What this category of data looks like
Typical entities in this category are hot rolling, cold rolling, section steel, and steel pipe enterprises. Daily generated 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, automatic mill controls, online thickness and width measurement tools, surface inspection systems, and MES systems. Data updates are generated in real time per coil or individual steel piece. Document fields include batch number, heating temperature, thickness deviation, surface defect level, and other items. Corresponding units are millimeters, degrees Celsius, level identifiers, and others. This configuration guide is developed based on FastGPT v4.17.0.
What constraints these characteristics impose on the model access and configuration workflow
Dispersed data sources include structured data collected by automated equipment and manually filled paper documents. Configurations must support multi-source data access and format compatibility. Real-time, coil-level update rhythms require configurations adapted to high-frequency incremental indexing and batch-associated context management. Documents contain multiple content types such as process specifications, inspection records, and quality certificates. Cross-review requires associating all documents for the same batch, so configurations need sufficient context storage space. Surface quality reports include image content. Configurations must support model access capabilities for multimodal vectors. Process review results directly relate to production quality. Configurations need strict generation parameters to reduce randomness.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Required to process defect detection images in surface quality reports, support multimodal document indexing and review |
temperature | 0.1–0.3 | Process parameter review requires strictness to reduce randomness of generation results |
maxContext | 8000–12000 token | Need to associate all related documents for the same steel coil batch, including rolling plans, heating schedules, and inspection records |
maxTokens | 2000–3000 token | Meet the length requirement for generating complete cross-review conclusions |
quoteMaxToken | 500–800 token | Extract valid review fragments from individual documents, avoid redundant content interfering with review logic |
AIPROXY_API_ENDPOINT | Fill in the actual deployment address of the proxy service | Adapt to multi-model access and proxy forwarding requirements |
AIPROXY_API_TOKEN | Fill in the key assigned by the proxy service | Complete identity verification for model calls |
Three common configuration errors
- After configuring
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, the call returns 404 body not found. Cause: When the proxy service coexists with other model forwarding services, no separate model routing rule is configured for the steel rolling scenario, leading to incorrect request forwarding. - After enabling the
visionconfiguration, multimodal vector models cannot be accessed. Cause: No dedicated forwarding rule for multimodal models is configured at the proxy layer. Requests are only forwarded in generic format, causing model incompatibility. - Cross-review results do not associate complete documents for the same steel coil. Cause: The
maxContextvalue is too small, truncating necessary document content such as batch-associated rolling specifications and thickness records.
How to confirm the configuration is complete
- Check the model access log to confirm the
visionconfiguration is active, and vector indexes for multimodal documents are successfully generated. - Submit a cross-review request for a single steel coil, verify that the number of associated documents in the returned result matches the preset
maxContextrange. - Verify the proxy configuration, confirm that calling
AIPROXY_API_ENDPOINTreturns normal model responses without 404 errors. - Adjust the
temperatureparameter, observe changes in the rigor of review conclusions to confirm the parameter takes effect.
The values provided are common starting points, and should be evaluated against the reader's own samples.