What data this category uses
Business data for this category comes from concentrate and auxiliary material analysis reports, DCS control systems, electrolytic cell voltage and current monitoring, laboratory test results, and energy and environmental monitoring systems. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Fields cover mineral composition, furnace temperature, cell voltage, current values, metal recovery rates, and more. Update frequencies support real-time synchronization for each raw material batch, each furnace, or each electrolytic cell.
What constraints these characteristics impose on model access and configuration
Multiple heterogeneous data sources require the model to parse both structured numerical values and unstructured text. Context processing for multiple formats must be supported. Real-time updated business data means the model’s called context cannot include outdated information. A valid context duration limit is required. Professional metallurgical terminology and complex calculation logic demand priority for response accuracy. Hallucination risks must be reduced. Long individual document lengths (such as complete electrolytic cell condition records) require a context window that can hold sufficient valid content. Key information must not be truncated. Errors in equipment inspection and maintenance directly impact production indicators. Response confidence must be strictly controlled.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Adapts to the standard length of single electrolytic cell condition records and smelting records, avoids truncation of real-time updated documents |
maxTokens | 4000–6000 tokens | Meets integrated parsing requirements for multi-source documents including batching lists, laboratory reports, and environmental protection ledgers |
quoteMaxToken | 1500–2000 characters | Ensures recalled professional documents are fully passed into the context, avoids loss of key maintenance parameters |
temperature | 0.1–0.3 | Reduces hallucination risks, ensures accuracy of professional responses for equipment inspection and maintenance |
| Workflow/API tool selection | auto | Automatically adapts to parsing and tool calling requirements for multi-source data |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapts to model access addresses for local or cloud deployments, ensures normal operation of the calling link |
Three common configuration errors
- An empty parsing result is returned after uploading electrolytic cell condition records. The cause is that
quoteMaxTokenis not configured to adapt to the parsing length of long documents, resulting in truncation of key content. - Only 1 recall result is returned after enabling the reranking function. The cause is that the number of reranked results was mistakenly set to 1, which does not match the business requirement of comparing multiple cell conditions for this category.
- A 401 status code is returned when calling the model interface. The cause is that
AIPROXY_API_TOKENis configured incorrectly or the deployment environment’s key is not bound correctly.
How to verify successful configuration
- Upload a single complete electrolytic cell condition record. Check whether the model’s returned parsing result includes core fields such as cell voltage, current values, and anode quality.
- Enable the reranking function. Upload 3 electrolytic cell condition records from different time periods at the same time. Confirm that the number of returned recall results meets business expectations.
- Call the FastGPT v4.17.0 API interface. Check that the returned response status code is 200 and the content includes specific equipment inspection and maintenance suggestions.
- Adjust the
temperatureparameter to 0.2 and 0.3. Compare the consistency of professional terms in the two responses. Confirm that there are no obvious term errors or hallucination content.
The values given are common starting points and should be measured against the reader's own samples.