What the Data for This Category Looks Like
Data for special steel intelligent due diligence reports comes primarily from steel plant factory quality inspection documents, industry public grade standard libraries, and production and transaction ledgers from supply chain upstream and downstream. Two update cycles apply to this data: Industry grade standard libraries are updated irregularly alongside standard revisions. Production and transaction data is updated in real time or in batches along with business processes. Single reports use a structured format with a fixed set of fields. These fields include grade identification, smelting process parameters, chemical composition indicators, mechanical performance parameters, downstream application scenario classification, and more. All fields have clear physical units: mechanical performance indicators use megapascals, impact performance uses joules, and other units follow standard practices.
What Constraints These Characteristics Impose on Model Access and Configuration
These characteristics impose clear constraints on the model access and configuration link. The requirement for structured fields and fixed units requires configuring field mapping and unit association verification rules to avoid parameter matching errors during parsing. Data with mixed update cycles requires configuring dual-mode data access parameters that support batch loading and real-time triggering, to adapt to synchronization needs of different data sources. The irregular update feature of industry standard libraries requires configuring a switch to quickly switch standard data sources, ensuring the standard basis for due diligence reports remains valid. Additionally, the narrow value range of professional parameters in the special steel field requires configuring parameter range verification logic to prevent invalid data from entering the model inference stage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Special steel due diligence reports contain multiple sets of structured performance parameters. The parsing process requires completing field mapping and unit verification. 600 seconds covers the complete parsing workflow |
chunk_size | 800–1200 characters | The structured field block length of special steel reports is uniform. This segment length ensures each segment contains a complete field group, avoiding splitting that disrupts parameter associations |
maxContext | 8000–16000 characters | A single special steel due diligence report has a large number of structured field sets. Complete parameter association information must be retained to avoid field matching failures caused by context truncation |
Recall Count | Top 8–12 entries | The correspondence between special steel grades and performance parameters is relatively fixed. Excessive recall will introduce irrelevant parameters, while insufficient recall will fail to cover all matching dimensions |
Similarity Threshold | 0.75–0.85 | The semantic similarity requirement for special steel professional parameters is relatively high. This range filters out low-match irrelevant data while retaining accurate parameter associations |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Packaged files for single batch due diligence reports may contain multiple batches of quality inspection data. This size supports upload and parsing of large files |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. 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.
Three Common Misconfigurations
- Phenomenon: GPU utilization remains below 30% during model inference, while a single CPU core reaches 100% utilization. Cause: GPU memory allocation parameters for the model are not configured, causing the model to use only the CPU for some preprocessing or post-processing logic, and failing to fully utilize GPU resources.
- Phenomenon: Field missing errors occur when parsing special steel due diligence reports, with return status code
400 Bad Request. Cause: Field mapping rules are not configured, so the model cannot recognize professional field names in special steel reports, leading to parsing failure. - Phenomenon: When multiple special steel reports are imported in batches, performance parameter matching results for some reports are inaccurate. Cause: The similarity threshold is set too low, causing irrelevant low-match parameters to be mistakenly recalled, which interferes with the matching logic of professional parameters.
How to Verify Proper Configuration
- A single standard special steel due diligence report is uploaded. Parsing logs are checked for mapping records of all preset fields to confirm field mapping rules are effective.
- A model inference task is started. Changes in GPU memory and utilization are viewed through the monitoring panel to confirm full utilization of GPU resources.
- Multiple special steel reports from different batches are imported. The number and matching degree of recall results are checked, and the similarity threshold is adjusted to a range that meets business requirements.
- The maximum-size batch report file is tested for upload. The upload and parsing process is confirmed to have no timeouts or interruptions.
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.