What Data for This Category Looks Like
This category covers enterprises engaged in color printing, label, carton, flexible packaging, and pharmaceutical packaging material processing. Daily generated documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first article approval samples, barcode inspection reports, die-cutting and lamination records, and more. Data sources include customer manuscripts, color management systems, printing press operation data, ink and substrate batch ledgers, spectrometer and barcode detector output results. Updates occur per order, per batch, or each time a printing plate is replaced. Documents contain structured tables and mixed text and image content, with fields such as edition number, color value, barcode number, die-cutting parameters, and batch number. Some fields include physical units like grams per square meter and millimeters.
What Constraints These Characteristics Impose on the Document Parsing and Chunking Link
High-frequency order-level documents require parsing processes to adapt to small-batch, multi-batch rapid processing, to avoid delays that disrupt production schedules. Mixed text and image first article approval samples and imposition sheets require parsing tools to recognize both text and image information, ensuring visual fields such as barcodes and color values are not lost. Coexisting structured and unstructured documents from multiple sources require flexible chunking rules that adapt to different document types, preserving contextual integrity for strongly associated fields like edition number and batch number. Errors can lead to full batch product scrapping, so parsing accuracy requirements are high, and key information must not be split or omitted.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the paragraph length of printing documents, avoid splitting associated fields such as edition number and ink ratio |
chunkSplitter | By paragraph + semantic boundary | Match the segmentation logic of process sheets and imposition sheets, retain complete parameter information within the same module |
customPdfParse | Enabled | Adapt to mixed text and image PDFs of first article approval samples, extract barcodes, color values and corresponding text descriptions from images |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Handle multi-document parsing tasks for batch orders, avoid timeout interrupts during frequent plate changes |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodate large imposition files and collections of multi-batch ink records |
chunkSettingMode | Manual configuration | Adjust chunking rules to adapt to field associations for different document types such as process sheets and barcode inspection reports |
Three Common Misconfigurations
- Failing to enable the
customPdfParseconfiguration causes image-based fields in PDF-format first article approval samples and barcode inspection reports to go unrecognized, with some documents returning empty parsing results. - Setting
PARSE_FILE_TIMEOUT_SECONDSto less than 120 seconds triggers timeout errors when batch uploading multiple documents from the same order, resulting in workflow parsing failures. - Setting
chunkSizeto over 2000 characters splits edition number information and corresponding die-cutting parameters in imposition sheets into different chunks, preventing associated retrieval.
How to Verify Correct Configuration
- Upload a typical imposition sheet PDF, check that the parsing result fully includes core fields such as edition number and ink ratio, with no splitting or omission.
- Batch upload 10 documents of the same order type, confirm that no timeout errors occur during parsing and all documents complete parsing.
- Access the FastGPT v4.17.0 configuration interface, confirm that the
customPdfParseswitch is enabled. - Adjust
chunkSizeto the 800–1200 character range, verify that chunked text retains the contextual association between edition numbers and corresponding process parameters.
The values provided are common starting points and should be measured against the reader's own samples.