What the Data for This Category Looks Like
The main entities in this category are color printing, label, carton, flexible packaging, and pharmaceutical packaging processing enterprises. Daily production ledgers include documents such as pre-press files, imposition sheets, printing process sheets, ink color matching records, first-piece sample approvals, barcode detection records, die-cutting and lamination records. Data sources include customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrophotometer test results, barcode detector readings, and more. Data updates occur per order, per production batch, or each time a printing plate is replaced. Documents include dedicated fields such as plate version, batch number, color parameters, substrate grammage, barcode number, and more. Some documents also include scanned images and test values.
Constraints on Document Parsing and Chunking
The high-frequency updates and multi-type document characteristics of this category require document parsing to support batch task processing and multi-format compatibility. The presence of dedicated fields and image-based documents requires chunking logic to retain field associations, and avoid splitting critical batch and plate version information. Image-based documents such as scanned first-piece sample approvals and barcode detection records require parsing tools to support image text extraction. Core test data will be lost without this support. Additionally, the business characteristic that printing plate errors can lead to full batch scrap requires parsing results to accurately match orders with corresponding ledger content, and avoid mixing content across orders.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Ledger documents for this category include complete process parameters and batch information for a single batch. This chunk length supports full information retention for single order ledgers, and avoids splitting critical fields |
chunkSplitter | Split by header/footer + batch number | Daily ledger documents generate headers with plate version and batch numbers per order or plate change. Splitting by this rule accurately distinguishes ledger content from different orders |
customPdfParse | Enabled | This category includes image-based PDF documents such as scanned first-piece sample approvals and barcode detection records. Enabling custom parsing extracts text and barcode information from images |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | When uploading multiple process sheets and imposition sheets in batches, parsing time increases with the number of files. This interval covers the parsing needs of conventional batch tasks |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Pre-press files for this category may include high-definition image materials. Large file upload support is required to fully parse all content |
chunkSettingMode | Manual configuration | Document structures for this category vary widely. Manual configuration adapts to different types of ledger documents, and optimizes chunking accuracy |
Three Common Configuration Mistakes
- After uploading ledger files for multiple orders, chunking results include spliced content across orders. This occurs when
chunkSplitteris not configured to split by order batch number. The default chunking logic splits by character length, and does not distinguish boundaries between different documents. - Knowledge base search tests fail to return results for scanned first-piece sample documents. This happens when
customPdfParseis not enabled. Text and barcode information from scanned images is not extracted, leading to no matching search results. - Parsing timeout errors occur when uploading large pre-press files in batches. This is because the
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than actual parsing time, and does not cover time requirements for multi-file batch processing.
How to Verify Proper Configuration
Under FastGPT v4.17.0, configuration effects can be verified via the following actions:
- Upload a single ledger file that includes plate version and batch number. Check whether chunking results split along order boundaries, with no spliced content across orders.
- Upload a scanned first-piece sample file. Confirm that parsing results include text and barcode information from the image, with no content loss.
- Upload 3 to 5 files from different orders in batches. Check whether parsing tasks complete within the interval configured for
PARSE_FILE_TIMEOUT_SECONDS, with no parsing timeout errors. - Adjust the
chunkSizeconfiguration value. Compare chunking results across different configurations, and confirm that the chunk length supports full information retention for single batch ledgers.
The values provided here are common starting points, and should be evaluated against the reader's own samples.