Vector Models and Indexing for White Goods Investment

Data profile for this category

White goods investment research data sources include public industry research reports, public financial reports of leading brands, e-commerce platform sales monitoring data, and parts supplier price announcements. Update cadences vary. Financial reports are updated quarterly. Industry research reports are updated irregularly alongside market trends. E-commerce sales data is updated daily. Document structures include category-specific model parameters, supply chain cost breakdowns, and competitor comparison tables. Fields include model number, monthly shipment volume, per-unit production cost, and number of offline retail store locations.

Constraints on vector models and indexing workflows

The data exists in two forms: structured tables and unstructured text. Update frequencies vary widely. These factors require vector models to support both exact field matching and semantic text retrieval. Multi-SKU model data expands retrieval recall scope. Precise filtering of irrelevant competitor information is necessary. Document lengths vary widely. Ranges span single cost data entries to ten-thousand-word research report analyses. Segmentation processing must avoid breaking contextual semantic connections. Sales data has high real-time requirements. Financial reports are updated periodically. Indexes must support flexible switching between incremental and full updates.

Configuration guidelines

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Adapts to the text length of white goods research reports, avoids splitting that disrupts semantic coherence of structured tables and long-form analyses
`retrieve_top_k`	10–15 results	Covers multi-SKU retrieval needs, prevents missing core model data due to insufficient recall count
`similarity_threshold`	0.72–0.78	Distinguishes semantic differences between different models in the same category, filters recall results from irrelevant competitors
`embedding_model`	`bge-m3` or `text-embedding-3-large`	Adapts to semantic embedding of mixed text and structured fields, supports precise retrieval of multi-dimensional data
`index_shard_num`	3–5	Adapts to the data volume of white goods multi-SKUs, improves retrieval concurrency and accuracy
`incremental_update_interval`	Every hour	Adapts to the real-time update requirements of sales data, synchronizes periodically updated financial reports and research report data

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 against your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: When using bge-m3 as the embedding model, retrieved similarity scores are generally high, with minimal differences between different SKU models. Cause: No separate weighting is applied to structured fields such as model numbers and shipment volumes, so pure text embedding cannot distinguish semantic differences between different SKUs in the same category.
Phenomenon: After configuring the knowledge base index, the backend shows normal index status, but no matching results are returned during retrieval. Cause: The incremental update switch is not enabled, only a single full update was performed, and subsequent new sales data was not synchronized to the index.
Phenomenon: After configuring retrieval rules in a custom search template, retrieval is not triggered, while the native question-answering function works normally. Cause: The template is not bound to the knowledge base's vector index configuration, and only the large model's native question-answering capability is called.

How to verify correct configuration

Perform a single structured field retrieval, verify that returned model numbers match the search keyword, adjust similarity_threshold until the matching degree meets business requirements.
Upload new sales data, wait for the configured incremental update interval, then perform retrieval to verify that the new data was successfully recalled.
Test segmentation of research report text of different lengths, verify semantic coherence of the segmented content, adjust chunk_size until no contextual breaks appear in retrieval results.
View the index monitoring panel, confirm that the number of shards matches retrieval concurrency, and there are no log records of timeouts or recall failures.

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.