What data for this category looks like
Typical subjects in this category are flow, pressure, and temperature instrument manufacturers and metrology laboratories. Daily documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version logs. Data sources cover design documents, standard instrument monitoring data, calibration software, laboratory information systems, and customer range requirements. The update frequency is once per instrument per calibration. Documents contain structured tables, metrology values, traceability numbers, and visualized error curves. Most fields are accompanied by dedicated units.
What constraints do these characteristics impose on document parsing and chunking
Documents contain structured tables and visualized error curves. The parsing link must accurately split tables and retain image-text associations. Update frequency is high, and batch import scenarios are common. The system must support fast parsing and batch processing logic. Fields include specific measurement units and traceability numbers. Chunking must retain contextual associations to avoid breaking data traceability logic. Additionally, the system must support multiple document formats including PDF, Excel, and DOC. Parsing rules must be adapted for each format to avoid losing sheet pages or original images.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to short fields with measurement units and traceability numbers, as well as long calibration procedure text, to avoid splitting that breaks contextual associations of error curve descriptions |
chunkSplitter | Separate by paragraph + table header | Adapts to structured segmentation of original calibration records, and retains sheet name associations for Excel tables |
customPdfParse | Enabled | Preserves original image content in PDF-format calibration certificates and error curves, avoiding image loss after OCR parsing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to batch import of Excel original records containing 100,000 entries, avoiding parsing timeout interruptions |
chunkSettingMode | Customize by document type | Adjust chunking rules separately for metrology certificates and error curve PDFs, adapting to different document structures |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch import of multiple calibration record documents, avoiding upload failure due to file size limits |
Three common mistakes
- Phenomenon: After uploading a PDF-format error curve document, the parsing result only contains OCR-recognized text, with no original error curve images. Cause: The
customPdfParseconfiguration is not enabled. The default parsing mode strips images and only retains text content. - Phenomenon: After importing Excel-format original calibration records, sheet page names cannot be read, and only data from the first sheet is loaded. Cause: The
chunkSplitteris not configured to separate by table header, and the sheet name association parsing rule is not enabled. - Phenomenon: After importing a DOC-format calibration procedure and enabling PDF enhancement, the parsing result does not change. Cause: The PDF enhancement configuration only applies to PDF-format documents, and does not adapt to the parsing logic for DOC documents.
How to confirm the configuration is properly set
- Upload a single PDF-format error curve document, and check whether the parsing result includes an image preview.
- Upload an Excel original calibration record with multiple sheet pages, and check whether the parsing result displays the names of the corresponding sheet pages.
- Upload a DOC-format calibration procedure document, and confirm whether the parsing logic adapts to the document structure after PDF enhancement is enabled.
- Import an Excel document containing 100,000 entries, and check whether the parsing process does not trigger a timeout error.
The values provided are common starting points and should be evaluated against the reader's own samples.
This document is written based on FastGPT v4.17.0.