What This Category of Data Looks Like
Data sources include hot metal and scrap metal test data, L2 process control data, laboratory composition reports, MES production management data, energy medium system data, and equipment condition monitoring data. Data updates are generated per heat, real-time, or on a shift basis. Daily produced documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Each document contains fields such as heat number, cast number, temperature parameters, element content, and equipment operating status, with corresponding different units and exclusive business logic.
What Constraints Do These Characteristics Impose on the Multi-turn Dialogue and Prompting Link
Since data is updated per heat, real-time, or on a shift basis, dialogue context must limit the valid time range to avoid using expired heat data. Fields and units vary across multiple document types, so prompts must clearly specify the target document type and field requirements to prevent mixing parameters and units from different documents. The strong coupling business logic between heat and cast numbers requires that multi-turn dialogue continuously associates information about the current heat and corresponding cast number, ensuring that drafted process documents and exception reports align with production rhythms. For the need to integrate multi-source data, prompts must clearly specify the data source type of the corresponding system to ensure content accuracy.
How to Configure Settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current heat | Ensure multi-turn dialogue is associated with production data for the same heat |
messages | Organize conversation content in a heat-cast hierarchy | Match the business process logic of iron and steel smelting |
variables | Pre-set core variables such as heat number and cast number | Simplify repeated input of professional parameters |
| Maximum context window | Previous 8 rounds of dialogue + real-time production data | Cover the complete operation cycle of a single heat |
| History retention duration | 10 hours | Match the production scheduling cycle of one shift |
| Single reply token limit | 8000 token | Adapt to the length requirements of professional documents |
Three Common Mistakes
- Formal generated documents include thinking process content. This occurs because the thinking process switch is not turned off, causing auxiliary reasoning text to be included in the output.
- Conversation history cannot be associated with production data for the current heat. This occurs because
chatIdis not bound to a unique heat identifier, causing the conversation context to be disconnected from actual production data. - Generated document fields do not match actual production requirements. This occurs because the prompt does not clearly specify the target document type and fields, leading to parameter mixing of business logic and fields from different documents.
How to Confirm Configurations Are Set Correctly
- Check that the FastGPT version is v4.17.0, and confirm that the current environment supports calls to the corresponding configuration items.
- View the
chatIdconfiguration item, confirm that it is bound to the unique identifier of the current heat, initiate a test conversation, and verify that the conversation is associated with the correct production data. - Initiate a query containing professional terminology, check that no thinking process text is included in the output, and confirm that the thinking process switch is turned off.
- Call the pre-set heat number and cast number variables in
variables, verify that the variables can be correctly substituted into the generated process documents and exception reports.
The values provided here are common starting points and should be evaluated against the reader's own samples.