What the data for this category looks like
Aviation airport financial report data is sourced from monthly operation briefings publicly disclosed by civil aviation regional administrations, as well as official annual and quarterly financial reports from airport groups. Data updates follow a set schedule: monthly operation data, quarterly flash reports, and annual official reports. Document structures include core fields such as takeoff and landing movements, passenger throughput, cargo and mail throughput, main business revenue, and non-aeronautical business proportion. Most units are person-times, tons, and ten thousand yuan. Some reports also include regional passenger flow distribution and route category details.
What constraints these characteristics impose on vector models and indexing
Monthly operation data has a small volume but updates frequently. Longer annual financial report documents impose requirements on vector segmentation adaptability. The combination of multiple professional fields and fixed units requires vector models to retain complete field semantics, and avoid damaging business-related information during segmentation. Text translated from structured reports may contain table nesting and format misalignment issues. Structured content alignment must be completed before indexing, otherwise vector recall accuracy will decrease. Frequently updated operation data requires indexes to support incremental update logic, to avoid resource consumption caused by full re-indexing.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Aviation airport financial reports include long annual reports and short monthly briefings. This range balances segmentation completeness and recall accuracy, and avoids truncating core business fields |
embedding_batch_size | 4–8 items/batch | Reduces single-batch embedding API call volume, avoids triggering call rate limits, and adapts to call restrictions of public embedding services |
INDEX_WORKER_THREADS | 2–4 threads | Controls concurrent indexing task count, balances indexing efficiency and system resource usage, and avoids errors caused by excessive concurrency |
recall_count | Top 8–12 items | Aviation financial report business logic links multiple fields. A sufficient number of recalled segments is required to cover the complete business chain, and avoid missing key information |
similarity_threshold | 0.72–0.80 | Financial report professional terms have high semantic similarity. This threshold filters low-relevance recall results, and improves retrieval accuracy |
incremental_indexing_toggle | Enabled | Monthly operation data updates frequently. Incremental indexing reduces computational overhead of full re-indexing, and adapts to high-frequency update scenarios |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require on-site analysis. It is recommended to test on self-provided samples before finalizing settings.
Three common misconfigurations
- Symptom: Text chunks are lost when
segment_lengthis set to 3000 characters. Cause: Long segments exceed the maximum context window of the embedding model, leading to automatic truncation or discarding of partial content during vectorization. - Symptom: Call limit errors are triggered during vectorization, with logs showing rate limit exceeded. Cause: The
embedding_batch_sizeandINDEX_WORKER_THREADSparameters are not adjusted, and concurrent call volume exceeds service restrictions. - Symptom: Knowledge base retrieval takes too long, or question and answer pair extraction tasks remain in the indexing state for extended periods. Cause: The incremental indexing toggle is not enabled. Full indexing processing large volumes of historical financial report data leads to resource exhaustion, or the
recall_countis set too high, increasing processing time in subsequent reranking steps.
How to confirm the configuration is correct
- Upload an annual financial report document, check the segmentation preview results, and confirm core business fields are not truncated.
- Submit a small batch of test data, observe logs during the embedding process, and confirm no rate limit errors occur.
- Initiate a retrieval test, verify that the number of recall results and the filtering logic of the similarity threshold meet expectations.
- Check indexing task status, confirm that frequently updated monthly data triggers incremental indexing, and no full re-indexing is performed.
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.