What data looks like for this category
Data related to cosmetics manufacturing equipment inspection and maintenance comes from multiple sources: registered and filed formulas, raw material safety documents, weighing and emulsification equipment operation logs, ERP/MES system records, laboratory test reports, and packaging material version documents. Data updates trigger with each completed production batch and each production line changeover. Document types include formula process sheets, weighing and feeding records, emulsification or filling records, site cleaning records, semi-finished and finished product test reports, and label review documents. Most documents use multi-column tables and inspection forms with embedded images. Fields include restricted substance content, emulsification temperature parameters, production batch numbers, packaging material version numbers, and other relevant details. Some documents require fixed formats to meet filing requirements.
What constraints these characteristics impose on document parsing and chunking
High-frequency updates tied to line changeovers require document parsing to support fast batch processing, to avoid delays that disrupt production line synchronization. Multi-column feeding records and equipment inspection table structures require parsing to preserve column alignment and field associations, to prevent splitting critical business fields during chunking. Documents with embedded on-site equipment photos require extracting both text and image content, rather than only plain text. Fixed format fields required by filing rules require chunking to maintain the link between fields and their corresponding business processes. Breaking this link will reduce the accuracy of subsequent equipment inspection and maintenance Q&A matching. Multiple document types from dispersed sources require unified parsing formats to ensure consistent context for subsequent Q&A.
How to set up the configuration
This configuration is based on FastGPT v4.17.0. Specific parameter settings are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Cosmetics manufacturing documents include long process descriptions and multi-column tables. This range avoids splitting critical business fields and complete table units |
customPdfParse | Enabled | Adapts to PDF documents with multi-column feeding tables and embedded equipment inspection images, preserves formatting and image content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for image recognition and table parsing when batch processing multiple document files with images after line changeovers |
chunkSplitter | Chunk by title + paragraph | Documents have clear process node and equipment section titles. Chunking by title preserves business context within each chunk |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading and parsing large PDF documents with high-resolution on-site equipment photos |
Three common errors
- Symptom: After uploading a PDF containing equipment inspection images, the parsed result only displays text content, with all images missing. Cause: Image recognition configuration for
customPdfParseis not enabled, so embedded image data is automatically discarded during parsing. - Symptom: After parsing multi-column weighing and feeding records, column data is disorganized and misaligned, and cannot match the raw document's raw material and feeding quantity fields. Cause: Multi-column table adaptation mode for custom PDF parsing is not enabled. The default chunking logic splits the column structure.
- Symptom: Some tasks trigger timeout errors when batch uploading multiple document files after a line changeover. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a duration suitable for large document files with images, so parsing terminates before completion.
How to confirm the configuration is correct
- Upload a test PDF containing multi-column feeding records and equipment inspection images, and check if the parsed result preserves column alignment and embeds images normally within the document content.
- Review background logs for parsing tasks, and confirm there are no timeout error records to verify that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration takes effect. - Randomly sample parsed chunk content, and verify that critical business fields such as restricted substance content, emulsification temperature parameters, and production batch numbers are not split.
- Upload a test PDF document with a size close to 1000 MB, and confirm that the parsing task completes normally with no file truncation or upload failure prompts.
The values listed above are common starting points, and should be evaluated against the user's own samples.