What the data for this use case looks like
Heat treatment production ledger data originates from material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and MES systems. Data is updated per furnace charge and batch. Daily documents include heat treatment process cards, charging diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling forms. Core fields include furnace number, heating temperature, holding time, cooling rate, hardness value, and metallographic structure grade. Units include degrees Celsius, hours, Vickers hardness units, millimeters, and other standard units.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Single furnace charge data is updated per batch and has many field dimensions. Multi-turn dialogue is used to sequentially verify consistency between charging positions, furnace temperature curves, and process cards. This prevents mixing data across different furnace charges. Multiple document types require separate extraction logic. Prompts must clearly specify corresponding fields for each document type. Data sources are dispersed. Dialogue must align field formats across different devices and MES systems. Non-conforming data linked to exception handling forms requires multi-turn verification. This ensures no abnormal items are omitted from generated reports.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Include three core variables: furnace number, process temperature, and hardness test values | The core identifiers and result fields of heat treatment ledgers are furnace number, process parameters, and performance data. Binding variables allows quick location of full data for a single furnace charge |
messages | Previous 10 turns of dialogue context | The single furnace charge processing flow includes no more than 10 steps such as charging, temperature measurement, and exception handling. Retaining the last 10 turns covers full flow interactions |
chatId | Bind furnace number as the unique identifier | Each furnace charge has independent data. Associating chatId with the furnace number avoids mixing data across furnace charges, ensuring dialogue context only relates to the current furnace charge |
| Context window | 8000–12000 token | The ledger for a single furnace charge includes content from multiple spliced documents. The window must cover all relevant data such as process cards and furnace temperature curves |
| Multi-turn trigger condition | Trigger one verification for each new ledger field input | Sequential verification of field consistency across multiple documents such as charging diagrams and furnace temperature curves is required, to avoid missed checks caused by too many inputs in a single turn |
Three Common Misconfigurations
- Symptom: The furnace number field entered in the form cannot be displayed in the dialogue. Cause: The furnace number variable was not bound to the
variablesparameter, so the corresponding field was not loaded into the context. - Symptom: The generated report lacks hardness test related data. Cause: The prompt did not explicitly require associating output results from hardness testers and metallographic equipment, and only extracted content from a single document.
- Symptom: Dialogue times out with no response. Cause: The context window was set beyond the system's carrying limit, and the window size was not adjusted based on the data volume of a single furnace charge.
How to Confirm the Configuration is Correct
- Enter the process card number for a single furnace charge, and check whether the dialogue automatically associates the
variablesdata for the corresponding furnace number. - Enter key parameters from the charging diagram and furnace temperature curve in sequence, and check whether the dialogue sequentially verifies field consistency.
- Trigger report generation, and check whether the output content includes all required ledger document fields.
- Simulate input from an exception handling form, and check whether the dialogue triggers the corresponding verification flow.
The values provided here are common starting points, and should be evaluated against the reader's own samples.