What the data for this category looks like
Data sources for civil aviation airport declaration materials and file review include flight operation management systems, local civil aviation supervision reporting systems, and ground support ledger archive libraries. Data update rhythm shifts with support tasks and archiving cycles. Single-flight declaration materials are submitted in real time alongside support tasks. File materials are archived in batches monthly or quarterly. Each material includes flight identification fields (flight number, takeoff and landing times), declaration subject information, attachment file lists, and review status fields. Some materials are PDF-format paper scans. Field units include times, pieces, and MB. All data must strictly match civil aviation industry reporting specification formats.
What constraints these characteristics impose on the "model access and configuration" link
Multi-source data is scattered across different systems. This increases complexity for model access authentication and cross-system synchronization. Document structures include fixed-format industry-specific fields. Models must accurately identify and extract specified content, creating clear requirements for format matching capabilities. Some file materials include scanned documents. Visual recognition capabilities are required to support content parsing. Declaration material submission frequency fluctuates with flight schedules. This requires adaptation to model call responses under high concurrency. Civil aviation sensitive data compliance rules require that the entire model call process be completed within a private network.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the average context length of a single civil aviation declaration material, avoiding truncation of core review information |
maxTokens | 3000–4000 token | Review results do not require overly long output, limiting token consumption to control call costs |
temperature | 0.1–0.3 | Review requires consistent and rigorous output, reducing randomness to ensure accuracy of field extraction |
toolChoice | auto | Supports automatic calling of database query tools to supplement real-time associated data from flight ledgers |
vision | Enabled | Adapts to PDF scanned file materials, supporting parsing and extraction of visual content |
AIPROXY_API_ENDPOINT | Model API address deployed privately at the airport | Ensures civil aviation sensitive data does not leave the private network, complying with industry data security requirements |
Three common errors
- Phenomenon: After embedding the FastGPT chat window via iframe on the front end, the parent page cannot retrieve the model's returned output content. Cause: The FastGPT webhook callback address is not configured, and the front-end event interface is not monitored, so returned data is not correctly passed to the parent page.
- Phenomenon: When querying civil aviation ledger data via Mongo, the token consumed by the model does not match the token actually returned by the API. Cause: The
maxContextparameter is not restricted, so redundant historical context is counted toward token consumption, causing actual call tokens to exceed expected values. - Phenomenon: Connection errors occur when calling the MCP MySQL service using a locally privately deployed Qwen3-32B model. Cause:
AIPROXY_API_ENDPOINTis not set to the local model's access address, andmaxTokensis not adjusted to match the local model's context limit, resulting in model call failure.
How to confirm the configuration is complete
- Submit a standard civil aviation takeoff and landing permit declaration material, check whether the model's returned review result accurately extracts core fields such as flight number, takeoff and landing times, and declaration unit.
- View the FastGPT API call log, confirm that the actual consumed token count matches the configured values of
maxTokensandmaxContext, with no abnormal excessive consumption. - Test the iframe-embedded chat window, retrieve the model's returned output content via the preset callback interface, and confirm that data delivery works normally.
- Call the locally privately deployed model, submit a file material containing a scanned document, check whether the model can normally parse the visual content and return a review result without error messages.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.