What the data for this category looks like
White goods marketing content data primarily comes from official brand product manuals, quarterly promotional plan collections, dealer training materials, online promotion copy libraries, and after-sales FAQ documents. The update rhythm adjusts with new product launches and promotional cycles, typically once per quarter or new product cycle. Document structures are mostly mixed format, including structured parameter tables, image-text descriptions, activity rules, and other content. Fields cover product models, cooling/heating power, energy efficiency ratings, installation dimensions, promotional validity periods, and more. Units are mostly watts (W), millimeters (mm), energy efficiency rating numbers, and similar units.
What constraints do these characteristics impose on document parsing and chunking
Mixed text, images, and structured tables require parsing to retain the association between text and corresponding images or tables. This avoids chunking that breaks the binding between parameters and their units. Multi-field, multi-unit parameter tables require parsing tools to fully preserve row and column correspondence, and avoid splitting individual cells arbitrarily. Content updated with new product and promotional cycles requires chunking logic to adapt to thematic paragraph boundaries, rather than fixed page-by-page splitting. Large long document collections require parsing timeout configurations to accommodate extended processing durations, preventing premature termination of parsing.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | White goods marketing documents contain both long parameter table paragraphs and short promotional copy. This length balances the integrity of both types of content |
chunkOverlap | 100–150 characters | Prevents chunking from breaking the contextual association between parameters and their units in parameter tables, and improves content coherence during retrieval |
enableImageParse | Enabled | White goods marketing documents often include product photos and promotional posters. This setting extracts image alt text and embedded descriptions |
parseExcelTableMode | fullTable | Product parameter tables require full retention of the correspondence between fields and units. Splitting tables will lose associated information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates the parsing duration of large promotional plan collections, to avoid parsing termination due to timeout |
enableMetadataExtract | Enabled, specify productModel, promotionPeriod | Marketing content needs to associate product models and activity cycles as retrieval metadata, to improve result accuracy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After uploading a docx-format product parameter document, some parameter unit information is missing in the retrieval results. Cause: The full table mode of
parseExcelTableModeis not enabled, or chunking breaks the contextual association between parameters and their units. - Phenomenon: After uploading a PDF document containing product photos, no descriptive text corresponding to the images is present in the parsing results. Cause: The
enableImageParseconfiguration item is not enabled, so embedded descriptive text for images is not extracted. - Phenomenon: After configuring a custom document parsing tool, the parsed product parameter tables have misaligned fields. Cause: The table recognition rules of the custom parsing tool are not adjusted for the multi-table structure of white goods marketing documents.
How to confirm the configuration is correct
- Upload a test document containing a complete product parameter table, and check if the parsed chunks retain the association between parameters and their corresponding units.
- Upload a promotional plan document containing product photos, and check if the parsing results include image alt text or embedded descriptive text.
- After enabling the metadata extraction configuration, view the parsed document metadata list to confirm that the preset product model and activity period fields are included.
- Adjust the
chunkSizeparameter, compare chunking results across different values, and confirm that no critical product parameter paragraphs or promotional copy are cut off during chunking.
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.