What data for this category looks like
The data for construction machinery intelligent due diligence reports comes primarily from three sources: manufacturer operation and maintenance systems, on-site construction scheduling logs, and equipment rental platform ledgers. Real-time operation data is updated at second-level intervals. Equipment ledgers and valuation data are updated daily. Rental settlement data is synchronized per individual service cycle.
The structure of a single report document includes fields such as unique equipment number, model specification, production date, cumulative operating hours, fault maintenance records, current usage or rental cycle, and market valuation. Most field units are standardized measurement items such as hours, Chinese yuan, and calendar days. Some non-standard models include custom parameter descriptions. These reports are mainly used for equipment rental risk control and collateral valuation review in the financial sector.
What constraints these characteristics impose on model access and configuration
The second-level update requirement for real-time operation data means model call latency must be kept within a reasonable range to avoid failure to sync the latest operating status. Differences in data formats across multiple sources require configuring parsing rules adapted to different data sources, and unifying data standardization processing logic.
Fields include custom parameter descriptions, which requires the model context window to accommodate complete equipment information and avoid truncation of key content. The currency units of valuation fields must be uniformly calibrated to ensure accurate valuation results. Unstructured fault records require configuring appropriate segment parsing rules to ensure maintenance details are fully extracted.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–16000 characters | Construction machinery due diligence reports include multiple fields and custom parameters, requiring full accommodation of all equipment information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single report may include multiple operation and maintenance logs, requiring sufficient time reserved for file parsing |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading complete due diligence document packages with multiple attachments |
Similarity threshold | 0.75–0.85 | Semantic matching accuracy for equipment fault records is relatively high, filtering recall results with low relevance |
recallTopK | Top 8 entries | Covers enough associated maintenance cases to ensure comprehensiveness of fault analysis |
toolCallEnable | Enabled | Requires calling data aggregation tools to complete multi-source data integration and report generation |
The parameter values provided on this page are all common recommended starting points for determining configurations. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing values.
Three Common Misconfigurations
- Phenomenon: A 400 error is returned when calling a vision model to process equipment inspection images, with a prompt indicating incompatible image format. Cause: No format verification rules supported by the vision model have been configured, or the uploaded PNG image has implicit compression differences.
- Phenomenon: Tool calls cannot be triggered after configuring a model deployed via ollama. Cause: The configuration switch for tool calls has not been enabled, or the selected model does not adapt to the tool call protocol.
- Phenomenon: The generated due diligence report does not include visual content such as line charts and bar charts. Cause: The configuration item for visual generation has not been enabled, or the model has not been authorized to call visual generation capabilities.
How to Confirm Configurations Are Correct
- Upload a standard construction machinery operation and maintenance log document, and check whether the parsed data fields fully match the preset equipment information items.
- Call the vision model to upload an equipment inspection image, and verify whether the returned recognition results meet expectations with no format errors.
- Configure a test model, initiate a tool call request, and confirm that the model can correctly trigger the preset data aggregation action.
- Generate a test due diligence report, and check whether it includes the preset visual chart modules with no missing content.
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.