What data for this category looks like
Data sources for this category include coating supplier TDS/SDS, spray booth and oven operating data, colorimeter and film thickness gauge test results, environmental monitoring data, and MES system data. Update cycles cover per-batch coating, shift-based operations, and continuous collection scenarios. Supporting documents include coating process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Fields include parameters with clear units such as coating viscosity, film thickness, oven temperature, and VOC concentration.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The data update cycles for this category are varied and linked to full-linkage processes. Multi-turn dialogue must retain process parameters and batch information from prior interactions to avoid repeating questions about already known details. Most document fields include clear units. Prompts must require matching specific test values with units, and avoid vague statements. Real-time data from continuous collection must prioritize the latest batch paint mixing records and oven curves during dialogue, to ensure analysis results align with current operating status. VOCs ledgers are tied to compliance requirements. Dialogue context must include the latest ledger update timestamp to ensure accurate responses to compliance-related questions.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum context | 800–1200 characters | Matches the average single-page length of coating-related documents, and avoids exceeding large model input limits |
| Number of retained conversation history entries | First 6 entries | Covers interaction information from the three core processes of pre-treatment, spraying, and curing, to meet multi-turn defect analysis needs |
variables binding scope | Bind current batch coating TDS and on-shift oven curve | Ensures prompts can call real-time business data exclusive to this category |
chatId persistence configuration | Enable session persistence with a 24-hour validity period | Aligns with FastGPT v4.17.0 session storage logic, and matches the cycle of shift-based operations |
| Context recall upper limit | Top 4 matching documents | Focuses on the most relevant coating process cards and defect repair orders, and avoids interference from redundant information |
Three common configuration mistakes
- Symptom: Conversation history cannot be deleted normally, and the interface displays a parameter error prompt. Cause: The
chatIdsession cleanup logic was not correctly bound, leading to a mismatch between the session identifier and actual storage. - Symptom: Empty results are returned directly when input content exceeds the preset length, with no interception prompt. Cause: No input length verification rule was configured, and no input upper limit reminder logic was added to the prompt.
- Symptom: Previous defect types mentioned earlier cannot be remembered after multiple rounds of dialogue, and the decomposition logic fails. Cause: The complete historical interaction of the
messagesarray was not retained, and only the context of a single interaction was called.
How to confirm correct configuration
- Initiate a first-round question that includes coating process parameters, and verify that the complete description of the parameter is retained in the conversation history.
- Input content that exceeds the preset length, and confirm that an interception prompt matching the business scenario pops up on the interface.
- Call the session cleanup interface, and verify that the conversation records for the specified
chatIdare completely cleared. - Initiate a question that requires linking multi-process data, and confirm that the response includes key parameters from prior interactions.
The values provided here are common starting points, and should be evaluated against samples relevant to the target use case.