What the data for this category looks like
The main entities in this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily generated documents include formula and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, oven or frying line equipment, laboratory test results, and food traceability ledgers. Documents update based on production batches, shifts, or daily schedules. Fields include allergen labels, feeding weight, baking temperature and duration, packaging date, and more. Some documents have mixed-format structural features.
What constraints these characteristics impose on document parsing and chunking
This category has frequent production line changes, with large differences in document structure across single batches. Chunking logic must adapt to documents in different formats. Accurate extraction of sensitive fields such as allergens and feeding quantities requires that chunking does not split associated data. Frequently updated documents need fast parsing to avoid delaying production ledger summary timelines. When multiple document types are uploaded together, distinguish between tabular baking curves and form-based production records. Unified chunking risks information loss without this distinction. Products sold directly to consumers require accurate parsed data to prevent ledger data deviations from chunking errors.
How to configure settings
The following configurations are formulated for FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to both long-text baking curve documents and short-form feeding lists and production records for this category. Avoids context breaks from overly small chunks or semantic confusion from overly large chunks. |
chunkSplitter | Split by paragraph + table | This category of documents contains many structured tables and segmented text. This chunking rule preserves table integrity and paragraph semantic coherence. |
customPdfParse | Enabled | Native PDF parsing often misaligns complex tables such as baking temperature-time curves for this category. Custom parsing services improve table recognition results. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing bulk production record files or large baking curve documents takes significant time. This duration covers parsing needs for most scenarios. |
chunkSettingMode | Manual mode | Chunking parameters need adjustment for different document types. Manual mode allows flexible adaptation to this category's mixed-format document characteristics. |
Three common configuration errors
- Table misalignment and incorrect field display after uploading a PDF. This happens when
customPdfParseis not enabled, and the native parsing service cannot correctly recognize this category's complex table structures. - File upload parsing failure after API integration, returning a timeout status code. This happens when
PARSE_FILE_TIMEOUT_SECONDSis set too short to complete parsing of large production record documents. - Context breaks after chunking, with associated fields split into different chunks. This happens when
chunkSizeis set too small, breaking the semantic integrity of associated data such as formulas and feeding quantities.
How to confirm the configuration is correct
- Upload a test PDF containing a baking temperature curve table, and check if parsed chunk results retain the complete table structure and data.
- Enter the system settings page, confirm that
customPdfParseis enabled, and that the value ofPARSE_FILE_TIMEOUT_SECONDSmatches actual parsing needs. - Upload a batch production record file of approximately 100 MB, confirm that the upload and parsing process has no errors, and that generated chunk data fully covers the document content.
- Adjust
chunkSizeto different ranges, test chunking effects for short-form and long-text documents, and confirm that chunking logic adapts to document types.
The values provided are common starting points and should be measured against the reader's own samples.