What the data for this category looks like
White goods investment research data comes from manufacturer public financial reports, industry association energy efficiency databases, e-commerce platform sales rankings, offline channel survey records, and professional research reports. Data update cycles vary. Manufacturer financial reports update quarterly. E-commerce sales data syncs daily. Energy efficiency compliance documents update alongside policy adjustments. Document structures include structured parameter tables with fields like SKU code, energy efficiency rating, cooling/heating power. They also include semi-structured research report chapters, unstructured user review text. Some documents include image and table attachments.
Constraints on Knowledge Base Retrieval and Recall
Multi-source, heterogeneous data structures require the retrieval workflow to separate structured parameters and unstructured text. This prevents result deviation caused by mixed matching logic. Data sources with varying update frequencies need matching incremental update intervals. This stops outdated data from appearing in current retrieval results. Diversity of fields and units — such as cooling capacity measured in watts, revenue measured in hundreds of millions of yuan — requires matching unit information during recall. This avoids ambiguous retrieval results. A large volume of detailed product model data leads to redundant basic recall results. Reranking and filtering optimize result relevance. This improves the reference value of investment research judgments.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity_threshold | 0.72–0.78 | White goods product parameter accuracy requirements are high. A too-low threshold introduces irrelevant competing product models. A too-high threshold misses detailed products in the same category |
recall_top_k | Top 12–15 results | Single white goods product parameter documents are lengthy. Basic recall covers multi-dimensional parameters and research report snippets |
chunk_size | 800–1200 characters | White goods research reports often include long paragraphs of market analysis. Too long a chunk loses contextual connections. Too short a chunk breaks parameter associations |
incremental_update_interval | 4 hours | E-commerce sales data updates daily, manufacturer financial reports quarterly. The incremental update balances real-time performance and computational cost |
rerank_top_n | Top 5–8 results | Core parameters and research report conclusions must be retained. Too many redundant results interfere with investment research judgments |
allowed_file_extensions | .pdf, .xlsx, .md | Covers common formats for research reports, structured parameter tables, and annotated documents |
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 tailored analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Observation: Retrieval results include documents outside the target set. Cause: Collection filter parameters are not configured. No rule is set to only retrieve the dedicated white goods investment research collection.
- Observation: Images in uploaded Markdown files fail to load. The image field is empty in returned results. Cause: Local images are not uploaded to the knowledge base storage directory. Image path replacement rules are not configured.
- Observation: Retrieval returned results only include abstract snippets. Full web page content cannot be accessed. Cause: The content parsing module after web scraping is not enabled. Link reading permissions are not configured.
How to Confirm Correct Configuration
- Upload one Markdown document with local images and one structured Excel parameter table. Check that images display normally in the parsed document, and all fields are fully extracted.
- Initiate a retrieval for a specific white goods product model. Verify that results only come from the preset investment research collection, with no external irrelevant documents.
- Adjust the similarity threshold and recall count. Confirm that retrieval result relevance and quantity match expected values.
- Trigger an incremental update task. Check that newly added e-commerce data or financial report documents are correctly indexed.
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.