What the documents look like
Store count and loss-control work draws on POS inventory, WMS records, monitoring systems and the SB/T count specification. The everyday documents are count sheets, variance analyses, loss-write-off approvals and monitoring retrieval records. Their subject is the store, the stockkeeper and the loss-prevention staff; their object is book-to-physical variance and shrinkage. Supplier settlement is outside this scope.
In cross-border and multilingual settings, one batch of count material can contain a Chinese count sheet, a foreign-language variance note and a bilingual write-off approval at the same time. Semantic retrieval carries cross-language understanding, so a Chinese question can match a foreign-language knowledge point and the multilingual material can enter one retrieval library without being translated document by document. Material updates run monthly or quarterly. After an update, the actual training and index state needs rechecking; a successful upload does not by itself mean retrieval has refreshed.
Constraints at the document chunking step
A count sheet usually contains a header, SKU rows, a variance column and remarks. A variance analysis contains conditional statements and a conclusion. A write-off approval contains an approval status and an authorization boundary. Chunking needs to inspect paragraphs against the structure of the specific material, so that a condition, its explanation and the content it constrains are not split apart. When a passage is structurally and semantically complete and single in meaning, retrieval precision improves.
Raw data and retrieval indexes need to be kept distinct. One set of raw data can map to several vectors, and the indexes field records the vector IDs it corresponds to. When the same raw data is hit by multiple recall paths, the vector score takes the highest score. Chunking results should retain a document identity and version that can point back to the source text, so that manual checking is possible.
The same-vector-model requirement for a retrieval library is a condition of that library itself. It does not extend to business-registration sites or other external systems. Libraries on different vector models cannot be associated together; the identifier is checked first, non-matching libraries are excluded, and the actual observation is recorded.
Configuration and implementation checks
The settings below concern the hypothetical drill sample on this page and list only documented settings or implementation checks relevant to this feature and primary task.
| Configuration item | Setting or check | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval or hybrid retrieval | Full-text retrieval for terms and numbers, semantic retrieval for natural-language variance notes |
| Reference limit | Maximum tokens referenced per search | Hypothetical trial starting value 3000 tokens, chosen because the sample source is short and conditional clauses must be retained; compared against other values and adjusted by observed effect |
| Minimum relevance | A 0-1 value, effective only under semantic retrieval or result reranking | Hypothetical trial starting value 0.4 to filter low-relevance fragments; compared with 0.2 and 0.6 to observe differences |
| Result reranking | A ReRank model is available and enabled per settings | Secondary ordering of text results when candidates are numerous; the 0-1 score can be used for filtering |
| Question optimization | Coreference removal and question expansion | Enabled for follow-up questions about count variance; disabled for standalone clear questions to reduce latency |
| Chunking check | Inspect paragraphs against count sheet, variance analysis and write-off approval structure | Confirm that conditions, explanations and the content they constrain are not split apart |
Three common errors
- Treating a successful upload as refreshed retrieval. Input: in the hypothetical drill sample, the March 2025 count sheet was uploaded without checking training and index state. Comparison: the upload record against the actual training and index state. Observable result: retrieval still returns the older chunk version, showing that the post-update state needs rechecking.
- Treating semantic recall or relevance as permission or entity isolation. Input: in the hypothetical drill sample, a write-off approval request whose draft sentence is "This store's loss write-off has been approved." Comparison: the draft sentence against the approval status field in the source text. Observable result: the source records only "pending review," so the draft sentence is unsupported, and the retrieval score does not change that judgment.
- Treating RRF as a toggle switch. Input: the hybrid retrieval configuration in the hypothetical drill sample. Comparison: whether an RRF switch exists in the configuration interface against the documented description. Observable result: RRF is a fusion process for hybrid results and is not presented as a standalone switch.
How to verify the setup
The verification below uses the hypothetical drill sample described earlier. The sample source text is: "On 15 March 2025, Store A held a closing count. SKU-1024 showed 80 units on the books and 74 units physically counted, a variance of -6 units. The cause of the variance is under investigation. Loss write-off approval status is pending review." The draft sentence to be judged is: "This store's loss write-off has been approved."
- Input: the sample source text and the draft sentence. Comparison: "has been approved" in the draft sentence against "pending review" in the source. Observable result: the two statuses do not agree, the draft sentence is unsupported, and the item is recorded as awaiting verification.
- Input: SKU-1024 and the -6 unit variance in the sample source text. Comparison: how full-text retrieval matches the identifier against how semantic retrieval matches the natural-language variance note. Observable result: the identifier query is matched by full-text retrieval and the variance cause by semantic retrieval; if one path does not match, that path's observation is recorded, with no guarantee of a first hit or of full recall.
- Input: the sample source text and an empty-result check request, with the paper input
[]. Comparison: how the downstream design handles an empty reference against the documented description. Observable result: the empty-result handoff is checked against the downstream design, and no claim is made that a fabricated rule makes retrieval always return[].
After material changes, check the actual training and index state and confirm that retrieved content matches the latest version. Retrieval and form-collected values do not constitute authorized execution; loss write-off approval, database writes and external synchronization each require their own downstream implementation.