What Data for This Category Looks Like
Daily data for this category covers documents including batching lists, smelting records, electrolyzer status records, laboratory reports, anode and cathode quality records, environmental protection ledgers, and more. Data sources include concentrate and auxiliary material analysis reports, DCS systems, cell voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring devices. Data updates proceed in real time alongside raw material batches, smelting heats, and electrolyzer operations. Individual records include fields such as raw material composition parameters, furnace status indicators, cell state, metal recovery rate, pollutant emission values, and others. Units include percentage, ton, volt, ampere, and additional standard units.
Constraints on Multi-turn Dialogue and Prompt Engineering
This category’s data has strong real-time performance, specialized fields, and frequent updates, which impose multiple constraints on multi-turn dialogue and prompt configuration. Multi-turn dialogue must retain the most recent real-time status data of electrolyzers and smelting furnaces to avoid expired context. Prompts must explicitly specify extraction of only concentrate composition, cell voltage and current, and other specialized fields from the current raw material batch, to prevent confusion with historical data from different batches. Additionally, to balance impurities in multiple raw materials and metal recovery rates, multi-turn dialogue must advance accounting steps sequentially by raw material batch and heat, to avoid interference from cross-batch data.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 1000–1500 characters | Adapts to the length of specialized documents with multiple fields for this category, avoids truncating key composition data, and aligns with the context processing logic of FastGPT v4.17.0 |
variables Binding | Bind real-time cell voltage and laboratory report fields | Ensures prompts call the latest business parameters |
chatId Configuration | Bind the exclusive ID of the current tender project | Isolates dialogue data from different tender projects to prevent interference |
| Dialogue History Retention Duration | Calibrated through actual testing, maximum 12 hours | Matches the operation cycle of each heat and batch |
| Prompt Field Limitation | Only extract raw material and furnace status data from the current batch | Aligns with the specialized data requirements of this category |
Three Common Misconfigurations
- Phenomenon: The prompt fails to recognize component parameters separated by spaces in batching lists, and returns incorrect raw material proportions. Cause: The prompt does not explicitly specify spaces as separators for component parameters, causing the model to confuse field boundaries.
- Phenomenon: When calling an application published via API, multi-turn dialogue context is lost, and previous cell status data cannot be recalled. Cause: The
chatIdparameter is not correctly bound, causing each API request to generate a new session and preventing reuse of historical dialogue data. - Phenomenon: Dialogue logs show as empty or fail to load, and previous tender accounting steps cannot be traced. Cause: Dialogue history retention duration is not configured, or the Docker configuration file does not correctly map the log storage path, resulting in non-persistent logs.
How to Verify Proper Configuration
- Initiate a simulated tender accounting dialogue, and check whether the reply includes the currently bound real-time cell status and laboratory report fields.
- Switch between different tender project IDs, and verify that after binding
chatId, dialogue data from different projects does not interfere with each other. - View the dialogue log storage directory, and confirm that logs are generated by session ID and adhere to the retention duration requirements of the business cycle.
- Input component parameters containing spaces, and check whether the prompt can correctly identify and extract the corresponding fields.
The values provided here are common starting points and should be measured against the reader's own samples.