What the data for this category looks like
The data for this category comes from hot metal and scrap steel inventory data, L2 process control system, laboratory test reports, MES production management system, energy medium scheduling system, and equipment condition monitoring system. Data update frequencies cover per heat, real-time, and shift cycles. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Fields include temperature, component proportion, equipment operating parameters, and others. The overall data is real-time structured data for high-temperature continuous processes, with strong coupling between heats and casting sequences.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The real-time multi-source data update frequency for this category is strongly tied to heats. This requires that the context for multi-turn dialogue must be strictly limited to the heat cycle corresponding to the current bid project, to avoid mixing invalid data from old heats. There are many specialized document fields. Prompts must explicitly specify the exact fields to extract, otherwise parameter matching errors will occur in generated bid documents and quotations. The high-temperature continuous process cannot be shut down arbitrarily, so multi-turn dialogue must respond quickly to temporary on-site parameter adjustments, without waiting for excessive context loading time. The strong coupling between heats and casting sequences requires multi-turn dialogue to gradually coordinate rhythm parameters between steelmaking and continuous casting, to avoid logical confusion caused by a single dialogue covering too many linked links. This configuration logic is based on FastGPT v4.17.0.
How to Set Configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique chatId for each independent bid project | Prevents confusion of dialogue contexts across different bid projects, ensures session data isolation |
messages | Limit historical dialogue turns to 8–12, only retain content related to the current heat | Real-time data updates per heat; excessive historical dialogue will interfere with current bid parameter calculations |
variables | Bind structured fields from documents such as heat plans and composition reports | Ensures prompts can accurately call standardized data collected on-site |
| Streaming output interval | Return one data block every 1–2 seconds | Adapts to front-end display rhythm, improves user viewing experience |
| Context timeout threshold | 600 seconds | Matches the average cycle of a single heat operation, prevents session interruption due to timeout |
Three Common Misconfigurations
- Phenomenon: After calling API streaming output, link clicks only cover the current page and cannot jump to a new page. Cause: No jump identifier matching bid document format is embedded in the streaming returned content, so the front end cannot recognize the new page jump instruction.
- Phenomenon: Dialogue log details do not match the actual response content, and the details repeat consistently after multiple conversations. Cause: No independent session storage bound to each
chatId, and session data from old projects is reused. - Phenomenon: Streaming return interval is fixed at 4 seconds and cannot be adjusted. Cause: No return speed control parameter configured, default output rhythm is used, and real-time requirements for bid document preparation are not met.
How to Verify Successful Configuration
- Initiate a multi-turn dialogue containing heat composition and continuous casting process parameters, check that the returned content includes structured fields from the corresponding documents.
- Generate an independent
chatIdto initiate conversations for two different bid projects, check that session logs correspond to their respective project data separately. - Test streaming output, adjust the return interval parameter, verify that the update frequency of the front-end display meets expectations.
- View dialogue logs, confirm that each historical
messagesentry corresponds to correct bid link data.
The values provided are common starting points and should be measured against the reader's own samples.