What data looks like for this category
E-commerce service investment research data mainly comes from e-commerce platform open APIs, merchant backend export files, and industry monitoring crawlers. Data covers basic product information, user reviews, transaction link logs, marketing activity rules, and other types. Product documents include structured fields such as SKU code, category name, pricing, inventory count, and detailed description. User reviews are unstructured long text. Data update frequencies include real-time (user reviews, inventory changes), hourly (transaction data), and daily (category market data). The length of individual documents ranges from tens of characters for product titles to thousands of characters for detailed page content.
What constraints these characteristics impose on vector models and indexing
Multi-type mixed documents for e-commerce services require vector models to adapt to both short text titles and long text reviews, to avoid semantic segmentation bias. Frequently updated data requires indexes to support incremental synchronization. Otherwise, full index reconstruction will consume excessive computing resources. Structured fields such as SKU codes and pricing must be separately mapped to vectors or used as metadata for filtering, to avoid confusion with text semantics. Duplicate cross-channel documents for the same product will cause redundant indexes. Deduplication must be based on unique identifiers, not only semantic similarity. E-commerce investment research needs to retrieve multi-dimensional related information. The index retrieval scope must cover product, competitor, and industry data, which imposes requirements on index sharding strategies.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800-1200 characters | The length of e-commerce documents varies widely. This range balances semantic completeness and vector computing efficiency, preventing short texts from losing key information and long texts from having their semantics damaged by segmentation. |
similarity_top_k | Top 20-30 results | E-commerce investment research needs to cover multi-dimensional related information. Too many retrieved results will exceed the context window limit, while too few will miss valid competitor or product data. |
score_threshold | 0.72-0.85 | The semantic similarity of e-commerce product titles and review texts is relatively high. A threshold that is too low will introduce irrelevant results, while a threshold that is too high will filter out valid competitor information. |
rerank_top_n | Top 5-8 results | Based on e-commerce user decision logic, retaining the most relevant product and review information after reranking avoids redundant results interfering with investment research judgments. |
incremental_update_batch | 500 items per batch | E-commerce data has a high update frequency. Performing incremental updates in batches reduces index reconstruction overhead and balances real-time performance and resource usage. |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Duplicate product documents for the same SKU remain after index merging. Cause: No deduplication using unique identifier fields configured for SKU codes. Deduplication based only on text semantics cannot distinguish cross-channel versions of the same product.
- Phenomenon: 401 error returned when calling the vector model. Cause: API keys and dedicated request domain names for the vector model are not correctly configured. Some e-commerce scenarios require binding to a platform-specific gateway address.
- Phenomenon: The order of retrieved index blocks does not match the original document after custom document splitting. Cause: No original order identifier is retained for document blocks. Incremental index update disrupts the block sequence.
How to confirm correct configuration
- Upload product documents containing duplicate SKU codes, and verify that the number of unique SKUs in the index matches the number of unique SKUs in the uploaded documents.
- Call the vector model test interface, check that no 401 error is returned, and that the vector data format meets configuration requirements.
- Submit an incremental update task, and verify that the index update delay does not exceed the preset batch processing cycle.
- Retrieve product titles with similar semantics, and check that the similarity scores of the retrieved results fall within the preset threshold 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.