What the data for this category looks like
Core data for textile manufacturing intelligent due diligence comes from factory production ledgers, supply chain procurement contracts, quality inspection reports, customs declarations, and industry capacity analysis documents. Updates follow a monthly or quarterly rhythm, matching production monthly reports and quarterly capacity review reports.
Document structures include structured tables such as material BOM tables and capacity scheduling tables, long-text production process descriptions, and scattered quality inspection documents and customs declaration forms.
Fields and units include yarn count (count), gram weight (grams per square meter), loom speed (rotations per minute), order delivery date (calendar days), plus identifying fields like material codes and production batch numbers.
Constraints on the Document Parsing and Chunking Workflow
Structured tables in textile manufacturing due diligence documents often have cross-page merged cells and multi-level headers. The parsing workflow must accurately identify table hierarchy and cross-page content to avoid incorrect splitting.
Diverse units within documents require post-parsing unified unit mapping, to prevent unit loss or confusion during chunking.
Long process description paragraphs are lengthy. Fixed-length chunking easily breaks process logic, so adaptation to the natural paragraph structure of documents is required.
Incrementally updated production data requires parsing to support identifying new content by batch number, avoiding repeated processing of historical documents.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Textile due diligence documents contain numerous BOM tables and quality inspection forms, requiring retention of cell hierarchy and cross-page content |
PARSE_OCR_DPI | 300–400 DPI | Textile documents contain small text such as batch numbers and yarn counts; low DPI causes recognition errors |
CHUNK_SIZE | 800–1000 characters | Textile process documents have many long paragraphs; adapting to natural paragraph length avoids breaking process logic |
PARSE_INCREMENTAL_ENABLE | Enabled | Textile production data is updated monthly; incremental parsing avoids repeated processing of historical batch documents |
MAX_CHUNK_OVERLAP | 100–150 characters | Chunks spanning process nodes need to retain contextual connections to prevent critical information from breaking |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large annual capacity reports and multi-page customs declarations take longer to parse; prevents timeout interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Parsed documents lack fields such as textile material batch numbers and yarn counts. Cause: The
PARSE_TABLE_ENABLEparameter is not enabled, resulting in incomplete extraction of hidden fields in structured tables. - Phenomenon: Uploaded textile due diligence PDFs cannot be selected in the dataset list. Cause: File format is not set to supported types
pdf,xlsx,docx, or file size exceeds theUPLOAD_FILE_MAX_SIZElimit. - Phenomenon: Chunking results split complete process descriptions into unrelated paragraphs. Cause: The default fixed-length chunking strategy is used, and the
CHUNK_SIZEparameter is not adjusted based on the long paragraph structure of textile documents.
How to Verify Correct Configuration
- Upload a test document containing BOM tables and process descriptions, check if parsed text retains fields such as material codes and yarn counts, to confirm the table parsing parameter is active.
- Enter the dataset management interface, upload the test file, and check the upload status to confirm no file size or format related error prompts are triggered.
- View the chunked result list, confirm that chunks of long process documents do not break at critical process nodes, and manually verify the contextual coherence of chunked content.
- Trigger an incremental parsing task, upload a document with a new batch, confirm only newly added content is parsed, and historical documents are not processed repeatedly.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.