What the documents look like
Retail packaging and single-use item reporting centers on disposable plastic product reporting and environmental supervision. The main data sources are the Ministry of Commerce reporting system, point-of-sale plastic bag sales, the procurement system, and store ledgers. Daily documents include plastic bag procurement ledgers, sales vouchers, semiannual reports, and self-discipline commitments. This category differs from cashier membership promotion because its purpose is regulatory submission. Errors can lead to rectification within a deadline, fines of ten thousand to one hundred thousand yuan, and regulatory interviews. Product material and selection questions need to retrieve procurement specifications, sales records, and reporting wording from these documents. The vector model and index determine whether retrieval can hit the correct original text.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Check the actual model and input type first. One original data block can be linked to multiple indexes, and the index and original content are checked separately. Index content is built from complete and focused information, and retrieval coverage and index length are weighed against samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion, and the query side alone is not sufficient. After adjusting the model or index, compare cited original text using the same set of questions, and do not promise results based on vector scores alone. Hybrid retrieval merges multiple result paths through RRF, reranking requires a model and must be enabled according to settings, and the minimum relevance value takes effect only in semantic retrieval or result reranking. Retrieval is a recall process and does not perform authorization decisions.
Hypothetical paper exercise sample: a store ledger records "March 2025 procurement of vest-style plastic bags, 200 bundles, specification 45cm×55cm, thickness 0.025mm." A sales voucher records "plastic bag sales revenue from March 1 to 31, 2025: 1,860 yuan." The draft sentence is "the store's March 2025 plastic bag procurement quantity of 200 bundles has passed headquarters compliance review." The question is whether this draft sentence is consistent with the ledger and voucher original text. The review status is not stated in the original text.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Comparison method and reason |
|---|---|---|
| Vector model | Same model identifier | Associated knowledge bases must use the same vector model; check the actual model and input type first |
| Index content | Ledger, voucher, and report chunked into indexes | Build indexes from complete and focused information; weigh retrieval coverage and length against samples |
| Image index | Generate image description or image vector at ingestion | Image retrieval depends on ingestion indexes; the query side alone is not sufficient |
| Search mode | Hybrid retrieval | Semantic recall and full-text recall are merged through RRF; numbered questions can be matched |
| Minimum relevance | Hypothetical trial starting value 0.3 for this exercise | This value takes effect only in semantic retrieval or result reranking; adjust by comparing against the same set of questions |
| Citation limit | Controlled by tokens | The document uses tokens as the citation limit unit, not top k |
Three common errors
- Treating retrieval similarity as an authorization decision. The input is the draft sentence "has passed headquarters compliance review," the comparison object is the ledger and voucher original text, and the observable result is that the original text has no corresponding field and the draft sentence is unsupported.
- Associating knowledge bases with different vector models together. The input is two knowledge bases with different model identifiers, the comparison object is the model identifiers, and the observable result is that the identifiers do not match, so the nonconforming one is excluded and recorded, and cross-model retrieval is not executed to compare missed recalls.
- Writing RRF as an on/off switch. The input is the search mode setting, the comparison object is the document description, and the observable result is that RRF is a hybrid result fusion process, not an independent switch.
How to verify the setup
- Input the ledger original text and the draft sentence from the hypothetical sample, compare the procurement quantity field and the review status field, and if the ledger records only the procurement quantity while the review status is not stated, the draft sentence is unsupported and the evidence gap is recorded.
- Input two knowledge bases with different vector model identifiers, compare the model identifiers, and if the identifiers do not match, exclude the nonconforming one and do not execute cross-model retrieval.
- Input an empty result citation array [], compare the downstream link design, and if the output link runs through with a citation length of 0, empty result handling is usable, without claiming that a fictional rule retrieval will necessarily return [].