What Data for This Category Looks Like
Data for this scenario typically comes from silicon wafer incoming material records, workshop MES systems, operation logs of diffusion furnaces, coating equipment, and printing presses, EL and IV test reports, and laboratory test data. Daily documents related to equipment inspection and maintenance include process cards, diffusion and coating parameter tables, printing and sintering process records, EL detection images, IV grading data ledgers, and yield analysis reports.
Data updates align with each batch of materials, each finished product test result, and daily production cycles. Fields across different documents include equipment operating parameters, test indicators, batch traceability codes, process window thresholds, and more. There are no unified fixed units, and values adjust based on the requirements of corresponding test items or processes.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Scattered data sources and fine-grained update cycles require multi-turn dialogue to bind chatId to associate context for the current production batch, preventing cross-batch data mixing. Frequently updated process parameters and test data require prompts to limit calls to production documents from the last 7 days, avoiding use of outdated process window information.
The presence of image attachments such as EL detection images requires prompts to explicitly support visual description parsing of image defects. The lack of unified units for fields requires prompts to require that each query includes a parameter unit description for the corresponding process, ensuring results match actual production requirements.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current production batch | Ensure multi-turn dialogue context only associates device data for the corresponding batch, avoiding cross-batch confusion |
messages | Retain the first 10 conversation histories | Photovoltaic production data has fine granularity; excessive history will interfere with current inspection queries. 10 entries cover recent process troubleshooting logic |
variables | Pass the current process, batch number, and detection parameter range | Limit the Q&A scope to the current production link, reducing irrelevant data recall |
| Maximum context length | 800–1200 characters | Balances context completeness and response speed, adapting to the text length of photovoltaic production parameters |
| Question Optimization | Enabled | Converts colloquial questions about equipment inspection into standard process query statements, improving document matching accuracy |
| Single conversation timeout | 600 seconds | Adapts to the time required for parsing large attachments such as EL detection images and generating complex maintenance solutions |
Three Common Misconfigurations
- Symptom: Invalid key error when calling the Java-wrapped dialogue interface. Cause:
appIdwas mistakenly used as the API key, and the correct application key from the FastGPT platform was not obtained. - Symptom: Q&A results do not associate with uploaded EL detection image content. Cause: No file type parameter was added to
variables, so the prompt did not trigger the image parsing logic. - Symptom: Dialogue output only returns fragmented content and does not generate complete maintenance suggestions. Cause: Insufficient historical context was retained in
messages, preventing the prompt from completing multi-turn reasoning.
How to Verify Successful Configuration
- Initiate a multi-turn dialogue for diffusion furnace inspection, check that
chatIdis consistently bound to the same batch identifier throughout the conversation. - Upload an EL detection image and ask about defect locations, confirm that the reply includes descriptive content related to the image.
- Call the custom-wrapped dialogue interface, use the correct API key to send a request, verify that the returned results meet the query requirements of the current process.
- Adjust the number of retained conversation histories, verify that response results match the troubleshooting logic of the current production link across different lengths.
The values provided are common starting points and should be measured against the reader's own samples.