What Data for This Category Looks Like
This category’s typical data comes from bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, factory inspection reports, and more. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, ovens or frying lines, laboratories, and food traceability ledgers. Update frequency is per batch, per shift, or daily. Document structures vary widely. There are plain text feeding records, as well as PDFs with charts. Fields include allergen labels, baking temperatures, shelf lives, batch numbers, and more. Some documents require linking multiple data dimensions.
What Constraints These Characteristics Create for Document Parsing and Chunking
This category has many document types, fast update cycles, and dense associated fields. These create multiple constraints for the chunking process. Multi-format documents require the parsing workflow to support plain text, PDFs with charts, and other formats. This avoids missing critical data. Frequent product line changes require flexible adjustment of chunking rules for different batches of documents. This prevents splitting associated information from the same batch. The integrity of sensitive fields such as allergens and foreign matter control directly affects audit results. Chunking must not split such associated content. Doing so increases review time and error risk.
How to Configure Settings
The following configurations are based on the parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of single documents in this category. Using characters as the unit avoids format deviations caused by token counting, and prevents splitting associated fields such as allergens and baking curves |
chunkSplitter | Separate by paragraphs + page headers and footers | Matches the structured layout of production records and formula sheets, retains key context such as batch numbers and dates, and adapts to batch processing of documents with frequent line changes |
customPdfParse | Enable table recognition + OCR mode | Adapts to chart-including documents such as baking curves and metal detection records, extracts critical information such as temperatures and detection values from images, and enables simultaneous recall of text and image content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing duration of single batch production records, and avoids timeout failures when uploading multiple batches of documents |
chunkSettingMode | Manual configuration | For scenarios with frequent line changes, adjust chunking rules for different document types, and adapt to documents of different formats such as formula sheets and production records |
Three Common Mistakes
- Phenomenon: Allergen fields and corresponding feeding list content are split into different chunks in the chunking result. Cause: The
chunkSizesetting is too small, and the contextual integrity of associated fields is not preserved. - Phenomenon: After uploading a baking curve PDF, the OCR recognition result is empty. Cause: The OCR mode of
customPdfParseis not enabled, so key data such as temperatures and times in images cannot be extracted. - Phenomenon: Parsing tasks time out when uploading daily production records in batches. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the actual required parsing duration, and does not adapt to batch processing of multiple batches of documents.
How to Verify the Configuration Is Correct
- Upload a formula sheet with allergen labels, and check that allergen entries and corresponding feeding quantities are in the same chunk in the chunking result.
- Upload a baking curve PDF, and confirm that the parsing result includes temperature and time values from the chart.
- Upload a single production record document under 10MB, and check that the parsing task completes within 600 seconds.
- Switch between different document types such as cleaning and disinfection records and factory inspection reports, and confirm that the chunking rules adapt to different layout structures.
The values provided are common starting points and should be measured against the reader's own samples.