What data for this category looks like
Personal care product research data comes primarily from national regulatory agency filing documents, brand official technical white papers, e-commerce platform product detail pages, and third-party ingredient test reports. Updates trigger irregularly alongside new product launches and compliance policy adjustments, with no fixed cycle. Document types include structured ingredient tables, long-text usage instructions, compliance statements, and compliance filing attachments. Fields cover ingredient names, concentration percentages, single-use amounts, shelf lives, filing numbers, and more. Units include percentages, milliliters, grams, months, and others.
What constraints do these characteristics impose on document parsing and chunking?
Structured ingredient tables are common, so cell association relationships must be accurately identified to avoid splitting ingredient descriptions that span multiple cells. Long-text usage instructions contain repeated scenario descriptions, so chunking must retain contextual logical chains. Fields with precise units for concentration and usage amounts must avoid breaking the binding between numerical values and their units. Compliance filing documents have strict paragraph structures, so chunking must not disrupt the complete semantics of individual clauses.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Personal care documents include long-text ingredient descriptions and table fragments. This range balances semantic completeness and recall density |
chunk_overlap | 100–150 characters | Retains ingredient association information across segments, avoids splitting continuous usage scenario descriptions |
parse_table_format | markdown_table | Accurately extracts structured ingredient tables, retains cell hierarchy relationships |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts parsing duration for multi-page filing documents and long-text technical white papers |
enable_unit_aware_split | Enabled | Avoids breaking the binding between numerical values and their units for concentration and usage amount fields |
max_table_cell_length | 2000 characters | Accommodates long-text ingredient description cells, avoids premature content truncation |
The parameter values provided on this page are common starting points for configuration setup. 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 errors
- Phenomenon: After uploading a filing document, the parsing node displays a timeout status, and the log returns an
ETIMEDOUTerror. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default value is insufficient to handle parsing for multi-page compliance documents. - Phenomenon: Only the first 3 rows of data are captured for ingredient tables, with subsequent row content missing. Cause: The
max_table_rowparameter was not set, or the default value is too low, and the table row limit was not increased. - Phenomenon: In parsed segments, concentration numerical values and percentage units are split into separate segments. Cause: The
enable_unit_aware_splitconfiguration was not enabled. The default chunking logic does not bind numerical values and their associated units.
How to confirm configurations are correctly set
- Upload a personal care product filing document with a complete ingredient table, and check if all table rows and cell content are fully preserved in the parsed text.
- Select a long-text segment containing concentration numerical values and their units, and verify that the chunking result retains the binding between the numerical value and its unit.
- Upload a brand technical white paper with more than 10 pages, and confirm that no timeout error is triggered for the parsing task.
- View the system parameter configuration page, and confirm that
enable_unit_aware_splitis enabled.
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.