What the documents look like
Retail plastic bag and single-use item reporting sits around single-use plastics reporting and environmental supervision, separate from membership marketing. The main data sources named in the brief are the Ministry of Commerce reporting system, POS plastic bag sales, the procurement system, and store ledgers. The most time-consuming steps are counting usage, checking sales vouchers, and reporting recovery status. Everyday documents include plastic bag procurement ledgers, sales vouchers, half-year reports, and self-discipline commitments.
For the inventory procurement and supplier materials task, materials usually appear as procurement ledgers, supplier qualification files, and sales vouchers. Parsing and chunking must retain the quantity, date, and supplier name in a ledger, together with the sales detail in a voucher. Half-year reports and daily sales data coexist, and their structures differ, so chunking has to follow each structure separately.
Constraints at the document chunking step
Procurement ledgers and supplier materials often appear as tables or line items. Paragraph splitting must keep the header bound to its row, the supplier name bound to the supplied category, and the date bound to the quantity range. Sales vouchers are mostly line-by-line records, and after chunking they should still point back to the original voucher number and date. Half-year reports and self-discipline commitments are summary or declaration materials, so chunking should keep their complete semantic unit and avoid separating a commitment condition from what it constrains.
Original data and retrieval indexes are distinct. One group of original data can map to multiple indexes. During retrieval, vectors are recalled first, then the original data is looked up in MongoDB by vector ID and merged, with the highest score taken for vectors that map to the same group of original data. Hybrid retrieval merges semantic and full-text results through RRF. Reranking requires a model and must be enabled by settings. Image retrieval requires image input and the corresponding model capability. These paths do not all run on every request. Semantic recall and relevance do not perform permission or entity isolation. The requirement that a retrieval library use the same vector model does not extend to external systems needing the same model. Libraries built on different vector models cannot be associated together; check identifiers and exclude those that do not match.
Configuration and implementation checks
The settings and checks below concern this page's document parsing and chunking task. Hypothetical drill sample: the original text of a store procurement ledger has three sentences — "On 1 March 2025, this store purchased 2,000 single-use plastic bags from Supplier A for fresh-produce packaging. On 5 March of the same month, it purchased 500 pairs of single-use gloves from Supplier B. Both purchases were recorded in store ledger number CG-202503." One draft sentence to be judged: "Supplier A's plastic bag procurement has passed the environmental compliance review." That draft sentence contains no review subject, review date, or review conclusion from the original text, so it is unverified and cannot be judged as passed.
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Chunk granularity | Chunk by single ledger record | Compare with whole-table chunking, check whether header and row stay bound |
| Reference limit | Set for this hypothetical trial to cover three records | Compare with a value covering one record, observe whether a summary question recalls completely |
| Minimum relevance | Set for this hypothetical trial to a medium value | Compare with a lower value, check whether low-relevance fragments are filtered |
| Search mode | Hybrid retrieval | Compare with semantic retrieval alone, check hits for number-type queries |
| Question optimization | Enabled | Compare with disabled, observe recall differences on short questions or follow-ups |
| Post-update review | Run a sample retrieval after upload | Compare with confirming upload success alone, check training and index state |
Three common errors
- Linking Supplier A's ledger record directly to the draft sentence "has passed the environmental compliance review." Input is the ledger original text and the draft sentence; compare whether both contain the same review subject, date, and conclusion; the observable result is that the original text states no review fact, so the finding should be unverified.
- Splitting the header from the data row after table chunking. Input is a ledger table with a header; compare whether the chunked result keeps the header-to-row correspondence; the observable result is that the row loses its field meaning and retrieval cannot point back to quantity and date.
- Treating upload success as retrieval already updated. Input is the upload operation record; compare whether a sample retrieval was run and training and index state checked; the observable result is that without running retrieval, new content cannot be confirmed as recallable.
How to verify the setup
- Input the three ledger sentences and the draft sentence; compare whether the draft sentence contains a review subject, date, and conclusion; the observable result is that the original text states none, so the finding is unverified and not passed.
- Input the chunked ledger table result; compare whether header and data row stay bound; the observable result is that the binding is retained and retrieval can point back to quantity and date, so the condition is met.
- Input a sample retrieval request after the update; compare whether the retrieval result contains the newly uploaded content; the observable result is that the sample retrieval hits the new content and the index state is updated, so the condition is met; when there is no hit or the state is unknown, record the gap.
An empty result can be checked against downstream design with an explicit paper input of []; this does not claim that a fictional rule retrieval necessarily returns []. A pending review does not show a lack of operating permission; signing, reaching cooperation, and contract effectiveness are three distinct facts. Authorization status must be recorded separately for material, operation, and request; retrieval or a form does not constitute authorization execution.