What the data for this category looks like
Daily processed documents for this category include process sheets, layout plans, cutting orders, and packaging and size matching lists. Data comes from orders and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector records, and customer sample seals. Data updates progress with each order and production batch. Single-order documents include fields such as cut piece dimensions, fabric direction, dye batch number, and packaging ratio. Units include specific measurement items such as centimeters, pieces, and yards.
What constraints these characteristics impose on the document parsing and chunking link
A high share of multi-column structured documents exist for this category, and formats vary across sources such as CAD patterns and process sheets. The parsing link must accurately identify column structures and field associations. Single-order data updates frequently with production batches. Chunk sizes must adapt to small-batch production scenarios to avoid losing key associated information such as cut piece dimensions and fabric direction after splitting. Field units and measurement rules differ across documents. Chunking must retain context associations to enable matching size and dye batch requirements for corresponding orders during subsequent cross-review. Some CAD-exported files have cross-page table structures. The parsing link must support non-standard document layouts.
How to set the configurations
The following configurations are based on FastGPT v4.17.0, and are tailored to the document characteristics of the apparel cutting and sewing category:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the multi-column table and short-field document structure of this category, avoiding splitting associated fields such as cut piece dimensions and fabric direction |
chunkSplitter | Split by paragraph and table boundaries | Adapts to structured tables and process descriptions in process sheets and layout plans, retaining field context associations |
customPdfParse | Enable multi-column table parsing mode | Accurately identifies cross-page tables and cross-column fields for multi-column layout plans and cutting orders exported from CAD |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Adapts to the parsing duration of bulk order documents, avoiding parsing timeouts for complex CAD drawing files |
chunkSettingMode | Manual configuration mode | Adjust chunking rules for different document styles, adapting to seasonal style differences |
Three common misconfigurations
- Symptom: Column misalignment and missing fields appear in the parsing result after uploading a multi-column layout plan. Cause: The multi-column table parsing mode of
customPdfParseis not enabled, and the default parsing logic cannot identify cross-column cut piece dimension data. - Symptom: The association between fabric direction and dye batch number is lost in the parsed document chunks. Cause: The
chunkSizesetting is too small, splitting associated fields of the same process into different chunks and destroying context integrity. - Symptom: Parsing tasks time out and fail after uploading large-volume CAD pattern files. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the actual required parsing duration, not adapting to the processing pace of complex drawing files.
How to confirm the configurations are correct
- Upload a typical multi-column layout plan document, check whether the column structure in the parsing result matches the original document.
- Split a process sheet document, confirm whether associated fields such as cut piece dimensions and fabric direction are in the same chunk.
- Upload a CAD pattern file under 100MB, confirm that the parsing task completes within 180 seconds with no timeout errors.
- Switch to documents of different styles, verify that the manual configuration of
chunkSettingModetakes effect, and that chunking rules adjust with document type.
The values provided are common starting points and should be measured against the reader's own samples.