What the data for this category looks like
The main entities in this category are skincare, haircare, makeup, and special cosmetics manufacturers. Daily generated documents include formula work instructions, weighing and batching records, emulsification or filling records, cleaning and sanitization records, semi-finished and finished product inspection reports, and label review documents. Data sources include registered or filed formulas, raw material safety materials, production equipment logs, ERP/MES systems, laboratory test results, and packaging material version documents. Data updates are triggered with each production batch and each line change. Documents contain precise numerical fields such as raw material usage, temperature parameters, batch numbers, and restricted substance content. Some documents use structured table formats.
What constraints these characteristics impose on document parsing and chunking
Documents in this category are mostly structured tables and semi-structured text, and are updated frequently with production batches. Chunking must retain complete information within a single batch to avoid mixing data across batches. Document sources include exported files from multiple systems, so parsing must support common formats such as Excel and PDF. Fields include precise numerical values with specific units. After chunking, corresponding metadata such as batch numbers and production times must be associated to ensure complete production information can be traced during retrieval. Frequently updated documents require parsing and chunking workflows to support rapid batch uploads to meet the timeliness requirements of daily audits.
How to set up configurations
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the information density of cosmetics manufacturing documents, avoids splitting complete production records across batches |
chunkSplitter | Split by paragraph + table boundaries | Aligns with the paragraph structure and table fields of formula sheets and inspection records, preserves data integrity |
customPdfParse | Enable the built-in structured table parsing mode or configure a third-party parsing interface | Adapts to structured documents in Excel and PDF formats, supports replacing third-party document parsing tools to accurately extract fields such as raw material usage and test results |
UPLOAD_FILE_MAX_SIZE | 100 MB | Supports uploading multiple batch production records in a single operation, adapts to enterprise batch audit scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Covers parsing time for large formula documents or bulk Excel files, avoids timeout errors |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types, adapts to specialized document formats such as formula sheets and batching records |
Three common mistakes
- Issue: Fields are missing or empty in the parsed result after uploading an Excel document. Cause: The structured table parsing mode for
customPdfParseis not enabled, or no third-party parsing tool interface is configured. The default parsing logic cannot recognize structured table fields in Excel. - Issue: Retrieved text fragments do not have corresponding metadata such as batch numbers and raw material names associated. Cause: Metadata binding configuration is not enabled during chunking, and batch information from the document header is not stored in association with subsequent content.
- Issue: Timeout errors are triggered when parsing large formula documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to complete parsing for complex documents.
How to confirm configurations are correct
- Upload a standard formula work instruction PDF, check that the parsed result retains core fields such as raw material names, usage amounts, and restricted substance content from the table.
- Search for batching records from a specific batch, confirm that returned text fragments include associated metadata such as corresponding batch numbers and production times.
- Upload a bulk production record Excel file larger than 50 MB, confirm that the upload and parsing process does not trigger errors.
- Adjust the
chunkSizeparameter, check that chunked results do not split content across batches, aligning with the audit logic for production documents.
The values provided are common starting points and should be measured against the reader's own samples.