What the data for this category looks like
Data for kitchen and bath appliance intelligent due diligence reports comes primarily from official brand parameter databases, e-commerce platform product detail pages, public reports from national quality inspection agencies, and after-sales operation archives. Data update rhythm is adjusted alongside new product launches. Update frequency is higher during new product launch cycles, and only compliance updates are made for discontinued models. The structure of individual report documents is fixed, including fields such as model identifier, energy efficiency rating, rated power (W), installation dimensions (mm), material type, warranty period, and compliance certification number. Some documents include installation diagrams and troubleshooting guides.
What constraints these characteristics impose on vector models and indexing
Features with multiple fields and fixed units require vector models to adapt to numeric semantics, and avoid confusing parameters with different units. For example, the semantic difference between "1000W" and "1000mm" must be distinguished. The fixed document structure requires chunk splitting to follow business modules, rather than using a fixed length. This avoids splitting installation requirements and rated power into the same passage, which damages semantic integrity. The phased update rhythm requires the index to support incremental synchronization. Only regenerate vectors for updated documents, do not perform a full index rebuild. The unique model identifier field requires the index to support metadata filtering by model. This narrows the recall scope and improves the targeting of due diligence reports.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The core business modules of kitchen and bath appliance due diligence reports mostly fall within this range. Splitting in this interval preserves semantic integrity for individual modules |
recall_top_k | Top 8–12 results | The number of core parameter modules for kitchen and bath appliance due diligence reports is limited. Excessive recall will introduce irrelevant content |
embedding_model | bce-embedding-v1 or similar models that support multi-field semantic alignment | This model can adapt to semantic distinction between values and units, avoiding vector confusion between different parameters |
index_incremental_sync | Enabled | Kitchen and bath appliance data updates are carried out in phases. Incremental synchronization reduces index construction time |
filter_metadata_fields | Enable model field filtering | Due diligence reports need to recall content related to the target model, avoiding irrelevant results across models |
push_data_timeout | 600 seconds | Individual kitchen and bath appliance due diligence reports have large text volumes. Sufficient time must be reserved for upload and vector generation |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Search return results have low relevance, with some results matching parameters from non-target models. Cause: The
filter_metadata_fieldsmodel filtering is not enabled, orchunk_sizeis set too small, resulting in parameter fragments from different models being split into the same passage. - Phenomenon: After using chunk mode to call the
pushdataAPI for upload, the interface continuously displays "Indexing" status with no changes. Cause:index_incremental_syncis not configured as enabled, orpush_data_timeoutis set lower than actual generation time, causing the upload process to interrupt without triggering retries. - Phenomenon: The final set of indexes after question answering splitting cannot generate vectors. Cause:
chunk_sizeis set smaller than the minimum valid text length for a single segment, resulting in incomplete semantics for the final fragment, which cannot be properly processed by the embedding model.
How to verify correct configuration
- Upload a single kitchen and bath appliance due diligence report, view the parsed chunk list, and confirm that each chunk corresponds to one business module with no cross-module splitting.
- Initiate a search with target model keywords, and check whether the recall results only include content related to that model.
- Submit an incremental update task, and check that the index backend only displays the vector generation progress for updated documents, with no full index rebuild prompts.
- Call the embedding model to test a single parameter fragment, and confirm that the returned vector results have no semantic confusion between values and units.
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.