What this category of data looks like
Data for this category comes from flow, pressure, temperature, and analytical instrument manufacturers and metrology laboratories. It includes assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version logs. Data is sourced from design documents, standard instrument and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Updates occur once per calibration for each device. Document fields include device under test number, calibration environmental parameters, error calculation values, traceability chain number, and other relevant fields. Units include standard measurement-related units such as Pa, ℃, and ppm.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The high-frequency update cycle for each device calibration requires conversation context to be tightly bound to the single calibration process of one device, to prevent cross-device data mixing. Mixed input of multiple structured document types requires prompts to explicitly prioritize reading core audit fields such as metrology certificates and error curves. Audit links such as traceability chains and environmental conditions have strong correlations, so multi-turn dialogue must retain records of each audit node. This ensures complete process traceability during subsequent reviews. Differences in field units across documents require prompts to standardize unit conversion rules, to avoid calculation errors when comparing or combining data across documents.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | First 15–25 historical messages | Aligns with the number of audit steps for a single device’s single calibration, avoids interference from redundant data |
variables binding rule | Bind device under test number and calibration environmental parameter variables | Maps to core fields of documents in this category, tightly ties to the single device calibration process |
messages context filtering | Only retain conversation content from the current calibration batch | Meets the update frequency requirement of managing calibration data by batch for this category |
| Prompt template variable | Embed {{platform current date}} | Meets fixed formatting requirements for calibration certificate signature dates, aligns with delivery specifications |
chatId binding rule | Bind unique identifier for a single device’s single calibration | Prevents cross-device conversation data mixing, ensures complete audit traceability |
| Maximum single conversation duration | 600 seconds | Aligns with single-round operation duration for calibration audits, avoids timeout interruptions |
Three Common Misconfigurations
- Symptom: Conversation history is not saved, and reply content is empty after reopening the page. Cause: The
chatIdparameter is not bound, or history record storage is not enabled in the configuration, so conversation context is not persisted. - Symptom: The
{{platform current date}}variable used in prompts does not correctly generate signature dates. Cause: The variable is not properly embedded in the prompt template, or format conflicts exist in the template that cause variable parsing failure. - Symptom: Replies from a prior AI conversation in the workflow are included in the final output. Cause: Context filtering rules are not configured, so historical messages are not truncated, and redundant content from multi-turn dialogue is returned along with the final result.
How to Verify Successful Configuration
- Initiate a calibration audit conversation for a single device, and confirm that only historical messages from the current batch are displayed in the context window.
- Insert the
{{platform current date}}variable into the prompt, generate test content, and confirm that the signature date matches the system current date. - Copy the
chatIdof the current conversation, reopen the conversation page, and confirm that historical messages match the earlier content. - Configure multi-turn dialogue in the workflow, test that only the final round of audit results is output, and confirm that prior round content is not included.
The values listed here are common starting points, and should be validated against relevant operational samples.