What data for this category looks like
This category typically includes documents such as batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratories, and energy and environmental monitoring systems. Updates are triggered per batch of raw materials, per furnace, or per cell. Some real-time monitoring data updates more frequently. Fields include raw material component proportions, furnace temperature, cell voltage and current, laboratory result values, and environmental emission indicators, with units such as percentage, voltage, current, and quality grade.
Constraints on model access and configuration
This category has scattered data sources and widely varying update cadences. Some monitoring data updates in real time, while other data is generated per furnace or cell batch. Model access must therefore adapt to batch processing and real-time invocation logic for multi-source heterogeneous data. During configuration, adjust context window parameters to match data processing needs for different update frequencies.
Documents contain professional test values and units. The model must support recognition of non-ferrous metal smelting terminology and format validation, and parameter adjustments must be made to improve long-text processing capabilities. Additionally, data association logic is complex: raw material components, furnace conditions, and laboratory results influence one another. Limit the total context per single invocation to avoid information overload.
How to set configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents for this category mostly consist of associated data across multiple batches and cell positions, requiring sufficient capacity to carry historical records and real-time monitoring data and avoid truncation of critical audit information |
maxTokens | 2000–3000 token | Inspection and audit tasks mostly focus on validation for a single batch or single cell condition, so overly long generated content is unnecessary, and this range reasonably controls computing resource consumption |
quoteMaxToken | 4000–6000 token | Sufficient associated documents such as batching lists and laboratory reports must be retrieved to cover core audit basis including raw material components and furnace condition parameters |
| Workflow/API tool selection | auto | Automatically select between document retrieval or data query tools based on task requirements, adapting to validation and association analysis needs for multi-source data |
temperature | 0.1–0.3 | Inspection and audit tasks require precise result output, avoiding random deviations in generated content |
AIPROXY_API_ENDPOINT | Fill in the actual deployment address of the enterprise's internal proxy service | Unify model invocation management, adapting to the enterprise's internal network environment and access permission policies |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding proxy service | Ensure the security of model invocation, restricting unauthorized access requests |
Three common configuration mistakes
- Model invocation returns the error "model does not exist or is unreachable". The cause is incorrect configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, or the proxy service not synchronizing the deployment address and access key for the corresponding model. - Database or tool invocation fails in the workflow. The cause is failure to correctly set the
toolChoiceparameter toauto, or failure to bind the corresponding data query tool permission in the configuration, resulting in blocked tool invocation. - Critical information is truncated in audit results. The cause is an excessively small
maxContextvalue, which fails to adapt to the long-text processing requirements of multi-batch, multi-document association for this category, leading to omission of core audit data.
How to confirm successful configuration
- Invoke the model test interface, input simulated text containing batching lists and laboratory reports, and check whether the returned results include content such as raw material component verification and furnace condition analysis, to confirm that context loading works correctly.
- Trigger a tool invocation test, verify whether associated electrolytic cell condition records and environmental protection ledger data can be automatically retrieved, to confirm that the
toolChoiceand tool binding configuration takes effect. - View the model invocation logs, confirm that the request formats for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correct, with no 401 or 404 access errors. - Adjust the
temperatureparameter to 0.2, generate multiple audit results, and confirm that the output content remains consistent and precise, with no random deviations.
The values provided here are common starting points, and should be evaluated against the user's own samples.