What this category of data looks like
Data for this category comes from slab/billet batches, heating furnaces, mill automatic control systems, online thickness and width measuring equipment, surface inspection devices, and manufacturing execution systems (MES). Daily documents include rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Data updates per coil or billet. Some online detection data is pushed in real time. Fields include batch number, heating temperature, rolling reduction, thickness deviation, surface defect type and location. Most units are millimeters and degrees Celsius.
What constraints these characteristics impose on model access and configuration
This category of data updates per coil or billet, and includes multi-dimensional process and result data. This requires model access configurations to support parallel processing of batch data and real-time online detection data. Do not use configuration logic that only adapts to static documents.
Multiple types of process and quality documents require preset parsing and mapping rules for corresponding fields during configuration. This prevents generic parsing from losing key business fields such as rolling temperature and thickness deviation.
The high-frequency update feature of real-time data requires context window and other configurations to adapt to the data refresh rhythm. Avoid processing delays caused by overly large windows.
The requirement for coil-level full-link traceability requires enabling cross-document batch association logic during configuration. This ensures the model can integrate full-process data for the same batch.
How to set configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | A single batch of billet data includes multiple process and quality documents. This range accommodates complete full-link data context |
maxTokens | 4000–6000 token | A single round of defect analysis needs to cover complete conclusions such as defect judgment and root cause location. This avoids truncating key content |
quoteMaxToken | 1500–2000 token | Sufficient length of original document fragments must be retained for traceability, matching the field length characteristics of coil-level data |
temperature | 0.2–0.4 | Defect analysis requires rigorous conclusions. Excessive randomization should be avoided |
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint provided by the service provider | Adapt to the privacy requirements of industrial data. Use dedicated endpoints to ensure data transmission security |
AIPROXY_API_TOKEN | Fill in the key provided by the service provider, rotate it regularly | Ensure identity verification security for model calls, prevent unauthorized access |
Three common configuration errors
- Phenomenon: The knowledge base matching results returned by the model only include 1 text block. Multi-dimensional process data cannot be obtained. Cause: The logic supporting multi-text block recall is not correctly configured. Only the default rule for single-match retrieval is used.
- Phenomenon: After configuring the document parsing node, the model cannot read the uploaded rolling specifications or surface quality report files. Cause: The dedicated field structure of this category of documents is not adapted. Generic parsing rules cannot identify content such as batch numbers and thickness deviations, resulting in parsing failure.
- Phenomenon: Model calls continue to fail, returning authentication errors or timeouts. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENare not filled in correctly, or the configured parameter values exceed the range allowed by the service provider.
How to verify a successful configuration
- Initiate a defect analysis test for a single batch of billet. Check whether the context returned by the model includes multiple document fragments such as the heating schedule and rolling specifications for the corresponding batch. Confirm that the configuration adapts to the business data volume.
- Trigger the parsing task for the uploaded rolling specifications or surface quality reports. Check whether the parsed fields include category-specific content such as batch numbers and thickness deviations. Confirm that the parsing rules adapt to the business document structure.
- Initiate multiple model call tests. Check the consistency of the returned results. Confirm that the
temperatureconfiguration value meets the requirements for analytical rigor. - View the FastGPT model call logs. Confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations can normally initiate requests, with no authentication-related errors.
The values provided are common starting points and should be measured against the reader's own samples.