What the data for this category looks like
White goods investment research data covers multi-source heterogeneous content. Sources include industry association reports, quarterly/annual financial reports of listed white goods enterprises, sales monitoring data from third-party research institutions, public transaction data from e-commerce platforms, and patent databases. Update cycles vary significantly: industry reports are updated quarterly or semi-annually, financial reports are released annually or quarterly, e-commerce sales data is updated daily, and patent data is stored in real time. Document structures include structured CSV files (with fields such as model, SKU, shipment volume), semi-structured research report PDFs, and unstructured industry news. Fields and units must strictly match industry standards. For example, the shipment volume unit is ten thousand units, the average price unit is yuan per unit, and patent fields include patent number and application date.
Constraints on citation source and traceability
The multi-source heterogeneous data feature requires the traceability system to distinguish data source types. This prevents confusion of similar data from different sources. Differentiated update cycles require configuring different synchronization cycles for different data sources. This ensures the timeliness of cited content meets investment research needs. Clear fields in structured data can directly bind corresponding source identifiers. For unstructured research reports, paragraph-level source information must be extracted to improve traceability accuracy. Unified unit standards require retaining original units during traceability. This avoids unit ambiguity across data sources. The large number of segmented product categories also requires traceability information to be associated with specific segmented categories. This ensures cited content matches query topics.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxRecallNum | 10-20 items | White goods investment research data has multiple sources and large per-source data volume. Too many recalled results will exceed the large model context limit. Too few recalled results will fail to cover complete information for segmented product categories |
recallThreshold | 0.75-0.85 | White goods investment research involves many professional terms. A threshold that is too low will introduce irrelevant home appliance category data. A threshold that is too high will fail to recall enough valid reference content |
csvEncoding | UTF-8 | Structured sales and inventory data in the white goods industry mostly uses UTF-8 encoding. Using this encoding avoids garbled characters after upload |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Batch-uploaded white goods research report PDFs and structured CSV files have large file sizes. A longer parsing time is required to avoid timeout failures |
referenceSourceDisplay | Enabled | Investment research scenarios require clear citation traceability. This ensures answer content is verifiable and compliant with financial investment research regulations |
reRankTopN | 5-8 items | There are many segmented product categories for white goods. Retaining the most relevant citation sources after re-ranking improves the accuracy of traceability information |
The parameter values provided on this page are common 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 errors
- Phenomenon: The answer returned by the knowledge base interface does not display citation sources, or the citation fields are empty. Cause: The
referenceSourceDisplayconfiguration item is not enabled, or recalled reference content is not correctly associated with data source identifiers. - Phenomenon: Garbled characters appear in the downloaded preview after uploading a structured CSV file for white goods. Cause: The
csvEncodingconfiguration is not set toUTF-8, and other encoding formats are used to parse the file. - Phenomenon: After setting
maxRecallNumto 3000, the large model does not receive context content. Cause: The number of recalled entries exceeds the large model context window limit, causing the system to truncate the context without passing it to the large model.
How to confirm configurations are correctly set
- Upload a structured CSV file for white goods, check the content display on the upload preview interface, and confirm there are no garbled characters.
- Initiate an investment research query targeting a white goods segmented product category, check the citation source module at the end of the answer, and confirm the corresponding data source information is displayed.
- Adjust
maxRecallNumto different values, verify that the context length changes as expected, and no context truncation occurs. - Upload an unstructured white goods research report PDF, check the document details in the knowledge base after parsing, and confirm correct paragraph source information has been extracted and associated.
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.