What the data for this category looks like
Data sources include raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory test reports. Daily documents include batching records, coating and calendering parameter sheets, winding/laminating process records, liquid injection ledgers, formation and grading data, OCV and safety test reports. Data is synchronized in real time per batch and per individual cell. Fields include process numbers, equipment operating parameters, test values, batch identifiers, and more. No unified fixed units are used; adjustments are made based on the corresponding test items.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category has strong real-time performance and is split by batch. Multi-turn dialogue must strictly bind the context of the current production batch to avoid mixing parameters across batches. Daily documents mostly consist of process-level parameters and test values. Prompts must clearly define the process scope of the equipment, such as coating, winding, or formation, to narrow the retrieval range. Data sources are scattered and fields have no unified format. Dialogue workflows must guide users to supplement key identifiers such as process numbers and equipment models to ensure accurate retrieval matching. Real-time updated data requires that the dialogue context window not be too large, to avoid loading outdated batch data that affects decision-making.
Configuration Settings
Configurations under FastGPT v4.17.0 can be referenced using the following table:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind current production batch number and process type | Used to anchor test data and process documents for the corresponding batch during multi-turn dialogue |
chatId | Bind batch identifier for the current work session | Used to distinguish dialogue contexts for different work batches, avoiding mixing parameters across batches |
messages | Retain the most recent 15 dialogue entries | Matches the dialogue interaction rhythm of single-batch processes for this category, avoiding interference from redundant historical data |
| Context window upper limit | 5000 characters | Matches the volume of single-batch process data for this category, preventing loading of excessive outdated data |
| Number of returns per single retrieval | Top 8 entries | Narrows the retrieval scope, focusing on core equipment documents and test data for the current process |
| Session timeout period | 600 seconds | Matches the single interaction duration for equipment inspection in this category, preventing premature session termination |
Three Common Mistakes
- Symptom: Files uploaded to the dialogue box fail to parse consistently, with no error logs in the backend. Cause: No dedicated document parsing rule configured for this category, so structured fields in cell manufacturing documents such as batching records and coating parameter sheets cannot be recognized.
- Symptom: Parameters across batches are mixed during multi-turn dialogue. Cause: The
chatIdis not used to bind the current work batch identifier, causing the context to load historical data from other batches. - Symptom: Maintenance suggestions output by the AI do not match the current process. Cause: The prompt does not limit the process scope of the equipment, resulting in retrieval of equipment documents from unrelated processes.
How to Confirm Correct Configuration
- Initiate an inspection dialogue for the coating process roller press. Verify that returned documents only include parameters and test data related to coating and calendering.
- Switch the work batch and initiate a new dialogue. Verify that the context automatically switches to data related to the new batch, with no retained content from the old batch.
- Upload a batching record document for this category. Verify that the system correctly parses the batch number and feeding data contained within it.
- Initiate consecutive multi-turn interactions. Verify that the dialogue context retains only the most recent 15 entries, with no excessive historical data loaded.
The values provided are common starting points and should be measured against the reader's own samples.