What the data for this category looks like
White goods investment research data primarily comes from quarterly and annual financial reports of listed home appliance companies, market monitoring reports released by industry associations, real-time sales data from offline stores, quotation documents from upstream component suppliers, and official national energy efficiency standard documents. Update frequency: Financial reports are updated quarterly and annually, market monitoring data is updated weekly, and energy efficiency standards are revised 1 to 2 times per year. Document structures include long research reports, structured financial report tables, scattered product parameter documents, and compliance label files. Core fields include product model, energy efficiency rating, annual power consumption (kWh/unit), price range, and supply chain cost proportion. Common units are kilowatt-hours, yuan, and percentage.
What constraints do these characteristics impose on document parsing and chunking
White goods investment research data comes from multiple sources with diverse formats, requiring support for multiple document types including PDF, Excel, and Word. Structured tables often contain merged cells, which increases parsing complexity. Long research reports include product parameter comparisons across paragraphs. Fixed-length chunking may split related information unintentionally. Weekly updated store data includes large numbers of duplicate historical files. Full parsing wastes computing resources. Investment research scenarios require accurate extraction of fixed core fields. Missing fields will reduce subsequent retrieval effectiveness.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | White goods research reports often contain long paragraphs of parameter comparisons. Excessively long chunks split related information, while excessively short chunks increase retrieval overhead |
chunkOverlap | 100–150 characters | Ensures key information such as cross-chunk product models and energy efficiency ratings is not truncated |
PARSE_TABLE_MERGE_CELL | Enabled | Home appliance financial reports and store tables have large numbers of merged cells. Enabling this option restores complete structured data |
ENABLE_INCREMENTAL_PARSE | Triggered by file update time | Store monitoring data updates weekly. Incremental parsing avoids repeated processing of historical files |
REQUIRE_EXTRACT_FIELDS | product model, energy efficiency rating, annual power consumption, price range | Investment research scenarios require fixed extraction of core fields to prevent invalid information from being included in chunks |
PARSE_FILE_TIMEOUT | 600 seconds | Large supply chain documents have significant content, requiring sufficient parsing time |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After uploading a store Excel file with merged cells, the parsing node shows "parsing successful" but some extracted table data is missing. Cause: The
PARSE_TABLE_MERGE_CELLconfiguration is not enabled, so complete structured information for merged cells cannot be restored. - Phenomenon: After batch uploading weekly store data, large numbers of identical chunk contents are generated repeatedly. Cause:
ENABLE_INCREMENTAL_PARSEis not enabled, and all files are processed in full mode without filtering historically parsed documents. - Phenomenon: After submitting a long home appliance research report, parsed chunks truncate cross-paragraph product parameter comparison content. Cause: The value of
maxChunkSizeis too small, failing to accommodate information relevance across paragraphs.
How to confirm configurations are set correctly
- Upload a home appliance store Excel file with merged cells, and check if parsed data fully restores merged cell content.
- Submit a long home appliance research report, and verify chunking results are split according to information logic, not just truncated by fixed length.
- Upload a set of previously processed weekly store data, and check if parsing tasks are marked as skipped and not executed repeatedly.
- View parsing logs to confirm all core fields configured in
REQUIRE_EXTRACT_FIELDSare normally extracted without omission.
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.