What the data for this category looks like
In aviation equipment marketing scenarios within the finance, insurance, or wealth management sectors, data primarily comes from official equipment manuals, aircraft performance parameter sheets, customer requirement communication documents, promotional material scripts, and similar sources. Update timelines align with new product launches, performance upgrades, or marketing campaign adjustments, with no fixed cycle. Document formats include multi-column structured Excel parameter tables, long-text chaptered manuals, and image-text mixed promotional materials. Fields cover aircraft model codes, maximum range, cruise speed, service lifespan, applicable combat or civilian scenarios, and more. Units are mostly professional metrics such as kilometers, kilometers per hour, and years.
What constraints do these characteristics impose on the "model access and configuration" workflow
The multi-column structured nature of aviation equipment marketing data means the default automatic segmentation logic cannot split content by parameter groups, leading to cross-column mixed segmentation results. The diversity of professional fields and units requires precise field mapping during model access to avoid parsing errors. Image-text mixed promotional materials require support for joint parsing of images and text. Data sources with no fixed update cycle require access configurations to support flexible incremental synchronization mechanisms, ensuring timeliness and efficiency of data synchronization.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_MULTICOLUMN_EXCEL | Enabled | Aviation equipment marketing data mostly uses multi-column parameter comparison tables. Enabling this option splits segments by column, avoiding mixed automatic segmentation |
rag_chunk_size | 300–500 characters | Aviation equipment has many independent parameter fields. This segmentation length ensures each segment corresponds to a single parameter group, avoiding cross-segment confusion |
maxContext | 8000–12000 characters | Aviation equipment marketing documents often contain long performance descriptions and multi-field parameters. This parameter ensures complete transmission of context information |
UPLOAD_IMAGE_MAX_SIZE | 10 MB | Promotional materials often include aircraft appearance images and performance charts. This value allows uploading images of compliant sizes to support model parsing |
similarity_threshold | 0.75–0.85 | Filters matching marketing content, ensuring recall results are highly relevant to user queries, and adapting to scenarios with many professional terms |
SYNC_INCREMENTAL | Enabled | Equipment updates have no fixed cycle. Enabling incremental synchronization only syncs updated documents, improving access efficiency |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- After uploading a multi-column parameter Excel file, the model’s parsing results have chaotic segmentation, with each row not treated as an independent segment. Cause: The
PARSE_MULTICOLUMN_EXCELconfiguration is not enabled, and the default automatic segmentation logic splits content by line breaks, resulting in mixed column data. - A
413 Request Entity Too Largeerror is returned when calling the model. Cause: The uploaded image or document size exceeds the configuration values ofUPLOAD_IMAGE_MAX_SIZEorUPLOAD_FILE_MAX_SIZE. - The model cannot be called normally after configuring a proxy service, and a
FATALlevel error appears in logs. Cause: The proxy service port is not open, the configured API key is invalid, or the proxy address and key parameters are not filled correctly.
How to confirm the configuration is complete
- Upload a test multi-column parameter Excel file, review the parsed segmentation results, and confirm that each parameter row is treated as an independent segment.
- Upload an aircraft image that meets the expected size, initiate a query that includes the image content, and confirm that the model can correctly identify the parameters or text in the image.
- After configuring the proxy service, initiate a model call request, and confirm that no
FATALlevel errors occur and normal results are returned. - After adjusting
similarity_threshold, initiate a query, and confirm that the number and relevance of recall results meet 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.