What the documents look like
Store count and loss control work in supermarkets and convenience stores centers on physical-versus-book inventory differences. The material set for this page is count sheets, variance analysis, loss write-off approvals and monitoring review records. These documents are produced on a monthly and quarterly rhythm, and each one typically carries fields such as item code, store number, count period and variance quantity. When retrieval runs against this material, item-code and model-number questions depend on exact keyword hits, while natural-language questions depend on semantically close content. The update frequency only means new versions of the material appear on a cycle; retrieval settings are checked against what was actually indexed.
This category differs from supplier settlement because it handles book-to-physical differences and shrinkage, and it does not process supplier payables. The parties involved are the loss-prevention department, store inventory keepers and finance auditors. A retrieval mistake here feeds inventory distortion, inflated gross margin or staff fraud, so the boundary between what a document states and what a downstream action has done matters.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases built on different vector models cannot be associated together; the vector model identifier of each base is checked and non-matching bases are excluded. Retrieval outputs cited content as an array, and its length can be zero, which means the output path still runs even when nothing is found.
Semantic retrieval, full-text retrieval and hybrid retrieval are compared against test questions; no single mode is assumed to suit all material. The minimum relevance value is a filter threshold that takes effect only in semantic retrieval or when result reranking is used, and it does not perform permission or entity isolation. The citation limit is measured in tokens and controls the total returned content. Question optimization performs coreference resolution and question expansion and adds one model call. Image retrieval requires a corresponding image input and model capability, and reranking requires a rerank model enabled by configuration. Retrieval is a recall process; business authorization, approval and database writes need separate downstream implementation.
Configuration and implementation checks
Hypothetical drill sample: a store count sheet dated March 2025 records "store number A-017, item code 6901234567890, book quantity 120 units, physical count 108 units, variance -12 units." The variance analysis records "the cause of this SKU variance is pending review, and the loss write-off approval form status is pending review." The proposed determination sentence is "this SKU has completed loss write-off approval." The source text contains the subject, object, status and period needed for the check.
| Configuration item | Hypothetical trial starting point and reason | Comparison and check method |
|---|---|---|
| Associated knowledge bases | Associate only count material bases that use the same vector model | Check each base's vector model identifier and exclude non-matching bases |
| Search mode | Start with hybrid retrieval | Compare recalled content separately for code questions and natural-language questions |
| Citation limit | Set a token value that covers a long variance analysis passage | Test with actual material of different lengths and check whether content stays complete |
| Minimum relevance | Label relevant and irrelevant samples first, then tune the threshold | Observe whether key count sheet fields survive filtering |
| Question optimization | Enable for multi-turn follow-ups containing references | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable by configuration when a rerank model is available | Compare candidate ordering before and after reranking and confirm key fields remain |
Three common errors
- Treating the proposed determination sentence "this SKU has completed loss write-off approval" as an effective fact. The source text only records that the loss write-off approval form status is pending review. Pending review does not show that approval passed, and it does not show that operating permission is absent. The check compares the approval form status field against the conclusion in the proposed sentence.
- Associating knowledge bases that use different vector models. The node requires knowledge bases with the same vector model, and cross-model bases cannot be associated together. The check inspects each base's vector model identifier, excludes non-matching bases and records the actual observation.
- Treating minimum relevance as a permission or entity filter. That value is a filter threshold that takes effect only in semantic retrieval or when result reranking is used, and it does not exclude unrelated business or unauthorized records. The check compares cited content before and after filtering and confirms that access control has a separate implementation.
How to verify the setup
- Input the hypothetical source text above together with the proposed determination sentence, and compare the approval form status "pending review" in the source against "has completed loss write-off approval" in the proposed sentence. If retrieval returns the source and manual review finds the status field is pending review, the proposed sentence is not supported; if the returned content does not contain that status field, record the evidence gap.
- Input item code 6901234567890 and store number A-017, use hybrid retrieval, and compare whether the returned citations contain book quantity 120 units, physical count 108 units and variance -12 units. If the cited content covers these fields completely, recall meets the check condition; if the citation is an empty array, record the empty result and check the downstream design.
- Prepare a multi-turn follow-up containing a reference, enable question optimization, and compare the subject and object of the optimized question against the original. If the optimized question still points to the same store number and item code, the subject was not altered; if the subject is replaced with another store or item, record the question optimization deviation. All three verifications use paper samples and make no claim that testing was performed.