What the documents look like
Export tax rebate filing work happens after goods have left the country, and it matches tax vouchers against export records. The working materials are the export tax exemption and rebate filing form, the export customs declaration, purchase invoices, and foreign exchange receipt vouchers, refreshed monthly or per batch. Product data and selection questions in this setting involve product names, specification models, units of measure, supplier fields, and voucher fields. Retrieval terms frequently carry item numbers and proper nouns. These fields sit close together in meaning while differing only slightly in surface form, so vector distance can blend distinct products together. Index content needs to stay focused for that reason.
Constraints at the vector indexes step
Knowledge bases that are associated for retrieval must use the same vector model. Confirm the actual model and its input type first; bases on different models cannot be associated together. Check the identifier and exclude the ones that do not match. A single source data block can be linked to multiple indexes, and the indexes and the original content are checked separately. Build index content from complete and focused information; retrieval coverage and index length are weighed against sample material. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, so the query side alone does not settle it. After any model or index adjustment, compare cited original text using the same group of questions. Do not promise an outcome based on a vector score alone. Semantic recall and relevance do not carry permission or entity isolation; retrieval itself is not authorization.
Hypothetical paper exercise sample. The material text supplied for checking has three sentences: "A foreign trade enterprise finance staff member organized a batch of export goods materials in March 2025. The export customs declaration states the product name as 'stainless steel insulated cup', specification model 'SJ-500', quantity one thousand two hundred units. The purchase invoice states the product name as 'insulated cup', specification model 'SJ-500', quantity one thousand two hundred units." The draft sentence to be judged is: "The customs declaration and the purchase invoice for this batch agree in the product name field." This sample is for paper checking only; the subject, object, period, and fields are all given.
Configuration and implementation checks
| Configuration or check item | Value or practice on this page | Basis and comparison method |
|---|---|---|
| Vector model consistency | Check the actual model identifier and input type | Associated knowledge bases must share one vector model; exclude cross-model ones |
| Index and source data | One source data block linked to multiple indexes | Indexes and original content are checked separately |
| Index content length | Hypothetical trial starting point: one complete and focused passage | Compare cited original text against the same question group; weigh coverage against length |
| Search mode | Choose semantic, full-text, or hybrid by retrieval term type | Item number and model questions lean toward full-text or hybrid |
| Minimum relevance | Hypothetical trial starting point: a middle value | This value takes effect only under semantic retrieval or result reranking |
| Citation cap | Counted in tokens, not item count | Chunk lengths vary widely in mixed knowledge bases |
Three common errors
- Treating retrieval as authorization. The input is one retrieval request; the comparison object is the operating permission state; the observable result is whether returned cited content and permission have been conflated. Returned content does not mean the subject has been authorized to file.
- Associating knowledge bases across vector models. The input is two bases with different model identifiers; the comparison object is the model identifier; the observable result is whether the two are excluded. Cross-model association is not allowed; check the identifier only.
- Treating a vector score as a business conclusion. The input is one high-similarity citation; the comparison object is the original voucher field; the observable result is whether the cited original text and the draft sentence share subject, period, and field. A score does not replace field checking.
How to verify the setup
- Using the hypothetical sample original text, input the draft sentence "The customs declaration and the purchase invoice for this batch agree in the product name field", compare the customs declaration product name "stainless steel insulated cup" with the purchase invoice product name "insulated cup". The two differ in surface form, so the draft sentence is not supported and remains pending verification.
- Input one retrieval request, compare the returned citations against the target retrieval scope, and observe whether the citations fall within a scope the operator may access. An empty result can be checked against downstream design using a paper input of an empty array; do not claim that a given rule always returns an empty array.
- Input the same question group, compare the cited original text before and after a model or index adjustment, and observe whether the cited content changes. The expectation is that cited original text can be compared sentence by sentence; do not promise a first-hit result or complete recall.