Vector Models and Indexing for Aviation Airport Investment

What the data for this category looks like

Aviation airport investment research data mainly comes from Civil Aviation Administration public announcements, airport annual operation reports, airspace control approval documents, real-time flight data APIs, and industry association research reports. Data update rhythms fall into three categories: Structured operation data, such as passenger throughput and takeoff and landing cycles, is updated daily or monthly. Policy documents are released irregularly. Industry analysis reports are updated quarterly or annually. Document types include structured tables, PDF-format policy files, and Word-format analysis documents. Most fields have clear units, such as "passenger throughput (person-times)", "terminal area (square meters)", and "takeoff and landing time (HH:MM)".

Constraints on Vector Models and Indexing

Structured data with clear field units requires vector encoding to retain field association information. This avoids matching deviations caused by unit confusion. High-frequency incrementally updated operation data requires indexes to support incremental synchronization without full reconstruction. This reduces computing resource consumption. Mixed document types require the parsing link to adapt to both structured tables and unstructured text. This ensures complete vector splitting. Some data has strong timeliness. Indexes must support expiration cleanup rules to avoid recalling outdated information.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`RECALL_TOP_K`	Top 10-15 entries	Aviation airport investment research data covers multiple segmented dimensions. This value balances the comprehensiveness of recalled information and control of context length
`SIMILARITY_THRESHOLD`	0.72-0.85	Structured field vector similarity has high differentiation, while unstructured analysis documents have large similarity fluctuations. This interval covers matching accuracy for both scenarios
`INDEX_INCREMENTAL_ENABLE`	Enabled	This category has daily or monthly incrementally updated data. Incremental indexes only synchronize newly added or modified content, reducing resource usage
`PARSE_TABLE_STRUCTURE`	Enabled	This category of data contains a large number of structured operation tables. Enabling this option retains field association information and improves vector encoding accuracy
`VECTOR_MODEL_BATCH_SIZE`	32-64	The average length of individual investment research documents is relatively long. This batch size balances vector encoding efficiency and memory usage
`INDEX_RETENTION_DAYS`	90 days	Real-time flight schedules and takeoff and landing data only require index retention for the past three months. Expired data can be automatically cleaned up

The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.

Three Common Configuration Mistakes

Phenomenon: Exported index backups only support restoring the entire knowledge base, and cannot be restored by business categories such as "throughput data" or "airspace policy". Reason: The INDEX_TAG_ENABLE configuration item is not enabled, so the index is not bound with business tags, and the backup only retains knowledge base-level granularity.
Phenomenon: After connecting an external vector model API, memory overflow errors (status code 503) occur when deploying on an ARM soft router. Reason: VECTOR_MODEL_BATCH_SIZE is not adjusted to a low batch value adapted to the ARM architecture, and the default parameters exceed the soft router's memory limit.
Phenomenon: After importing Feishu multi-dimensional documents or Excel files, table fields are not correctly parsed into structured vector indexes. Reason: The PARSE_TABLE_STRUCTURE configuration item is not enabled, so structured content is treated as plain text, and field association information is lost.

How to Confirm Configuration Correctness

Upload an airport monthly operation report, check if the parsed text retains field structures such as "passenger throughput" and "takeoff and landing cycles", to confirm that the table parsing configuration is effective.
Submit a query containing segmented business keywords, check if the number of recalled results matches the preset RECALL_TOP_K value, and verify that the recall logic matches business requirements.
Trigger an incremental index synchronization task, check the system operation logs to confirm that only index processing records for newly added data are displayed, to verify that the incremental index configuration is effective.
Adjust SIMILARITY_THRESHOLD and repeat the same query, observe the change in the number of recalled results, to confirm that the similarity threshold configuration meets expectations.

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.