What Data for This Category Looks Like
Business data for photovoltaic module encapsulation scenarios originates from wafer sorting systems, glass, adhesive film, and backsheet batch ledgers, production MES platforms, string welding and lamination equipment, and EL and IV testers. Daily generated documents include layout drawings, material batch records, lamination process records, EL defect images, IV test reports, insulation voltage withstand records, and finished product packing lists. Data updates follow a set schedule: one test and process record per module, with batch ledgers updated synchronously for each raw material batch. Individual documents include dedicated fields such as wafer current gear, lamination temperature curve, EL hidden crack locations, and power test values.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated frequently per individual module and batch, and includes multiple specialized document types. These traits create multiple constraints for multi-turn dialogue and prompt configuration. Use chatId to bind all full-process data for a single module, ensuring multi-turn dialogue traces records for the same production unit. Use prompts and variables to limit retrieval scope, preventing retrieval of irrelevant documents from other batches. Adapt to specialized fields across multiple document types, and gradually clarify key details such as equipment models and material batches mentioned by users during multi-turn dialogue, to keep retrieval results aligned with the business scenario.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the current module serial number or production batch number | Associate full-process process, test, and batch data for the same production unit, ensuring multi-turn dialogue context accurately matches |
messages | Retain the most recent 8–12 rounds of conversation content | Adapt to multi-turn traceability requirements for photovoltaic encapsulation processes, avoiding context overflow that reduces retrieval accuracy |
variables | Pre-set business variables such as wafer current gear, lamination temperature range | Quickly narrow retrieval scope to locate dedicated process records for the corresponding batch |
| Single search upper limit | Top 3 results | Specialized process document types are concentrated in this scenario, and excessive results will interfere with the model's accurate judgment of standard content |
| Question optimization | Enabled | Convert users' colloquial questions into industry-standard retrieval keywords, improving recall matching rate |
Three Common Configuration Errors
- Symptom: When calling the API with the
chatIdparameter, the corresponding identifier does not appear in the conversation log, and retrieval results include component data from unrelated batches. Root cause:chatIdis not bound to the business production batch number in the FastGPT v4.17.0 conversation configuration, causing context association to fail. - Symptom: After configuring prompts, the model retrieves unrelated document types, such as returning both EL defect images and packing lists. Root cause: No preset document filtering conditions via
variables, and prompts do not limit retrieval scope to process specifications and technical standard content. - Symptom: During multi-turn dialogue, the model cannot recall the wafer current gear information mentioned in the previous round. Root cause: Insufficient retained
messagescontext, or the configured context length does not cover the full process of process traceability.
How to Verify Correct Configuration
- Manually pass a test
chatId, then check if the corresponding identifier appears in the conversation log to confirm the context association logic functions correctly. - Input a question referencing a specific material batch, then verify that the model only retrieves process records for that corresponding batch, confirming retrieval scope filtering is active.
- Launch multiple consecutive questions, then verify that the model can recall details such as equipment models and test parameters mentioned in previous rounds, confirming
messagescontext retention works properly. - Access the FastGPT v4.17.0 AI configuration interface, confirm the question optimization function is enabled, and verify that colloquial questions are correctly converted into professional retrieval keywords.
The values provided here are common starting points, and should be evaluated against the reader's own samples.