What the data for this category looks like
The data for white goods intelligent due diligence reports comes primarily from official brand technical manuals, public reports from national energy efficiency testing institutions, industry standard documents, and e-commerce platform product detail pages. Data update cadence aligns with new product release cycles. Update frequency is higher during new product launch phases. Regular parameter updates follow a quarterly cycle. Individual documents mostly combine structured tables and explanatory text. Core fields include product model, rated power, external dimensions, net weight, energy efficiency rating, and certification number. Power is measured in watts, dimensions in millimeters, weight in kilograms, and energy efficiency ratings are identified using Arabic numerals.
Constraints imposed by these characteristics on knowledge base retrieval and recall
White goods data has numerous structured fields with clear units. Retrieval must match field semantics precisely, to avoid recall bias caused by cross-unit confusion. Data from multiple sources has inconsistent parameter specifications. Normalization processing must be completed during the recall phase, to ensure consistent parameters for the same product model across different channels. High-frequency updates during new product launch phases require the knowledge base to support fast incremental uploads and index updates, to avoid data lag. Individual documents contain multiple nested parameter tables and redundant explanatory text. Precise localization of parameter-containing paragraphs is required, to avoid recalling irrelevant after-sales or marketing content.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | top 10-15 results | White goods documents have many parameter entries. Too many recall results increase reranking computation load. Too few fail to cover all valid parameters |
similarity_threshold | 0.72-0.85 | White goods model naming rules are strict, and similar models have distinct parameter differences. A threshold that is too low may recall irrelevant model data. A threshold that is too high may miss parameter differences across batches of the same model |
rerank_return_count | top 5-8 results | Intelligent due diligence reports only require core parameters, not excessive redundant content. Reranking retains the most relevant structured parameter entries |
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Official brand technical manuals are mostly multi-page PDF collections. Single document parsing takes a long time. An overly short timeout setting causes parsing failures |
chunk_length | 800-1200 characters | White goods documents mostly combine tables and explanatory text. Chunk length adapts to the length of combined content, avoiding splitting that breaks contextual associations of parameters |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Large brand technical manual collections have significant file size. A higher upload limit is required to complete full index construction |
The values given are common starting points and should be measured against the reader's own samples.
Three common misconfigurations
- Symptom: After enabling the reranking model on the application side, retrieval results return empty, but matching content can be properly returned in the test interface of the knowledge base management backend. Cause: The reranking model configuration on the application side is not synchronized with the configuration in the knowledge base backend, or the application side does not have correct permissions to call the reranking model.
- Symptom: After performing a version upgrade, the knowledge base page fails to load normally, and the displayed version number does not match the actual upgraded version. Cause: The knowledge base index was not rebuilt during the upgrade process, or the configuration files of the version update package were not overwritten correctly.
- Symptom: After clicking the knowledge base module and triggering an error, detailed error log information cannot be obtained. Cause: The console log collection function was not enabled in advance, making it impossible to locate the specific interface or parameter issue corresponding to the error.
How to confirm correct configuration
- Access the retrieval test page in the knowledge base management backend, enter typical white goods product model keywords, and check whether the fields and units of the recall results match the target parameters.
- After enabling the reranking model, initiate the same retrieval from both the application side and the backend test interface, compare the number and content of returned results, and confirm that configurations are synchronized.
- Upload an official brand technical manual document, wait for parsing to complete, view the parsed chunked content, and confirm that parameter tables and explanatory text contexts are not incorrectly split.
- After completing the version upgrade, access the system settings page to check the version identifier, confirm that it matches the officially released version, then attempt to retrieve knowledge base content to confirm normal loading.
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.