Tool Calling and Plugins for Aviation Airport Financial Report Analysis

Aviation airport financial report data mainly comes from publicly available annual and half-year reports of listed airport groups, monthly industry

What this category's data looks like

Aviation airport financial report data mainly comes from publicly available annual and half-year reports of listed airport groups, monthly industry operation reports released by regional civil aviation administrations, and official statistical platforms of the civil aviation authority. The data update cycle is centered on quarters. Annual reports disclose complete financial and operation details. A single financial report document includes core fields such as takeoff and landing cycles, passenger throughput, cargo and mail throughput, non-aeronautical business revenue, and operating costs. Most units are cycles, passengers, tons, and ten thousand RMB. Some sub-items include operation-related statistical dimensions.

What constraints do these characteristics impose on tool calling and plugins

The structured fields in aviation airport financial reports are numerous and require unified units. Tool calls must accurately match exclusive fields such as takeoff and landing cycles and passenger throughput with their corresponding units to avoid data calculation deviations. Financial reports follow fixed quarterly and annual update cycles, and are released in batches during disclosure windows. Plugins must support batch parsing of multiple financial report files, and configure timeout and concurrency parameters to adapt to concentrated workloads. Some airport financial reports include segmented non-aeronautical business data. Tool calling must identify field associations across business categories, and cannot directly reuse field mapping logic from general consumer financial reports. When analyzing combined real-time operation data and historical financial report data, plugins must support cross-data source format alignment and unit conversion to ensure analysis result accuracy.

How to set configurations

Configuration ItemRecommended ValueRationale
PARSE_FILE_TIMEOUT_SECONDS600 secondsAviation airport financial reports include multi-page operation details and attachments, with significantly longer parsing time than general financial reports
UPLOAD_FILE_MAX_SIZE2000 MBSingle annual financial report may include multiple attached operation data files, with larger file size than general categories
maxContext8000–12000 charactersFinancial report fields are closely linked, requiring sufficient context to ensure field matching accuracy for tool calls
tool_call_max_retries3 retriesThere are many exclusive fields in aviation financial reports. Initial calls are prone to matching deviations, and retries can improve call success rate
rag_recall_top_kTop 8 entriesHigh accuracy requirements for financial report data. Too many irrelevant document recalls will interfere with tool calling logic
PLUGIN_CONCURRENT_LIMIT4 concurrent requestsBatch file parsing occurs during financial report disclosure windows. Limiting concurrency prevents service overload

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, and testing on samples tailored to the specific deployment is recommended before finalizing settings.

Three common errors

  • Phenomenon: In the 4.8.21 Docker deployment version, after uploading a financial report file and starting parsing, the log continuously returns a slow operation xxxxms error. Cause: The PARSE_FILE_TIMEOUT_SECONDS value is not adjusted to adapt to aviation financial reports, and batch parsing concurrency is not limited, leading to MongoDB connection backlog and query response timeout.
  • Phenomenon: The tool calling process cannot link to the uploaded financial report knowledge base, returning no relevant documents retrieved. Cause: The matching threshold for rag_recall_top_k is not configured, or the knowledge base metadata is not labeled with the corresponding category classification, resulting in insufficient recall scope.
  • Phenomenon: After configuring deepseek-r1 as the tool calling model, the generated analysis result has incorrect field units and confused data correspondence. Cause: Exclusive field mapping rules are not configured, and the model cannot recognize the semantics of exclusive fields in aviation financial reports, leading to deviations in calling results.

How to confirm proper configuration

  • A test aviation airport financial report file can be uploaded, and the parsing log checked to confirm that parsing time meets the preset PARSE_FILE_TIMEOUT_SECONDS threshold, with no timeout errors occurring.
  • A tool calling request can be initiated, and the returned knowledge base recall results checked to confirm inclusion of the financial report’s exclusive fields, and normal association logic.
  • A preset financial report analysis workflow can be run to verify that tool call-returned fields match the actual content in the financial report document.
  • 3 to 5 financial report files of the same category can be batch uploaded, and service load during concurrent parsing checked to confirm no abnormal errors occur.

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.