What the data for this category looks like
Data for aviation airport intelligent due diligence reports comes from four main sources: publicly available operational data from civil aviation regulatory authorities, annual airport reports, publicly available air traffic flow monitoring information, and airport infrastructure filing documents. The data update cadence falls into three categories. Daily takeoff and landing and throughput data updates daily. Annual operating indicators update per fiscal year. Infrastructure-related documents release in stages as projects progress. Most documents combine structured tables and long-form text. Core fields include takeoff and landing sorties, passenger throughput, cargo and mail throughput, runway length, and terminal building construction area. Corresponding units are sorties, person-times, tons, meters, and square meters respectively.
Constraints for vector models and indexing workflows
The large number of structured fields with fixed units creates a risk. Fields with identical names can have different semantic meanings due to unit differences. Complete field normalization before indexing. High-frequency daily operational data updates require indexes to support incremental writes. This avoids excessive resource usage from full index rebuilding. Core paragraphs of long-form annual reports require appropriate segment lengths. This prevents truncation of critical operating data. Differences in multi-source data formats require unified field mapping rules. This prevents missing fields or incorrect associations during indexing.
Configuration Setup
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-large | Adapts to the mixed scenario of structured fields and long-form text for aviation airport data, supports encoding of general semantics and structured data |
chunk_size | 800–1200 characters | Adapts to the paragraph length of annual report long-form text and operational data tables, avoids truncating core field descriptions |
chunk_overlap | 100–150 characters | Preserves contextual connections between segments, prevents loss of logical links between fields after splitting |
index_type | HNSW | Supports fast recall of high-frequency incremental data, adapts to the query requirements of daily updated operational data |
top_k | Top 8–12 results | Balances recall accuracy and query latency, meets the multi-dimensional data retrieval needs of due diligence reports |
vector_store_max_batch_size | 50 items/batch | Adapts to the daily data volume for incremental updates, avoids overload from single batch writes |
The parameter values provided on this page are common recommended starting points for configuration setup. 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
- Issue: When using the
Doubao-embedding-largeindexing model, enter a custom request URL and API key then click test. The system returns a401 Unauthorizederror. Cause: The full path of the custom request URL is not configured correctly, or the API key is not granted permission to call the embedding model. - Issue: Import airport operational table data into the knowledge base. During retrieval, the system does not return vector results for structured cells. Cause: Multi-vector support configuration for table data is not enabled, and vector embeddings are not generated separately for individual field content within tables.
- Issue: Import daily updated takeoff and landing sorties data. Retrieval results do not include the latest updated content. Cause: Incremental synchronization rules for the index are not configured. The system only performs full index rebuilding, and does not execute incremental writes. This causes new data to fail to load correctly.
How to Verify Successful Configuration
- Access the vector model configuration page of the knowledge base. Confirm that the target embedding model is selected, and that the custom request URL and API key are configured correctly.
- Upload a single piece of airport operational table data. Check that the parsed segments retain core fields and units, with no abnormal truncation.
- Run an incremental data import task. Wait for completion then retrieve the corresponding data. Confirm that the results include the updated content.
- View the index service monitoring dashboard. Confirm that the success rate of recall requests and the stability of batch writes meet the configuration 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.