What Data for This Category Looks Like
This category covers business assets of flow, pressure, temperature, and analytical instrument manufacturers and metrology laboratories. Data sources include design documents, standard instrument monitoring data, calibration software outputs, laboratory information systems, and customer range requirements. Updates occur after each calibration for every device. Daily documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version logs. Fields include measured parameter values, error ranges, calibration environmental parameters, metrology certificate numbers, software version numbers, and more. Formats are adjusted dynamically based on calibration scenarios.
How These Characteristics Impact Multi-turn Dialogue and Prompt Engineering
Data for this category is scattered across multiple systems and different documents, and updates after each calibration for every device. This requires the dialogue system to retrieve the latest data in real time. Multiple types of documents include visual content such as error curves. This requires the dialogue flow to support recognition of non-text knowledge base entries. Calibration records are core deliverables that must strictly match traceability chain information. Multi-turn dialogue needs to gradually verify parameter compliance to reduce the risk of failed metrology audits. A single dialogue must cover multi-field verification to avoid deviations being passed to customer production links.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of each calibrated device | Enable dedicated dialogue context for single devices, avoid cross-device data confusion |
messages | Retain the most recent 20 turns of dialogue content | Adapt to the multi-step traceability needs of calibration workflows, avoid context loss |
variables | Bind device number and calibration date variables | Automatically fill retrieval conditions to accurately match corresponding calibration records |
| Single dialogue timeout | 120 seconds | Cover time-consuming steps such as multi-point error collection and traceability chain review |
| Knowledge base recall limit | Top 8 matching results | Cover multiple types of documents including calibration procedures, original records, and error curves |
| Maximum context token count | 8000–12000 token | Adapt to the content length after splicing multiple documents, avoid truncating critical information |
Three Common Configuration Mistakes
- Symptom: When using a strict question-and-answer template to pull in the knowledge base, an image-type knowledge base entry is retrieved, and the interface prompts that no answer is found. Cause: The strict question-and-answer template only supports matching text-based question-and-answer pairs, and cannot recognize image-based knowledge base content such as error curves.
- Symptom: Multiple dialogue requests are sent consecutively in a short period, and subsequent requests enter a waiting state without executing immediately. Cause: No dialogue interruption trigger logic is configured. The system processes all requests in queue order by default, which cannot meet the rapid multi-turn traceability needs of calibration scenarios.
- Symptom: When calling the FastGPT API, an error is returned stating "No permission to operate this dialogue record". Cause: The
chatIdparameter is not correctly bound, causing the ownership verification of the dialogue context to fail. The knowledge base and context content of the specified dialogue cannot be accessed.
How to Confirm Configuration Is Complete
- Upload an error curve image to the knowledge base, initiate a query using the strict question-and-answer template, and confirm that the calibration parameter information associated with the image can be returned normally.
- Send two consecutive dialogue requests with an interval of less than 1 second, and confirm that subsequent requests can execute immediately without waiting delay.
- Call the API with the correct
chatIdparameter, and confirm that the returned result contains the context content of the corresponding dialogue without permission errors. - Configure a content export node in the workflow, and confirm that a document download link containing calibration records and error data can be generated.
The values provided are common starting points and should be measured against the reader's own samples.