What the documents look like
Retail packaging reporting for plastic bags and single-use items centres on single-use plastics reporting and environmental supervision. The material comes from the Ministry of Commerce reporting system, POS plastic bag sales, procurement systems and store ledgers. The documents handled day to day include plastic bag procurement ledgers, sales vouchers, half-year reports and self-discipline commitments. Sales vouchers are produced daily; half-year reports are compiled on a half-year cycle. Procurement ledgers record incoming batch and quantity, sales vouchers record what was actually sold, half-year reports summarise usage and recovery, and self-discipline commitments record a store's own restraint statement. Contract and transaction material comparison requires checking whether subject, object, period and quantity fields agree across these materials.
Two features of this category shape the comparison work. First, the same physical quantity appears under different field names and different counting bases: a procurement ledger counts what entered the store, a sales voucher counts what left through the till, and these are not interchangeable. Second, the half-year report aggregates, while sales vouchers arrive per transaction, so a period stated in a report must be matched against vouchers covering that same period before any total is treated as supported.
Constraints at the document chunking step
Chunking constraints here come from the material structure itself. Procurement ledgers usually run line by line by batch; sales vouchers record transaction by transaction; half-year reports are written to an aggregate basis; self-discipline commitments are signed at store or head office level. Chunking needs to keep each material's document identity and version, and avoid separating a condition, a note or a qualifying statement from the content it constrains. One group of source data can correspond to multiple indexes, and retrieval indexes must be distinguished from the source data. Semantic recall and relevance scores serve similarity ranking only; they do not perform permission or entity isolation. Whether a retrieval library matches a vector model is checked against that library's own identifier, without extending the requirement to other external systems.
Hypothetical paper exercise sample. A retail store submits a plastic bag procurement ledger with the text: "1 January 2025 to 30 June 2025, this store procured a total of 12,000 single-use plastic bags, supplier a packaging factory, procurement batches 2025-01 to 2025-06, six batches in total." It also submits a sales voucher with the text: "1 January 2025 to 30 June 2025, this store's POS system recorded plastic bag sales of 11,800 units, unit price 0.5 yuan per bag." A draft sentence awaits judgement: "In the first half of 2025 this store's plastic bag procurement quantity and sales quantity were consistent, both 12,000 units." This draft sentence is to be compared against the two source texts above.
Configuration and implementation checks
| Configuration item | Hypothetical exercise starting value | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Material contains both identifiers and natural language; hybrid retrieval uses semantic and full-text recall together and merges through RRF |
| Reference limit | Set to 3,000 tokens for this hypothetical exercise | Procurement ledger and sales voucher entries are long and need to hold complete batch and period fields; compare against 2,000 to observe truncation |
| Minimum relevance | Set to 0.3 for this hypothetical exercise | This value takes effect only in semantic retrieval or with result reranking; compare against 0.5 to observe candidate count changes |
| Question optimisation | Set to enabled for this hypothetical exercise | The draft sentence contains reference and ellipsis; question optimisation can complete subject and period; compare against disabled to observe retrieval term completeness |
| Result reranking | Set to enabled for this hypothetical exercise | When candidates are numerous, reranking provides a second ordering; compare against disabled to observe position changes |
| Chunking method | Chunk by material structure | Procurement ledger by batch, sales voucher by transaction, half-year report by aggregate basis, each as its own chunk, retaining document identity and version |
Three common errors
- Treating the procurement quantity in the procurement ledger as the same as the sales quantity in the sales voucher. In the sample, procurement is 12,000 units and sales are 11,800 units; the fields differ and the counting bases differ, so numerical proximity does not make them consistent. To check, input both source texts, compare the procurement quantity field with the sales quantity field, and observe whether the two take the same value under the same period and the same subject.
- Treating the word "consistent" in the draft sentence as an already-verified conclusion. The draft sentence claims procurement and sales are both 12,000 units, while the sales voucher records 11,800 units, so the draft sentence and the source text differ in value. To check, input the draft sentence and both source texts, compare the draft sentence value with the source text value, and observe whether an unsupported statement is present.
- Treating a retrieval result's relevance score as a basis for permission or entity filtering. Minimum relevance filters low-relevance results only; it does not exclude unrelated business or unauthorised records. To check, input the same query, compare returned content at different relevance thresholds, and observe whether irrelevant material still appears in the results.
How to verify the setup
- Input the procurement ledger source text and the sales voucher source text from the hypothetical sample, compare the procurement quantity field with the sales quantity field, and observe whether the two take the same value under the same period and the same subject. If the values differ, the "consistent" statement in the draft sentence is not supported.
- Input the draft sentence "In the first half of 2025 this store's plastic bag procurement quantity and sales quantity were consistent, both 12,000 units", compare the draft sentence value with the 11,800 units in the sales voucher source text, and observe whether a value difference exists. If a difference exists, the draft sentence needs revision or a supplementary note.
- Input the query term "first half of 2025 plastic bag procurement quantity", compare whether the returned result contains the procurement ledger source text, and observe whether chunking retained the period and quantity fields. If the returned result is missing key fields, check whether the chunking method separated a condition from the content it constrains.
Where a query returns an empty result, the downstream design can be checked against a clearly labelled paper input of an empty list; this does not establish that a fictional rule always returns an empty list. Cross-model association cannot be performed jointly; check identifiers first, exclude those that do not match, and record what is actually observed rather than performing a cross-model retrieval to compare missed recall. The optional paths in the overall document flow do not mean every request executes all of them: image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled by configuration. Where material is absent or only related-stage evidence exists, the status remains unsupported or pending verification; it is not a negation or a conflict. A pending review does not demonstrate a lack of operating permission, and signing, reaching cooperation and contract effectiveness are three distinct facts.