What This Category of Data Looks Like
Aviation airport research report data mainly comes from public documents of civil aviation management departments, operation monthly reports of airport groups, special reports of industry associations, and information disclosed in conjunction with airlines. The update rhythm follows monthly and quarterly core cycles, with temporary supplementary documents generated from events such as airport expansions and route adjustments. Document structures typically include four modules: core operation indicators, financial summaries, policy interpretations, and industry trend analyses. Fields include passenger throughput, flight takeoff and landing sorties, cargo and mail transportation volume, number of connected cities, and other items. Each indicator comes with standardized units. Some research reports also include associated regional traffic flow data.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The multi-structured fields and mixed long-short text features of aviation airport research reports require vector models to support joint encoding of multiple fields. This prevents a single vector from failing to distinguish semantic differences between operation indicators and analytical content. The coexistence of fixed monthly updates and temporary event documents requires the indexing system to support incremental updates and on-demand refreshes. This avoids resource consumption caused by full index rebuilding. Unit attributes of different fields must be standardized during the preprocessing stage. This prevents vector space shifts caused by unit differences. Additionally, the length of individual research reports varies widely, from hundreds of words of operation summaries to thousands of words of trend analyses. Flexible segmentation rules must be adapted to avoid losing key information due to long text truncation.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aviation airport research reports contain both long-text analyses and short indicator entries. This range balances semantic completeness and vector recall accuracy |
recall_top_k | Top 10–15 results | Core indicators and analytical content of research reports are scattered across different paragraphs. A sufficient number of recalled segments is needed to cover all key information |
vector_db_index_type | HNSW | Adapted to the monthly incremental update scenario of research reports. The HNSW index balances recall speed and accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single large research reports may include multi-page operation data parsing. A longer timeout period prevents parsing interruptions |
similarity_threshold | 0.72–0.80 | Indicator content in aviation airport research reports has relatively high semantic similarity. This threshold filters low-correlation redundant segments |
rerank_top_n | Top 3–5 results | The reranking stage focuses on the most relevant research report segments. This avoids excessive input exceeding context window limits |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After importing research reports, the dataset status remains "Indexing in progress" for multiple hours. Cause: Incremental index trigger rules are not configured. Full index rebuilding is not adapted to the large-file scenario of batch-imported research reports, causing index process blocking.
- Symptom: Vector calculation scores are abnormally high, and multiple results have identical scores. Cause: Unit fields in research reports are not standardized during preprocessing. This causes vector space shifts across different indicators, leading to logical errors in similarity calculations.
- Symptom: Vectorization processing time for a single long research report exceeds expectations significantly. Cause: Segmentation length is set too small, leading to an excessive number of generated vector segments. This exceeds the batch write threshold of the vector database, causing queue waiting.
How to Confirm Proper Configuration
- Manually import a single typical research report, check the parsed segmentation results. Confirm that segments cover core indicators and analytical paragraphs, with no obvious truncation.
- Submit a query containing multi-dimensional indicators, verify the relevance of recalled results. Adjust recall and similarity-related configurations to meet business requirement ranges.
- Batch import monthly updated research report packages, check the index update progress. Confirm that the incremental update process triggers normally without blocking errors.
- Check the vector database index logs. Confirm that the index type and configuration items match, with no abnormal parameter conflicts.
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.