What This Type of Data Looks Like
Airport financial report data comes primarily from publicly disclosed documents issued by civil aviation industry regulators, official annual reports of airport operating entities, quarterly operation briefings, and monthly production bulletins. Data update cycles fall into three categories: annual, quarterly, and monthly. Annual financial reports are released within four months after the end of the fiscal year. Quarterly financial reports are released within one month after the end of the quarter. Monthly operation data is released within 10 days of the following month. Document structures include four main modules: structured operation data tables, financial revenue and expense details, route operation analysis, and major event explanations. Fields include passenger throughput, cargo and mail throughput, takeoff and landing times, operating revenue, and more. Some documents include additional fields such as route networks and served cities.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
The multi-source, dispersed origin of airport financial report data requires the knowledge base to support multi-path synchronization configuration. This prevents missing publicly available data from different channels. The varying update cycles require setting staged incremental update rules. This ensures the latest monthly and quarterly data is prioritized for retrieval. The complex document structure, which includes both structured tables and large blocks of text, requires the retrieval system to support cross-module semantic associated recall. This avoids separating tables from their corresponding analysis content. The specialized business fields and precise semantic requirements require adjusting the weights of retrieval matches. This prevents invalid recall results caused by semantic deviation.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | top 8-12 results | Airport financial report documents have large data volume per file. Too many recalled results will exceed context limits. Too few will fail to cover core business fields |
similarity_threshold | 0.72-0.85 | Financial report data has high requirements for business accuracy. A too low threshold will introduce irrelevant daily operation data. A too high threshold will miss associated analysis content for segmented business |
segment_length | 800-1200 characters | Financial reports include both structured tables and text analysis content. Too long segments will break context association between tables. Too short segments will split complete business logic descriptions |
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Single annual financial report documents have large file size. The parsing process involves extracting data from multiple modules, requiring a longer timeout buffer |
maxContext | 12000-16000 characters | Must accommodate multiple recalled financial report segments, user queries, and system prompts simultaneously, to support multi-document associated retrieval needs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single annual financial report documents typically include multiple pages of charts and detailed data. This value covers the size limit for most single documents |
Three Common Mistakes
- Symptom: The knowledge base semantic retrieval returns multiple results, but the model replies that no relevant answer was found. Cause: The recalled document fragments fail to cover the core business fields of the user's query. For example, only passenger throughput data is recalled, but the corresponding revenue-related analysis content is not.
- Symptom: A
413 Request Entity Too Largeerror appears after uploading a financial report document once. Cause: The uploaded document size exceeds theUPLOAD_FILE_MAX_SIZElimit, and large documents were not split in advance. - Symptom: The AI reply result does not match the financial report content stored in the knowledge base. Cause: The
similarity_thresholdis set too high, resulting in recalled document fragments that are semantically similar to the query but not directly relevant, failing to hit core financial report data.
How to Confirm the Configuration Is Correct
- Upload a single annual financial report document, check the parsed segmented content, and confirm that the
segment_lengthvalue matches the mixed table and text structure of the document. - Initiate a query that includes specific financial report fields, check the number of returned recalled results, and confirm that the
recall_countvalue meets business retrieval requirements. - View the knowledge base update log, and confirm that financial report data of different cycles completes synchronization according to the preset update rhythm.
- Simulate a high-concurrency query scenario, confirm that the system does not experience context overflow or timeout errors, and verify the rationality of the
maxContextandPARSE_FILE_TIMEOUT_SECONDSvalues.
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing configuration values.
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.