What the documents look like
Store counting and loss control work from book-to-physical differences and shrinkage. The main data sources are POS inventory, WMS, monitoring systems, and the SB/T counting standard. Everyday documents include count sheets, variance analyses, loss-report approvals, and monitoring review records. Product information and selection questions must handle the product lines on a count sheet, the SKU descriptions in a variance analysis, the product names and specifications in a loss-report approval, and the time and location clues in a monitoring review record. These materials update on a monthly or quarterly cycle, and retrieval faces the same product written in several ways across documents. The vector model and index step therefore has to handle mixed expressions of product name, specification, and number. Full-text retrieval is more direct for numbers and model codes, while semantic retrieval suits natural-language descriptions.
Constraints at the vector indexes step
Knowledge bases joined to one retrieval must use the same vector model. Check the actual model and input type first; libraries with different vector models cannot be associated together. One source data block can be linked to multiple indexes, and index content is checked separately from original content. Index content should be built from complete, focused information, and retrieval coverage against index length is weighed with sample material. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, so the query side alone does not decide it. After a model or index change, compare cited original text using the same question set; a vector score alone does not support a performance claim. Semantic recall and relevance are ranking and filtering conditions inside retrieval, and they do not carry permission or entity isolation. A similarity setting does not exclude unrelated business lines or unauthorized records. Retrieval is a recall action; authorization, approval, database writes, and external synchronization each need their own downstream implementation.
Configuration and implementation checks
The table below lists documented settings or implementation checks for this page's hypothetical exercise sample. The values are starting points for this hypothetical trial, with the reason for choosing each and the value it is compared against.
| Configuration item | Hypothetical starting point for this trial | Reason and comparison method |
|---|---|---|
| Vector model | Same model identifier | Multiple knowledge bases joined to one retrieval must be checked for an identical model identifier; a library with another model identifier is not associated with them |
| Index content | Product name plus specification plus number | Compared with an index holding only the product name, observe the difference in cited original text for the same question set |
| Search mode | Hybrid retrieval | Compared with semantic retrieval alone, observe hits on number-type questions |
| Minimum relevance | Hypothetical starting point 0.3 | Compared with 0.5, observe whether cited original text still contains the target line after filtering |
| Citation limit | Hypothetical starting point 2000 tokens | Compared with 1000 tokens, observe whether cited content truncates the target line |
| Question optimization | Enabled for multi-turn follow-ups | Compared with the disabled state, observe retrieval results across consecutive follow-up questions |
Hypothetical exercise sample: the source material reads "March 2024 count sheet: product A, specification 500 ml, counted quantity 12 units, book quantity 15 units, variance -3 units." The draft sentence to be judged reads "Product A showed a variance of -3 units in the March 2024 count, and the loss report has been confirmed." The source states only the count variance quantity and does not state the loss-report approval status, so "the loss report has been confirmed" in the draft sentence is unsupported.
Three common errors
- Treating a semantic recall or relevance score as a permission or entity-isolation mechanism. Input is the same question set against different libraries; compare whether the cited original text returned falls outside the authorized scope, and observe whether the relevance setting changes the return of unauthorized records. Relevance is a filtering threshold and does not perform authorization judgment.
- Associating libraries that use different vector models. Input is two knowledge bases with different model identifiers; compare the model identifier field and observe whether they are allowed to be associated. When identifiers differ, exclude the non-matching one and record what was observed; do not run a cross-model retrieval to compare missed recalls.
- Treating a retrieval result as evidence that approval or a loss report is complete. Input is count-sheet source text containing a variance quantity and a draft sentence containing "the loss report has been confirmed"; compare the value of the same field and observe whether the source states the loss-report approval status. An unstated approval status is unsupported; a pending review does not show that an operating permission is absent.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence; compare "the loss report has been confirmed" in the draft sentence against the variance quantity stated in the source, and observe whether the source contains a loss-report approval field. When the source does not state it, record the evidence gap and judge the sentence unsupported.
- Input the same set of product-number questions; compare the cited original text returned by hybrid retrieval against semantic retrieval alone, and observe whether the number-type question hits the corresponding line. Hits are checked against the actual cited original text; a first-position hit or a full recall is not guaranteed.
- Input two knowledge bases with different model identifiers; compare the model identifier field and observe whether they are allowed to be associated. When identifiers differ, exclude the non-matching one and record the actual observation; do not infer the runtime result of associating them. An empty-result path can be checked against a downstream design using the explicit paper input [], without claiming that a fictional rule-based retrieval necessarily returns [].