What Data for This Category Looks Like
Data for aviation airport intelligent due diligence reports comes primarily from Civil Aviation Regional Administration public operation statistics, airport annual/monthly operation annual reports, air traffic control department airspace use data, and airport financial disclosure documents. Data update rhythms fall into three categories: monthly takeoff/landing and passenger flow statistics, quarterly financial briefings, and annual full due diligence documents. Document structures include standardized operation fields (takeoff/landing times, passenger throughput, cargo and mail throughput, with units of times, person-times, and tons respectively), financial fields (revenue, cost, with unit of ten thousand yuan), airspace qualification and authority, surrounding transportation supporting information, and some documents include multi-page attachments and tabular data.
Constraints Imposed on Model Access and Configuration
The multi-dimensional fields and mixed types of aviation airport due diligence data require the model access link to adapt to datasets that mix numerical and text data. Data sources with different update frequencies require configured incremental synchronization rules with differences. The structure of long documents and multiple attachments increases parsing and indexing time. Standardized unit fields need to avoid model mismatching of units. Dispersed multi-source data entry points require unified access mapping configuration to ensure consistent data formats for airport data from different sources.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aviation airport due diligence reports contain multi-page operation data and financial details. Excessively long context will trigger the model's context overflow limit |
embedding_batch_size | 16–32 | Multi-source mixed airport data includes numerical and text fields. An overly large batch size will cause excessive memory usage and reduce parsing efficiency |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Annual due diligence reports include multiple attachments and tables. The conventional parsing duration exceeds the default threshold |
Recall count | Top 8–12 results | Airport due diligence data covers multiple dimensions including takeoff/landing, finance, airspace. Sufficient indicators are needed to support due diligence conclusions |
Similarity threshold | 0.72–0.78 | The data contains a large number of standardized numerical fields. A threshold that is too low will introduce irrelevant matching items and reduce retrieval accuracy |
Rerank result count | Top 3–5 results | Core due diligence indicators are concentrated in a small number of key entries. Too many returned results will distract the model |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors
- When accessing domestic large models, an invalid token is displayed, and the interface returns a 401 status code. The cause is that the custom key of OneAPI is not correctly bound to the FastGPT model configuration item, and the correct access address is not configured.
- After adding a new embedding model, indexing succeeds but search tests report errors with field parsing failure prompts. The cause is that type conversion rules are not configured for the standardized numerical fields of airport data, causing the model to fail to correctly identify numerical indicators.
- A locally deployed source code service cannot connect to a FastGPT service deployed via Docker, and logs show connection timeout. The cause is that correct port mapping and network access permissions are not configured in the Docker container, causing local data sources to fail to be pulled by FastGPT.
How to Confirm Configuration Is Complete
- Enter the model management page, run the connectivity test, and check that the returned result shows a successful connection.
- Upload an airport monthly due diligence report, and check that the parsed field list includes preset fields such as takeoff/landing times, passenger throughput, and cargo and mail throughput, with no missing fields.
- Initiate a search test, enter "2024 airport takeoff and landing data", and check that the number of returned results matches the preset recall and reranking configuration.
- Configure an incremental indexing task, and verify that updated airport operation data can be automatically synchronized to the knowledge base.
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.