What this category of data looks like
The data for glass intelligent due diligence reports comes from three core sources: building glass factory quality inspection reports, supply chain traceability ledgers, and on-site installation inspection records. Shipment batches trigger data updates. A full update is completed before each shipment batch. Supply chain traceability data is synchronized quarterly. Documents have a two-part structure: a structured header section and an attachment inspection section. The header section includes fields such as batch number, supplier information, and material type. The attachment section includes physical inspection data like thickness, hardness, and light transmittance. Field units follow standard physical measurement standards, including millimeters and Mohs hardness scales.
What constraints these characteristics impose on the model access and configuration workflow
The structured batch attributes of glass data require configuring field standardization mapping rules during model access. This prevents parsing errors caused by mixed physical units. Batch-triggered update rhythms require configuring an incremental synchronization mechanism. Model calls are triggered only when new batch data is generated. This reduces unnecessary computational load. Image data in the attachment section requires accessing a model node that supports multimodal input. Image preprocessing parameters must be configured to adapt to inspection photos of varying resolutions. The large number of structured fields requires adjusting the context window configuration. This ensures core inspection parameters are fully passed to the model.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single glass inspection report has moderate parsing content. 300 seconds supports complete parsing of full batch data and attachment images |
maxContext | 800–1200 characters | Glass data includes multiple sets of physical inspection fields. This range can fully carry core inspection information for a single batch |
Recall Count | Top 6 entries | Glass due diligence requires covering three core dimensions: batch, material, and inspection. The top 6 entries can match all core fields |
Rerank Return Count | Top 4 entries | Prioritize retaining inspection results strongly related to glass batch information. Avoids redundant data interfering with model judgment |
UPLOAD_FILE_MAX_SIZE | 50 MB | Maximum volume of a single glass due diligence report with inspection images is approximately 45 MB. 50 MB reserves reasonable headroom |
MODEL_INPUT_FORMAT | Structured JSON | Most glass data consists of structured batch fields. Structured format improves model parsing accuracy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each situation requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- The interface displays "Browser does not support voice input", and the speech recognition model cannot be called normally. Cause: Browser voice input permissions are not configured, or input format adaptation for speech recognition is not specified during model access.
- After configuring the rerank model, question and answer responses return a rerank result of false. Cause: The rerank model input format does not match the structured fields of glass data. This prevents the model from identifying valid input.
- After importing a locally deployed video and image processing model, glass inspection image parsing cannot be completed. Cause: Model image preprocessing parameters are not configured. Glass image resolution is not adjusted to the range supported by the model.
How to confirm configuration is complete
- Upload a single glass due diligence report. Check if parsed structured fields match the original document. Verify that field mapping rules take effect.
- Initiate a query containing glass batch information. Check if recall results are sorted to prioritize matching core inspection fields. Verify the configuration effect of the rerank model.
- Test voice input for queries related to glass inspection. Confirm that speech recognition results can be properly passed to subsequent model processing steps.
- View model call logs. Confirm that all configuration parameter values match preset settings. No abnormal error messages are present.
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.