What the documents look like
Retail plastic-bag and single-use-item reporting sits inside a supervision context rather than a membership-marketing context. The material set for this page is four document families: plastic-bag purchase ledgers, sales vouchers, semi-annual reports, and self-discipline commitments. The stated data sources are the Ministry of Commerce reporting system, POS plastic-bag sales, the procurement system, and store ledgers. Consequences named in the brief include a deadline for correction, a fine range of 10,000 to 100,000, and a regulatory interview, so retrieval for operating reviews and anomaly review must return ledger, voucher, report, and commitment material accurately.
Two update rhythms appear in the brief: semi-annual and daily. Because retrieval behavior on this page does not change with update frequency, that dimension is noted and not carried further. The distinguishing feature against cashier membership promotion is the regulatory reporting character: the retrieval target is checkable purchase, sales, and submission records.
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 with different vector models cannot be associated together. Search modes are semantic retrieval, full-text retrieval, and hybrid retrieval; hybrid retrieval merges paths through RRF, and ReRank performs a second ordering of candidate text. The minimum relevance value takes effect only under semantic retrieval or when result reordering is used. The citation limit is measured in tokens. The output citation content is an array whose length can be 0, so an empty result still traverses the output path. Question optimization performs coreference removal and question expansion.
Retrieval returns content only. It does not carry authorization, approval, or business execution. A relevance score is a filtering threshold, not a category, entity, or permission filter, so it does not exclude unrelated businesses or unauthorized records. Retrieval is not authorization.
Hypothetical paper exercise sample: a supermarket headquarters compliance role is organizing one store's plastic-bag purchase ledger for the first half of 2025 in July 2025. The material text to be checked consists of three sentences: "From January to June 2025, the store purchased plastic bags in 12 batches, and the supplier was Packaging Plant A." "The May 2025 sales voucher shows plastic-bag sales revenue of 860 yuan." "On 30 June 2025 the store signed a self-discipline commitment, and the commitment content was to reduce single-use plastic-bag use." The draft sentence to be judged is: "The store completed the plastic-bag usage submission for the first half of 2025 and obtained headquarters compliance approval." The phrases "completed the submission" and "obtained approval" do not appear in the material text and remain unsupported; the material supports only the three facts of purchase batches, sales revenue, and signed commitment. This sample is for retrieval and checking rehearsal only and does not represent any real customer or actual business action.
Configuration and implementation checks
The table below is tied to this page's sample and lists documented settings or implementation checks relevant to this function.
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the base holding the four material families for this page | Check whether each base's vector model identifier matches, and exclude those that do not |
| Search mode | Try semantic retrieval first, then hybrid retrieval | Compare whether the two modes return content covering purchase, sales, and commitment against the sample source sentences |
| Citation limit | Measured in tokens; start at a value that can hold the three sample source sentences | Compare whether returned content fully contains the sample source text, and adjust if not |
| Minimum relevance | Mark relevant and irrelevant sample sentences first, then set a threshold | Takes effect only under semantic retrieval or result reordering; compare whether relevant sentences survive filtering |
| Question optimization | Enable for follow-up questions containing coreference and check the result | Compare the original question with the optimized question and check whether the subject was changed incorrectly |
| Result reordering | Enable when candidates are numerous and check the score | Compare the ranking position of the sample relevant sentence before and after reordering |
Three common errors
- Associating knowledge bases that use different vector models together. Input: two bases whose vector model identifiers differ. Comparison: each base's identifier against the node requirement. Observable result: the association is blocked or the non-matching base must first be excluded; no further inference about missed recall or redundancy follows from this.
- Treating minimum relevance as a permission or entity filter. Input: unauthorized records of other stores in the sample. Comparison: whether those records can still be returned before and after filtering. Observable result: relevance affects ordering and filtering only, and authorization requires a separate downstream implementation.
- Treating retrieval return as proof that submission or approval is complete. Input: the draft sentence "completed the submission and obtained approval." Comparison: whether the material source text states submission and approval. Observable result: the source text does not state them, so the judgment remains unsupported.
How to verify the setup
- Input the three sample source sentences and the draft sentence. Compare whether the returned content contains the three items of purchase batches, sales revenue, and signed commitment. Observable result: all three items are returned and the submission and approval in the draft sentence are not supported by the source text, so retrieval and checking meet the expectation.
- Input a follow-up question containing coreference. Compare whether the subject of the optimized question still points to the store for the first half of 2025. Observable result: the subject is not changed incorrectly, so question optimization meets the expectation.
- Input an explicit paper empty result
[]. Compare whether the downstream design handles a citation length of 0. Observable result: the empty-result path is traversed and no fabricated rule return is triggered, so empty-result handling meets the expectation. All of the above are paper checks; no retrieval or business action was actually executed.