in research reports has high semantic similarity. A threshold that is too low introduces irrelevant documents; a threshold that is too high may miss relevant segmented data. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing long individual research reports takes significant time. This prevents parsing failures due to timeout. |
temperature | 0.1–0.3 | Research report retrieval and question answering require accurate values and terminology. A lower temperature ensures consistency and accuracy of responses. |
| Segment length | 1000–1500 characters | Core data paragraphs in aviation industry research reports are typically around 1000 characters. Segmented retrieval enables precise matching of content corresponding to user questions. |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- When calling the text content extraction module, input any parameter into the code block in the prompt. The full response shows the code block as undefined. The cause is that the code block in the prompt does not properly wrap the extraction rules, so the module cannot identify the scope of content to be extracted.
- After using variable references in a dialogue, the temperature setting button on the right disappears, making it impossible to adjust the model temperature parameter. The cause is that in some versions of the FastGPT frontend interface, activating variable references temporarily hides the model parameter settings panel. Exit the variable editing state to recall the panel.
- After calling the workflow via the API, the running data in the conversation log is empty. The cause is that the workflow is not configured with a log output node, or the API request does not carry the correct session identification parameter. This prevents the system from associating conversation context with running data.
How to confirm correct configuration
- Initiate a query that includes a specific airport and time range. Verify that the returned results include corresponding fields and units, with no unit confusion or numerical deviation.
- Initiate three consecutive progressive queries. For example, first ask for monthly operational data of a specific airport, then ask for year-over-year changes, then ask for segmented category data. Verify that the system retains previously specified filtering parameters, eliminating the need to re-enter qualifying conditions.
- Upload a single complete aviation industry research report. Verify that parsing time does not exceed the configured timeout threshold, and that the matching degree between segmented retrieved content and user questions meets expectations.
- Use the variable reference function in a dialogue. Exit the editing state to confirm that the temperature setting button reappears, and that parameters can be adjusted normally.
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.