What the data for this category looks like
This category’s daily data includes batching lists, smelting records, electrolyzer condition records, laboratory test reports, anode and cathode quality records, environmental protection ledgers, and other materials. Data sources cover concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring equipment, laboratory testing, energy and environmental protection monitoring devices. Data update frequency changes based on raw material batches, smelting heats, or electrolyzer operating status. Some real-time monitoring data has a relatively high synchronization frequency. Data fields include mineral impurity proportions, cell voltage and current parameters, metal recovery rates, byproduct disposal directions, and more. Units include percentages, amperes, volts, tons, and other standard units.
What constraints do these characteristics impose on the model access and configuration link?
Multiple heterogeneous data sources require the model access link to adapt to business documents in different formats. This ensures the system correctly reads and verifies raw material composition, cell condition records, and other relevant data. Frequently updated real-time data requires context configuration to support quick synchronization of the latest status. This prevents tender documents from using outdated furnace condition or laboratory data. For refined processing requirements related to mineral impurities and metal recovery, model tool calls must match exclusive business fields for smelting and electrolysis. These fields differ from carbon-iron system parameters used in categories such as steel. Targeted configuration of tool trigger rules is therefore necessary.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Tender documents need to accommodate multiple sets of raw material composition, cell condition records, metal accounting, and other content, requiring sufficient context space |
temperature | 0.1–0.3 | Quotations and tender documents must remain rigorous, avoiding generated fictional content that deviates from actual business data |
| Workflow/API tool selection | auto | Automatically determine whether to call exclusive tools such as batching balance and metal recovery rate accounting, adapting to multi-source data verification |
AIPROXY_API_ENDPOINT | Fill in according to the official access address provided by the cloud service provider | Adapt to compliant large model call links, ensuring secure data transmission |
quoteMaxToken | 1500–2000 characters | Tender documents need to reference many fragments such as laboratory reports and cell condition records, reserving sufficient space for referenced content |
Note: This page is compatible with FastGPT v4.17.0
Three common configuration mistakes
- Phenomenon: The large model generates tender documents with impurity proportions that do not match actual laboratory data. The tool call does not trigger batching balance verification. Cause:
toolChoiceis not set to auto, or tool parameters are not aligned with the business field format for smelting and electrolysis. This prevents the model from automatically calling exclusive accounting tools. - Phenomenon: Timeout errors occur during large model calls, with execution time exceeding 10 seconds. Cause: Reasonable value limits for
maxTokensare not applied, or context truncation rules are not configured. This causes the model to process excessive redundant historical cell condition and smelting record data. - Phenomenon: Batching balance results from code running in the workflow cannot be passed to the model. The interface displays the
chat:ai_input_is_eerror. Cause: The result field output by code running is not correctly mapped to the model input context. This leads to missing business data fields received by the model.
How to confirm successful configuration
A test question containing typical electrolyzer conditions and raw material compositions is submitted. The model output is checked to confirm it includes accurate metal recovery rate calculations and tender quotation content. Large model call logs are reviewed. AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN are confirmed to be configured correctly, with no connection failures or permission errors. Tool call links are triggered. The batching balance result generated by code running is checked to confirm it is correctly injected into the model input context, with no empty fields. The temperature parameter is adjusted to 0.2. The generated tender document is verified to remain rigorous, with no fictional descriptions deviating from actual business data.
The values provided are common starting points and should be measured against the user’s own samples.