What Data for This Category Looks Like
Data sources for this category include TDS/SDS from coating suppliers, spray booth and oven operation data, data collected by colorimeters and film thickness gauges, environmental monitoring records, and MES system data. Daily documents include coating process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs ledgers. Each document includes professional fields such as coating viscosity (Pa·s), film thickness (μm), oven temperature (℃), and VOCs concentration (mg/m³). Update frequency is per coating batch and per shift. Key operational data is collected continuously.
What Constraints These Characteristics Impose on Multi-Turn Dialogues and Prompt Words
The data characteristics of this category impose clear constraints on multi-turn dialogue and prompt word configuration. Frequently updated coating and process data across multiple batches require that dialogue contexts be accurately bound to the document identifiers of the corresponding batch, to avoid mixing data across batches. Continuously collected real-time operation data requires quick location of parameters within a specified time window during multi-turn interactions. Prompt words must clearly define the time range and batch association rules for data. Differences in field units across different documents, such as μm for film thickness and Pa·s for coating viscosity, require unified unit conversion rules in the dialogue logic to prevent parameter matching deviations. Compliance reviews involving VOCs ledgers and defect repair sheets require fixed field verification logic. Prompt words must embed preset verification processes.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique string of the coating batch number | Ensure that dialogue history is only associated with documents such as process cards and paint mixing records for the corresponding batch, to avoid mixing data across batches |
variables | Preset three variables: batch number, detection time, and defect type | Used to quickly filter target data during multi-turn interactions and narrow the retrieval scope |
messages | Retain the first 3 valid interaction contexts | Adapt to the high-frequency update characteristics of data in this category, and avoid interference from redundant historical data on current review logic |
| Maximum Context | 800–1200 characters | Adapt to the average length of coating-related documents for a single batch, ensuring that core parameters are fully loaded |
| Question Optimization | Calibrate based on actual measurements | Optimize question wording for coating professional terminology to improve the accuracy of professional data retrieval |
Three Common Misconfigurations
- Symptom: When calling the API to initiate a review dialogue, a batch identifier is specified, but irrelevant batch dialogue data is returned when retrieving historical records. Cause:
chatIdis not bound to the coating batch number, resulting in the session identifier not being accurately associated with the document data of the corresponding batch. - Symptom: Inconsistent units of film thickness data returned during multi-turn dialogue, with mixed use of μm and mm. Cause: No preset unified unit conversion rule is included in the prompt words, and field unit verification is not enforced during interactions.
- Symptom: A code running node is added after the AI dialogue node to remove think tags. The debug phase works normally, but thinking content is still displayed during formal operation. Cause: No unified processing logic for multi-turn dialogue history is configured in the workflow, and think tags from some historical interactions are not cleaned up.
How to Verify Proper Configuration
- Initiate a test dialogue, enter keywords for the coating process of the specified batch, and verify that the returned historical dialogue is only associated with documents for that batch.
- Adjust the context window parameter, test loading coating documents of different lengths, and confirm that core parameters are not truncated or redundant.
- Trigger multi-turn interactions, verify that the field units returned after each question remain consistent.
- Call the API to retrieve historical records, and verify that only session data bound to the current
chatIdis returned.
The values provided are common starting points and should be measured against the reader's own samples.