What Data for This Category Looks Like
Normally, inspection and process record data for crystalline silicon photovoltaic cell manufacturing comes from wafer incoming material ledgers, production MES systems, real-time operating parameters of diffusion furnaces, coating equipment, and printing presses, test results from EL and IV testing equipment, and laboratory yield analysis reports. Daily documents include process cards, diffusion and coating parameter tables, printing and sintering records, EL detection grayscale images, IV grading data tables, and batch yield reports. Data update frequency is typically synchronized once per production batch. Within a single batch, updates are made in real time for each wafer. Device parameters are refreshed per operating cycle. Data fields include initial wafer parameters, process window values, defect coordinates, electrical performance conversion efficiency values, grading levels, and more. Some documents include images and multi-dimensional numerical units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and include both images and text. This requires the multi-turn dialogue module to handle both structured numerical and unstructured image documents at the same time. Frequently updated batch data requires dialogue contexts to be strictly linked to the session associated with chatId, to avoid mixing cross-batch data. The need to associate parameters across multiple processes requires prompts to bind variables to pass exclusive document content for each process. The large volume of data per single batch requires limiting the historical rounds of messages to prevent analysis deviations caused by context overflow.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 token | Matches the total character count of multi-process documents for photovoltaic cells, prevents messages context overflow |
| Session ID Binding Rule | Bind production batch ID and chatId | Ensures multi-turn dialogue only links to inspection and record data for the current batch, avoids cross-batch confusion |
| Variable Binding Configuration | Bind variables as exclusive identifiers for process cards, EL detection images, and IV data | Enables prompts to accurately call business document content for the target category |
| File Upload Timeout Setting | 300 seconds | Adapts to upload times for large files such as EL detection images, prevents mid-transfer disconnections |
| Document Type Preset | Split by business scenario | Differentiates analysis logic for process parameter documents and defect detection documents |
This configuration is based on FastGPT v4.17.0.
Three Common Misconfigurations
- A 503 error appears when uploading documents via the dialogue window, and backend logs show the file was uploaded successfully: The temporary file storage quota of the multi-turn dialogue module does not match the single-file size limit for photovoltaic cell EL detection images, causing session storage to trigger rate limiting.
- Cross-process parameter data cannot be linked during multi-turn dialogue:
variableswere not correctly bound, so prompts cannot call exclusive document content for different processes. - Cross-batch inspection data appears in dialogue history: No binding rule between
chatIdand production batch was configured, causing context from different sessions to mix.
How to Verify Proper Configuration
- Upload a single EL detection image and an IV grading data table, then initiate a query to confirm the dialogue can extract exclusive fields from both document types simultaneously.
- Initiate two or more related queries, confirm that historical context is retained correctly and no data confusion occurs.
- Upload a detection file larger than the preset size, confirm that the corresponding file size limit prompt is triggered.
- Check that
variablesare bound to exclusive business document identifiers for photovoltaic cell manufacturing, such as process cards and device parameters.
The values provided are common starting points and should be measured against the reader's own samples.