This document is written for FastGPT v4.17.0.
What data for this category looks like
Typical entities are hot rolling, cold rolling, section steel, and steel pipe enterprises. Daily documents include rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Data sources include slab or billet batches, heating furnaces, mill automatic control systems, online thickness and width measurement tools, surface inspection systems, and MES systems. Data is updated per coil or per pipe, in real time. Fields include batch identifiers, temperature parameters, rolling speeds, thickness and width deviations, surface defect types, and more. Units include millimeters, degrees Celsius, meters per second, and others.
What constraints these characteristics impose on model integration and configuration
The real-time coil-level data and batch association attributes of this category require model integration to support associating multi-source data by batch dimension, to avoid mixing data across batches. Frequently updated online inspection data requires configuring a low-latency call link, to prevent audit deviations caused by data lag. Mixed input of multiple types of structured documents requires the model to recognize field boundaries of different documents, and parsing rules for structured data must be clearly defined in the configuration. Coil-level traceability requirements require that model output results be bound to corresponding batch identifiers, to avoid mismatches between audit results and physical products.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Covers complete rolling plan, inspection records, and quality certificate data for a single coil, to avoid truncation of critical audit information |
temperature | 0.2–0.4 | Reduces random output probability, ensuring consistency in surface defect determination and dimensional deviation calculation |
| Workflow/API tool selection | auto | Allows the model to independently select tools for quality inspection data extraction and batch association queries, adapting to multi-source data audit scenarios |
AIPROXY_API_ENDPOINT | Fill in the proxy address deployed within the enterprise | Adapts to enterprise intranet data access restrictions, ensuring access to data sources from MES and online inspection systems |
quoteMaxToken | 2000 token | Limits the length of referenced historical audit documents, avoiding context overload that affects response speed |
Three common configuration mistakes
- Phenomenon: Irrelevant zero-value fields frequently appear in tool call results, and content is mixed with the model's original training responses. Cause: The
toolChoiceconfiguration was not set to limit the tool call scope, causing the model to call unassociated general tools and mix in irrelevant training data. - Phenomenon: Single audit tasks time out. Cause: Context configuration was not adjusted to fit the length of coil-level data, causing the context to exceed the model's supported upper limit.
- Phenomenon: Surface defect determination results fluctuate significantly. Cause: The
temperaturevalue is set too high, causing the model to produce inconsistent determinations for the same defect type.
How to verify a correct configuration
- Import complete rolling plan, thickness record, and surface inspection report for a single coil, run an audit task, and check if the returned results include all required batch fields.
- View call logs to confirm that the actual
maxTokensconsumption does not exceed the configured upper limit, and there are no context truncation prompts. - Test the tool call function to confirm that only data sources related to steel rolling quality audit are returned, with no irrelevant content mixed in.
- Run 5 consecutive audit tasks for the same batch, check the consistency of defect determination results, and confirm that the
temperatureconfiguration is reasonable.
The values provided here are common starting points, and should be evaluated against the reader's own samples.