What Data for This Category Looks Like
Data for this category originates from slab or billet batches, heating furnaces, mill automation controls, online thickness and width measurement tools, surface inspection systems, and MES systems. Updates are generated in real time per coil or individual steel piece. Daily documents include rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Document fields cover batch identifiers, process parameters, test data, quality judgment results, and other items. Content includes two categories: numerical test values and text-based judgment conclusions.
Constraints Imposed by These Characteristics on Model Integration and Configuration
Data for this category is generated in real time per coil or individual steel piece. A single batch document includes multi-dimensional process parameters and test results. Full process data for the batch must be associated to complete audits. This creates three constraints:
- Single batch data volume is large. Context windows must be configured appropriately to avoid truncating valid information.
- Data updates in real time. Retrieval response during model integration must match production pace.
- Documents contain mixed-format content. Processing logic for structured test data and text judgments must be adapted. Audits have strict requirements for result accuracy, to avoid judgment deviations caused by improper configuration.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Total token count of documents related to a single rolling batch typically falls within this range, preventing truncation of critical process and test data |
maxTokens | 2000–3000 token | Audit conclusions must include defect judgments and batch traceability explanations. This length covers complete output requirements |
quoteMaxToken | 1500 token | Original test data must be retained as audit basis, avoiding context overflow caused by overly long quoted content |
temperature | 0.1–0.3 | Audit processes require consistent judgment results. Lower temperature values reduce random deviations |
AIPROXY_API_ENDPOINT | Exclusive proxy address for enterprise internal industrial networks | Core data for this category is stored in internal network systems. A proxy is required to achieve secure and compliant model integration |
Three Common Misconfigurations
- Phenomenon: After uploading a batch of rolling-related documents, corpus processing time is significantly longer than expected. Cause: No adaptive preprocessing method is selected for the multi-field mixed structure of documents in this category, causing the preprocessing module to repeatedly parse redundant content.
- Phenomenon: When configuring
AIPROXY_API_TOKEN, the interface returns Error 1406 (22001): Data too long for column 'models'. Cause: The token parameter value is too long, exceeding the preset length limit of the system storage field. - Phenomenon: After attempting to integrate the MemoRAG extension function, the model cannot normally retrieve surface inspection data. Cause: No confirmation of adaptation compatibility between FastGPT v4.17.0 and MemoRAG, and incompatible extension code was imported directly.
How to Confirm Proper Configuration
- Upload the complete document set for a single steel coil, check whether the audit results output by the model include core fields such as batch identifiers, process parameters, and test data.
- Simulate real-time upload of newly generated surface quality reports, verify whether retrieval response time matches production pace.
- Input a test question containing long-text judgment conclusions, confirm that the model output length does not exceed the limit set by the
maxTokensconfiguration. - Check the network connectivity of
AIPROXY_API_ENDPOINT, ensure internal network data can be normally synchronized to the model service.
The values provided are common starting points, and should be evaluated against the reader's own samples.