What This Category of Data Looks Like
This category of data is sourced from molten iron and scrap steel entry data, L2 process control data, laboratory composition reports, MES system heat plan data, energy medium monitoring data, and equipment state sensor data. Data update frequencies cover per heat, real-time, and per-shift cycles. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Professional data fields include molten iron temperature (℃), carbon content (%), continuous casting pulling speed (m/min), equipment vibration amplitude (mm/s), and other specialized parameters. The data volume of a single heat document is approximately 1500-3000 characters.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The strong real-time nature of the data and its aggregation by heat require that dialogue contexts be bound to the current heat identifier, to avoid mixing data across different heats. Multiple document sources require that prompts clearly specify the type and field scope of referenced documents, to prevent retrieval of irrelevant data. The per-shift update frequency means overly long historical dialogues will introduce outdated information, so the context window length must be limited. The business characteristic of severe error consequences requires that prompts include priority judgment logic for exception handling, to prioritize response to risk scenarios such as continuous casting interruptions and heat scrapping.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context Window | First 20 turns of dialogue or 8000 characters, calibrated via actual testing | Matches the data volume of a single heat document, avoids introducing outdated cross-heat information |
| Historical Dialogue Retention Period | 7 days | Covers a complete shift cycle, meets the needs of cross-shift inspection traceability |
chatId | Bound to the unique identifier of the current heat | Addresses the strongly coupled business characteristics of heats, avoids mixing dialogue data across different heats |
messages | Stored grouped by heat ID | Facilitates retrieval of historical dialogue records by heat, aligns with the aggregation logic of business data |
variables | Populated with the batching list and composition report fields of the current heat | Provides scenario-specific parameters for prompts, improves the relevance of responses |
| Context Recall Quantity | Top 5 latest equipment inspection records | Matches the real-time updated characteristics of equipment data, prioritizes calling the latest status information |
Three Common Misconfigurations
- Symptom: Dialogue logs are empty after invoking the workflow. Cause: The current heat session is not bound via
chatId, leading to mixed session identifiers and incorrect log archiving. - Symptom: Response results differ from expectations under the same prompt. Cause: Dedicated process records and composition data for the current heat are not populated in
variables, resulting in the prompt lacking scenario-specific parameters. - Symptom: Dialogue history cannot be retained after configuration. Cause: The historical dialogue retention period is not set, or the system storage path is not correctly associated with session data.
How to Confirm Configurations Are Correctly Set
- Initiate a new dialogue, enter an inspection question for the current heat, and check whether the context includes the first 5 rounds of dialogue content.
- Check whether the
variablesfield has been populated with dedicated data such as the batching list and composition report of the current heat. - View the session storage directory, confirm that the log file corresponding to the
chatIdhas been generated and contains dialogue records from the past 7 days. - Invoke the workflow, verify that the returned results are associated with the process document data of the current heat.
The values provided are common starting points and should be measured against the reader's own samples.
FastGPT v4.17.0