What Data for This Category Looks Like
Data sources for this category include material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers and metallographic equipment, and MES systems. Data is updated per furnace charge and per batch. Daily generated documents include heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and abnormal handling forms. Data fields cover heating temperature, holding time, hardness values, and similar items, with corresponding units such as degrees Celsius, hours, and Rockwell hardness units. Field formats and collection logic differ across documents.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
Independent data for each furnace charge and batch requires dialogue context to be bound to a single furnace charge identifier, preventing cross-furnace data confusion. Multiple document types include text and image content, requiring multi-turn dialogue to support multimodal input and cross-document association. Specific unit and format requirements for fields mean prompts must clearly restrict output units and formats, lowering manual verification costs. Differences in multi-source data collection logic require the dialogue process to gradually verify parameters collected by different equipment, ensuring data consistency.
How to Configure the Setup
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of a single furnace charge | Isolate dialogue contexts across different furnace charges to avoid cross-data interference |
variables | Bind variables for furnace number, material grade, and customer number | Quickly locate exclusive documents and parameters for the current furnace charge, reducing context retrieval costs |
messages | Arrange in the order of "system prompt + historical dialogue + current task" | Ensure the large model prioritizes reading the latest task instructions and context data |
| Multimodal input switch | Enabled | Support processing image documents such as charging charts and furnace temperature curves |
| Single dialogue input limit | 800–1200 characters, calibrated based on actual testing | Avoid input overload leading to model response delays or errors |
| Context window | Retain the first 10 rounds of dialogue + documents related to the current furnace charge | Balance context completeness and model processing efficiency |
Three Common Mistakes
- Phenomenon: Calling the multimodal dialogue interface returns a 400 invalid image error, and the dialogue log shows the image field was not loaded correctly. Cause: Image data such as charging charts and furnace temperature curves was not embedded into the
messagesparameter in the required format. - Phenomenon: Uploaded documents such as process cards and hardness reports are not used by the large model for statistical summarization. Cause: The uploaded file metadata or content was not bound to the
variablesparameter, preventing the model from reading context data. - Phenomenon: Response lag occurs during multi-turn dialogue. Cause: The context window size for single dialogue was not limited, causing the model to process excessive historical dialogue and document data.
How to Confirm the Configuration Is Correct
- Check whether the
chatIdparameter is bound to the unique identifier of a single furnace charge, ensuring dialogue contexts across different furnace charges are isolated. - Verify whether the
variablesparameter includes necessary fields such as the material grade and customer number of the current furnace charge. This can be confirmed by viewing variable replacement results in the dialogue log. - Test uploading images such as charging charts and furnace temperature curves, then initiate a query such as "Describe the charging position of this furnace charge", and confirm whether the model correctly identifies and associates the image content.
- Adjust the character length threshold for single dialogue input, setting it based on a balance between model response speed and accuracy.
The values provided here are common starting points and should be measured against the reader's own samples.