What the data for this use case looks like
Data sources include silicon wafer incoming material records, production MES system logs, operation logs of diffusion furnaces, coating equipment, and printing presses, EL defect detection images, IV electrical performance test data, and laboratory yield analysis reports. Updates follow the rhythm of generating summary reports per silicon wafer batch, per individual wafer test result, and daily. Daily documents include structured process parameter tables, time-series operation curves, annotated detection images, and text description reports. Fields cover process temperature, deposition rate, defect coordinates, conversion efficiency, and other metrics, with units including degrees Celsius, seconds, watts per square centimeter, and others.
What constraints do these characteristics impose on the document parsing and chunking link?
High-frequency wafer-level data requires the parsing process to have low latency, to avoid impacting real-time traceability requirements. The mixed multi-type document scenario requires the chunking logic to balance structured parameters and unstructured image annotations, without breaking data associations. Precise process parameter fields require chunking to retain context, avoiding splitting continuous parameter groups for the same process window. Large-volume files from batch detection data require the parsing module to support segmented processing, to prevent timeout interruptions.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of parameter sections in photovoltaic cell manufacturing documents, avoiding splitting paragraphs containing continuous process parameters |
chunkSplitter | Chunk by paragraph + semantic relevance | Retains the contextual association between parameters and corresponding results in process cards and test reports, improving traceability accuracy |
customPdfParse | Enable OCR enhancement mode | Adapts to text extraction requirements for scanned EL test reports and paper process cards |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Meets the parsing time requirements for batch EL detection images and multiple sets of device logs, avoiding timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports packaged uploads of multiple batches of detection data, adapting to daily enterprise data scale |
Three common errors
- API parsing succeeds but no results are returned. This is caused by incorrectly configuring the callback parameters for
customPdfParse, or settingPARSE_FILE_TIMEOUT_SECONDStoo short, resulting in a return before parsing finishes. - Logs prompt a split error. This is caused by setting
chunkSizeto less than 300 characters, splitting paragraphs containing continuous process parameters, leading to semantic fragmentation in chunks that cannot be associated. - Document parsing speed is too slow. This is caused by not enabling the batch parsing switch for
customPdfParse, or uploading files exceeding theUPLOAD_FILE_MAX_SIZElimit, leading to segmented retries.
How to confirm the configuration is properly set
- Upload a single diffusion process card PDF, check if the parsed chunks retain complete temperature and time parameter groups without semantic fragmentation.
- Call the FastGPT v4.17.0 file parsing API, confirm that the returned results include parsed text fragments with no empty fields.
- Upload a packaged file of batch EL detection images, check if parsing time stays within the preset range of
PARSE_FILE_TIMEOUT_SECONDS. - View system logs, confirm there are no chunking errors related to
chunkSplitter, and chunk lengths fall within thechunkSizesetting range.
The values provided are common starting points and should be validated against applicable sample data.