What the Data for This Category Looks Like
Raw data is sourced from official public product specifications of appliance brands, energy efficiency filing public disclosures, test reports from third-party testing institutions, and circulation data from industry associations. Updates are synchronized when new products are launched. Regular data updates occur every quarter. Documents use a mixed structure of structured tables and paragraph descriptions. Fields include product model, energy efficiency rating, rated power, net weight, launch date, and warranty period. Most units use standard metric units such as watts, kilograms, and years.
Constraints on Vector Models and Indexing
The mixed structure of structured fields and free text in documents requires distinguishing field weights during vectorization. This prevents generic semantic retrieval from confusing clear fields like rated power and warranty period. The combined rhythm of sudden and scheduled updates requires indexes to support both incremental writing and scheduled full verification modes. This adapts to the rapid launch of new products. The wide range of document lengths requires a segmentation strategy tailored to different document types. This prevents critical compliance information from being truncated in long test reports, or insufficient semantic information in short specification sheets. The unified unit standard across multiple product categories requires index metadata to retain unit fields. This prevents matching errors caused by unit ambiguity during semantic retrieval.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed length of white goods specification documents and test reports, avoids semantic loss in short texts or truncated redundancy in long texts |
chunk_overlap | 50–100 characters | Retains critical parameter associations across segments, prevents fields like rated power and energy efficiency rating from being split by segment boundaries |
vector_store_type | pgvector | Supports binding structured fields to vector metadata, adapts to multi-field retrieval requirements |
similarity_threshold | 0.72–0.85 | Calibrated via actual testing, distinguishes semantic similarity boundaries for appliance models and energy efficiency ratings |
top_k | Top 6 entries | Matches the typical volume of associated data for due diligence reports, avoids excessive redundant recall interfering with compliance verification |
incremental_index_enabled | Enabled | Adapts to sudden update demands from new product launches, reduces resource consumption from full index rebuilds |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Configuration Mistakes
- Phenomenon: The vector database only stores vector data and cannot associate original document content. Retrieval results cannot be traced. Cause: No binding between vector storage and original document metadata is configured. Only text fragments were uploaded for vectorization.
- Phenomenon: When using the bge-m3 vector model, semantic retrieval similarity scores are generally high. Cause: The model’s semantic weight is not adjusted for the structured fields of white goods. Generic models have insufficient semantic matching accuracy for professional fields such as rated power and energy efficiency rating.
- Phenomenon: No data is returned after configuring the index. Cause: The index’s automatic refresh switch is not enabled, or the uploaded original document does not match the index’s metadata tags.
How to Verify Correct Configuration
- View the metadata fields of the vector database. Confirm that key original document fields such as product model and energy efficiency rating are included. Verify the binding relationship between metadata and vectors.
- Upload a typical white goods test report, run a semantic retrieval, and check that the segment length of the recall results matches the configured
chunk_sizerange. - Upload a new product document, trigger incremental indexing, wait for the configured refresh interval, and retrieve the relevant fields of the new product. Confirm that the index has been updated.
- Adjust the test value of the similarity threshold, compare recall results across different thresholds, and confirm that the threshold boundary meets the semantic matching needs of the business scenario.
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.