What this category of data looks like
This category of data mainly comes from slab or billet batch ledgers, heating furnace operating parameters, mill automatic control logs, online thickness and width measuring equipment data, surface inspection reports, and manufacturing execution systems (MES). Daily documents include rolling plans, heating schedules, rolling specifications, single-coil shape and thickness records, surface quality reports, and quality certificates. Data is updated in real time per coil or per individual steel coil. Fields include batch number, heating temperature, reduction rate, thickness deviation, surface defect level, and others. Units include degrees Celsius, millimeters, percentage, and others.
What constraints these characteristics impose on the model access and configuration workflow
Since data is updated in real time per coil or per individual steel coil and has strong batch correlation, model access must support high-frequency, small-batch data reading to avoid delays caused by bulk pulling. Mixed structured and semi-structured documents of multiple types require configuration to adapt to text parsing for different formats, and batch-related fields must be retained for full-process traceability. Single-coil data volume is limited, but total data volume grows with production capacity, so context window size must be controlled to prevent overflow. Process specifications and standards rely on precise parameter matching. Field misalignment or unit confusion will directly cause Q&A results to fail.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of full-process documents for a single batch of rolling, avoiding context overflow |
maxTokens | 1200–1500 token | Ensures replies fully cover key steps of process specifications while controlling generation latency |
quoteMaxToken | 180–220 token | Limits the length of a single quoted document segment, matching typical paragraphs of single-coil surface quality reports or thickness records |
| Workflow/API tool selection | auto | Allows the model to independently judge whether to call retrieval tools, adapting to Q&A scenarios that require matching real-time batch data |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring consistency between process parameters and standard requirements |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise's internal model deployment address | Official access address for docking large models, adapting to dedicated model requirements for steel rolling scenarios |
Three common configuration mistakes
- The corresponding model does not appear in the platform's model list. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN, with no correct pointing to the deployed large model service address. - Q&A results do not reference the correct single-coil process document. The cause is that the
maxContextconfiguration value is too small, failing to load all batch-related associated documents. - Parameter deviations appear in the generated process steps. The cause is that the
temperaturevalue is too high, leading the model to generate parameter content that does not meet standard requirements.
How to confirm successful configuration
- Log in to the FastGPT v4.17.0 platform, and check whether configured models show an online status on the model management page.
- Enter the batch number of a single-coil billet, initiate a process specification query, and verify whether returned results include core parameters such as heating temperature and reduction rate for the corresponding batch.
- Initiate a Q&A related to surface defect determination, and verify whether returned results reference correct surface quality report content.
- Initiate 10 consecutive high-frequency queries, and check whether interface responses meet business real-time requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.