What this category of data looks like
This category of data primarily comes from customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometers, and barcode detectors. 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. Data is updated per order, per material batch, or each time printing plates are replaced. Documents contain fields such as batch identifiers, process parameters, inspection results, and substrate specifications, with no unified fixed format, adjusted according to order requirements.
What constraints do these characteristics impose on the "document parsing and chunking" link
Data in this category is multi-source and has inconsistent formats. Document formats exported from different systems vary significantly, and update frequency is extremely high. The volume of documents parsed in a single task changes with order scale. Documents also contain multiple types of content including text, color value parameters, inspection values, and packaging structure identifiers, requiring accurate distinction of the business meaning of different fields. Metadata such as batches and plate numbers associated with the data directly affects the accuracy of equipment inspection and maintenance. If metadata associations are lost during chunking, search results will fail to match actual production scenarios. Therefore, the document parsing and chunking link must adapt to multi-format input while ensuring the binding relationship between metadata and text blocks.
How to set the configurations
These configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text density of documents such as printing process sheets and imposition sheets, avoiding single chunks containing cross-plate content |
chunkSplitter | By paragraph + semantic boundary | Adapts to the structure of multiple types of documents, preserving the semantic integrity of ink color matching records and die-cutting records |
customPdfParse | Enable table and image OCR parsing | Adapts to documents containing tables and images such as pre-press files and first article approval samples, extracting color values and inspection results |
UPLOAD_FILE_MAX_SIZE | 3000 MB | Adapts to the upload requirements of large pre-press files and batch order documents, avoiding parsing failures for large files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of complex multi-page documents, avoiding timeout interruptions |
chunkSettingMode | Custom mode | Supports adjusting chunking rules for different document types, adapting to the differences of multi-format documents in this category |
Three common mistakes
- Symptom: When attempting to upload Excel-format ink batch records or imposition sheets, the interface prompts that the file type is not supported. Cause: Parsing adaptation for non-PDF documents is not enabled, or corresponding format parsing rules are not configured.
- Symptom: When uploading a large pre-press PDF file, the parsing process triggers a timeout or format error. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, exceeding the system default limits. - Symptom: After retrieving matched text blocks, associated metadata such as batch numbers and printing plate numbers cannot be obtained. Cause: Document metadata fields are not bound during the chunking link, or metadata return rules are not configured in the retrieval logic.
How to confirm the configuration is correct
- Upload a typical production document of this category, check whether the parsed text blocks completely retain business-related parameter and identification information.
- Attempt to upload test documents in multiple formats, confirm that the parsing process can normally handle non-PDF business files.
- Conduct targeted retrieval tests, verify whether the search results include associated metadata of the corresponding document.
- Upload a large document matching the daily production scale of this category, confirm that the parsing process does not experience interruptions or errors.
The values provided are common starting points and should be measured against the reader's own samples.