What This Category’s Data Looks Like
Kitchen and bath appliance investment research data comes primarily from official product manuals, third-party testing reports, e-commerce product detail pages, brand new product launch announcements, and supply chain quotation documents. Update frequency aligns with new product launches. Standard active products have parameter changes synchronized every quarter. A single document typically includes fields such as model identification, rated power, energy efficiency rating, body dimensions, installation specifications, and after-sales period. Most units use standardized metrics like watts, millimeters, cubic meters, and years. Some documents include installation diagrams and troubleshooting steps.
Constraints on Retrieval and Recall
The multi-field structured data and professional measurement units for kitchen and bath appliances require the retrieval system to support field-level precise matching. This prevents generic searches from confusing parameters such as power and dimensions. Mixed text and image content in documents raises OCR error risk during text extraction. This calls for targeted optimization of segmentation rules. Frequently updated new product data requires the retrieval system to support incremental recall. This stops outdated parameters from being returned. E-commerce documents that mix marketing copy and technical parameters can reduce recall result relevance. This requires pre-classifying content before indexing.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Single kitchen and bath appliance test reports and product manuals typically do not exceed 300 MB. Reserved redundancy space adapts to multi-image bundled documents |
maxChunkSize | 800–1200 characters | Kitchen and bath appliance parameter and description text mostly consists of short sentences. This segmentation length preserves the integrity of parameter groups, avoiding split association information breaks |
recallTopK | Top 8–10 results | Single-round investment research queries require comparison across multiple model parameters. Too many recall results increase token consumption. Too few fail to cover comparison dimensions |
similarityThreshold | 0.72–0.80 | Semantic similarity threshold to distinguish professional parameters from marketing copy. Prevents low-relevance marketing content from being recalled |
rerankTopN | Top 3–5 results | Investment research scenarios require precise matching of core parameters. Retains the 3-5 most relevant documents after re-ranking, balancing recall accuracy and response speed |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Kitchen and bath appliance documents often include high-definition diagrams. OCR parsing takes longer. This duration prevents large document parsing interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values may vary based on material format, data volume, and business rules. It is recommended to test against your own samples before finalizing settings.
Three Common Configuration Mistakes
- Retrieval results do not match investment research queries, returning large amounts of non-technical parameter marketing content. The cause is a
similarityThresholdvalue that is too low. This fails to filter marketing copy from e-commerce detail pages, leading to recall results mixing professional parameters and promotional content. Ultimately, the large language model-generated response deviates from investment research requirements. - Single-round query response time is too long, and token consumption exceeds expectations. The cause is not limiting the number of
recallTopKrecall results, and not configuringrerankTopNfor secondary filtering. A large number of redundant documents are included in the context. This increases retrieval time and occupies excessive token quotas. - Uploading product test reports over 100 MB triggers upload failure. The cause is the default
UPLOAD_FILE_MAX_SIZEconfiguration set to 100 MB, which does not adapt to the storage requirements of single large kitchen and bath appliance documents. This prevents files from being properly uploaded to the knowledge base.
How to Verify Proper Configuration
- Upload one typical kitchen and bath appliance product manual. Check if the parsed segmented text retains complete parameter groups, with no obvious split breaks.
- Initiate one parameter query for a specific kitchen and bath appliance model. Verify that the recall results include the core technical parameters of that model, with no large amounts of irrelevant marketing content.
- Upload one bundled document over 300 MB. Confirm that the upload process is not interrupted, and the parsing task completes normally.
- Check system logs to confirm that incremental synchronization configuration is active, and new product documents can be retrieved within a reasonable time frame.
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.