What the documents look like
Seller ratings and experience-score maintenance work with consumer feedback that arrives after fulfillment. The main data comes from platform experience scores, service indicators, after-sales reviews, and customer-service systems. The documents handled day to day include negative-review handling records, experience-score details, service diagnostic reports, and rectification ledgers. Store operations staff, customer-service supervisors, and quality inspectors work with this material. Errors in this area can reduce search weight, restrict promotional sign-up, and lower store conversion, so product-information and selection Q&A must keep source text, indexes, and cited scope separate during review.
This page addresses one step: the vector model and index layer for product-information and selection Q&A. Retrieval here means recall of candidate content. Semantic recall and relevance scores do not perform permission or entity isolation, and they do not authorize any business action. A high score on a record does not mean the record has been approved for external use.
Constraints at the vector indexes step
Knowledge bases linked for retrieval must use the same vector model. The actual model identifier and its input type are checked first, and libraries using different models are excluded rather than co-linked. One source data block can carry several indexes, so index content and source text are reviewed separately. Indexes are built from complete and focused information; retrieval coverage and index length are weighed against the sample at hand. Image retrieval also depends on whether an image description index or image vector index was generated at ingestion, so the query side alone does not settle whether an image can be found. After any model or index change, the same set of questions is compared against cited source text. Vector scores alone do not support a claim about outcome.
A retrieval run is not a fixed single path. Problem optimization, semantic retrieval, full-text retrieval, hybrid retrieval, image description retrieval, image vector retrieval, RRF fusion, and Rerank are combined according to input and settings. Image paths require image input and matching model capability. Rerank requires a rerank model and its own enablement. RRF is a fusion step for multiple result channels, not a switch. The minimum relevance value applies only under semantic retrieval or result reranking, and it is a filter threshold rather than a final sort rule. The citation limit is measured in tokens.
Configuration and implementation checks
Hypothetical drill sample: a store operator on 10 June 2025 organizes a negative-review handling record. The source text reads: "Order A was signed for on 3 June, a negative review was submitted on 5 June, customer service called on 6 June with no answer, and a replacement part was shipped on 7 June." The draft sentence under review reads: "This negative review was fully handled on 6 June." The settings below are listed against this page's documents and primary task.
| Configuration item | Hypothetical starting point for this drill | How to check |
|---|---|---|
| Vector model | Same model as the linked knowledge base | Compare model identifier and input type; exclude libraries with a different model |
| Index content | Each negative-review record keeps subject, order, time, and status | Compare item by item with source text; confirm no key field was removed or altered |
| Citation limit | 2000 tokens as the starting value for this drill | Compare with sample citation length; observe whether source text is truncated |
| Minimum relevance | 0.4 as the starting value for this drill | Compare against semantic or rerank scores; observe which citations are filtered |
| Search mode | Hybrid retrieval with RRF | Check whether semantic and full-text results are both recalled |
| Result reranking | ReRank enabled, filtering by its score | Check whether rerank score and cited source text agree |
The two numeric starting values are chosen only to give this drill a concrete comparison point. They are not official defaults, and the reason for each is to have a value that can be compared against observed citation length and observed scores.
Three common errors
- Treating a vector score as an authorization result. Observed error: a high score on a negative-review record is read as meaning the record is cleared for external use. Check: input the same question set, compare the score against the handling status in the source text, and observe whether the record still sits at the "no answer" stage.
- Co-linking libraries that use different vector models. Observed error: experience-score details and negative-review records built on different models are placed into one retrieval. Check: compare model identifiers, exclude the non-matching library, and record the actual observation. Cross-model retrieval is not executed for comparison.
- Treating a draft sentence as verified fact. Observed error: the draft says handling was completed, while the source text shows only an unanswered call and a shipped replacement part. Check: input the draft sentence and the source text, compare subject, date, and status fields, and observe whether the same field carries mutually exclusive values.
How to verify the setup
- Verification one: input the hypothetical source text and the draft sentence, and compare "customer service called on 6 June with no answer" against "fully handled on 6 June." If the same subject, the same date, and the same field carry mutually exclusive values, the draft sentence is judged unsupported. A stage-only statement, or a field the source text does not cover, is recorded as unsupported or pending review rather than as a contradiction.
- Verification two: input the same set of negative-review questions and compare cited content against the source text. The condition is met when the citation retains the order, signature, negative review, contact, and replacement-part fields and stays within the citation limit. Retrieval checks record observations and failure branches; they do not guarantee a first-hit result, full recall, or an empty return for unrelated queries.
- Verification three: input the paper sample
[]and check whether the downstream path carries through. The condition is met when the output path accepts an empty array and no invented rule is claimed to force an empty return. A pending review status does not demonstrate a lack of operating permission. Missing evidence is recorded as a gap, and no test, deployment, approval, or refund is claimed to have occurred.