What Data for This Category Looks Like
Typical data for this category comes from raw fiber inspection results, ERP systems, spinning and loom operation logs, evenness testers, fabric inspection machine test results, and order specification documents. Daily documents include cotton blending lists, process setup sheets, equipment operation records, yarn and grey fabric inspection sheets, defect ledgers, and more. Most have structured fields such as fiber length (mm), spinning twist (twists per meter), defect grade, and lot number. Data update frequency shifts with raw material batches, production shifts, or fabric lot numbers. The content of individual documents is generated incrementally across the production workflow.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
The high-frequency updates and structured document traits of this category create multiple constraints for the chunking process. First, large batches of bulk inspection records or equipment logs may have cross-batch, cross-lot content split by default chunking parameters, breaking the associativity required for traceability. Second, blending lists and inspection sheets that include tables will have the corresponding relationships between cell indicators broken if split using generic character-based chunking. Third, heterogeneous documents from multiple sources — such as PDF process sheets and online spreadsheet ledgers — need adapted parsing rules to avoid losing format details. This category has a high number of equipment heads. Defect records are mostly continuous batch data. Chunking must avoid cross-batch splitting to enable quick location of issues for a single lot or batch during traceability.
How to Configure
These configurations are based on FastGPT v4.17.0, adapted to the document parsing and chunking needs of the spinning and weaving category.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the single-page content length of single inspection records and process sheets for this category, avoiding splitting complete table rows or inspection entries |
chunkSplitter | markdown | Preserves the structural associativity of tables and headings in documents for this category, adapting to formatted PDF and online spreadsheet content |
customPdfParse | Enabled | Filters out irrelevant content such as headers and footers from process sheets and inspection sheets, retaining only valid production-related fields |
UPLOAD_FILE_MAX_SIZE | 20 MB | Matches the common file size of single-batch inspection records and equipment logs for this category, avoiding parsing timeouts from overly large files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for parsing large batches of documents, adapting to the loading and chunking process for multi-source heterogeneous files |
chunkSettingMode | Manual | Adjusts chunking parameters separately for different document types such as cotton blending lists and defect ledgers, to match business associativity logic |
Three Common Mistakes
- Phenomenon: After uploading a bulk defect record file larger than 10 MB, over 1000 chunks are generated, with some chunks empty or failing vectorization. Cause: The
chunkSizeparameter was not adjusted, and the default chunking rules split long tables into too many small fragments, causing loss of associative information during vectorization. - Phenomenon: After importing blending list data from Feishu online spreadsheets, the parsed results lose the unit information corresponding to fiber indicators. Cause: Table-adapted chunking rules were not enabled, and direct character-based chunking broke the corresponding relationship between cells, separating fields from their units.
- Phenomenon: When creating a knowledge base based on uploaded inspection records, the exported dataset.csv cannot load the original chunked content. Cause: Unique field mapping for this category was not retained, and a generic template was used directly for export, causing the question-and-answer format to mismatch the original chunked content.
How to Verify Proper Configuration
- Upload a single grey fabric inspection record PDF under 10 MB, check the number of generated chunks and content integrity, confirm each chunk contains complete inspection entries and corresponding lot numbers.
- After enabling
customPdfParse, upload a process setup sheet with tables, confirm the parsed content retains the table row and column structure and parameter corresponding relationships. - Import a blending list exported from Feishu online spreadsheets, verify that the parsed content includes complete fields such as fiber length and blending ratio, along with their corresponding units.
- Export the current knowledge base's dataset.csv, confirm the fields include unique unit annotations for this category, such as mm, twists per meter, and others.
The values provided are common starting points and should be measured against the reader's own samples.