What the data for this category looks like
The data for white goods marketing content primarily comes from customer acquisition campaign materials from financial institutions. This includes official gift brochures from partner brands, detailed cooperation pages on e-commerce platforms, standardized scripts for offline events, copy for phased gift-giving campaigns, and a repository of common after-sales questions. Update cycles align with customer acquisition campaign timelines, with no fixed schedule. Single-update documents include three core content types: core gift parameters, marketing selling points, and campaign rules. Fields include gift model, energy efficiency rating, applicable space, campaign validity period, and others. Parameter units are mostly standardized measurement units such as watts, kilowatt-hours, and square meters. Text content is mostly composed of short paragraphs, with significant variation in the length of individual documents.
What constraints these characteristics impose on vector models and indexing
Scattered sources of white goods marketing content lead to insufficient consistency of cross-channel text terminology. This causes semantic deviation in vector extraction, so normalization configuration must be adapted to correct these differences. The non-fixed update cycle creates fluctuating pressure for index updates. Dynamic adjustment of index refresh thresholds is required to adapt to sudden traffic from financial institution customer acquisition campaigns. Significant variation in the length of individual documents—coexisting short selling point copy and long campaign rules—imposes requirements on variable-length segmentation processing logic. Some parameter fields have clear measurement units. Vector modeling must retain unit semantics to avoid matching deviation, so vector models sensitive to structured parameters must be selected.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_normalization | Enabled | White goods marketing content includes structured parameters. Unnormalized vector models cause semantic matching deviations. Enabling this setting unifies vector magnitudes and is compatible with most mainstream embedding models |
chunk_size | 800–1200 characters | Balances semantic integrity for both short selling point copy and long campaign rules, and avoids segmenting core product parameters and marketing information |
similarity_threshold | 0.72–0.78 | Filters low-relevance recall results, and adapts to the semantic matching accuracy requirements of white goods product parameters and marketing selling points |
retrieval_top_k | Top 6–8 results | Covers multi-dimensional user query needs, while avoiding introducing excessive irrelevant promotional or after-sales content |
index_refresh_strategy | Incremental update | Adapts to the non-fixed document update cycle, and reduces resource consumption from full index rebuilding |
rerank_top_n | Top 3–5 results | Focuses on the most relevant marketing content and product parameters, improving the accuracy of final recall results |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After switching the knowledge base vector model, the interface shows no progress, and the model switching operation cannot be performed again. Cause: Cache data of the original vector index was not cleared before switching. The new model cannot overwrite the metadata records of the old index.
- Symptom: Single token consumption during knowledge base queries exceeds expectations, causing query timeouts or excessive resource usage. Cause: The
max_retrieval_tokenparameter was not configured, or the set value is too large, resulting in ultra-long documents being directly sent to the vector retrieval process. - Symptom: After updating product images in marketing materials, retrieval results still return old image content. Cause: The incremental update switch for image indexing was not enabled, or the index refresh cycle was set too long, failing to synchronize updated image vector data in a timely manner.
How to confirm proper configuration
- Run test queries that include product parameters and marketing selling points, check the relevance and quantity of recall results, and adjust corresponding configuration items to match business needs.
- Upload updated marketing documents, check the update status on the index progress interface, confirm that incremental indexing triggers normally, and verify that the index refresh strategy is effective.
- After switching the vector model, check the current status on the model configuration interface, confirm that the new model has been loaded with no abnormal errors, and verify that the model switching process works correctly.
- Test the token consumption of a single query, compare it with the preset retrieval token upper limit parameter, and confirm that actual consumption falls within the expected range.
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.