What the documents look like
Export tax rebate filing happens after goods leave the country, and the work is matching tax vouchers. The typical materials are the export exemption and rebate filing form, the export customs declaration, purchase invoices, and foreign exchange receipt vouchers. They come from the electronic tax bureau, the export rebate filing system, customs electronic information, and the financial ledger.
Cross-border material and multilingual retrieval deals with the same set of vouchers described under different languages and different field conventions. A commodity name and specification on a customs declaration often reads differently from the item name and unit of measure on a purchase invoice. The currency and amount on a receipt voucher must correspond line by line with the filing form. Retrieval has to handle this kind of cross-document, cross-language field matching, and the vector model and index configuration decide whether the same batch of material can be recalled consistently.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Check the actual model and the input type before associating them. One source data block can carry several indexes, and the index content is checked separately from the original text. Build index content from complete and focused information, and weigh retrieval coverage against index length using the sample at hand.
Image retrieval also depends on whether an image description or image vector index was generated at ingestion time, so the query side alone does not settle it. After a model or index change, compare quoted original text using the same set of questions. A vector score by itself does not support a promise about outcomes.
Semantic retrieval computes similarity from vector distance and supports cross-language understanding, so a Chinese question can match an English knowledge point. Full-text retrieval depends on keyword hits and suits exact lookups such as numbers, models, proper nouns, and error codes. Hybrid retrieval uses semantic and full-text recall together and merges results through RRF; the merged range is wide, so a ReRank pass is usually needed, with filtering by the rerank score. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used. It is a filter threshold and does not constitute the final ordering rule. The quote limit is controlled in tokens and does not use top k. The search unit is the library; a collection only classifies data for management. Retrieval itself does not perform authorization checks, and failing to retrieve a record does not mean that record is not permitted.
Configuration and implementation checks
The configuration below centers on a hypothetical drill sample. The sample source text reads: In March 2026 a finance staff member of a foreign trade enterprise handles the rebate filing for a batch of exported goods. The export customs declaration states the commodity name as "stainless steel cookware." The purchase invoice states the item name as "stainless steel tableware set." The foreign exchange receipt voucher shows the payment for this batch arrived in February 2026. The sentence to be judged is: "The commodity names on this purchase invoice and the export customs declaration are consistent and can be used directly for filing." The subject, object, period, and fields of that draft sentence all come from the source text above.
| Configuration item | Hypothetical starting point for this drill | Reason for the choice and comparison method |
|---|---|---|
| Vector model | Semantic retrieval associates this batch's material library under one vector model | When associating several knowledge bases, check the model identifier first and exclude those that do not match; do not run cross-model retrieval comparisons |
| Index content | Build focused indexes separately for the customs declaration commodity name and the purchase invoice item name | Compare quoted original text using the same set of questions, and observe whether recalled content covers both fields |
| Search mode | Recall first with hybrid retrieval, then enable ReRank according to the setting | Number and proper-noun queries depend on full-text recall; semantically close content depends on vector recall |
| Minimum relevance | Set to 0.3 as the starting point for this drill, compared against the score after reranking is enabled | The value takes effect only in semantic retrieval or result reranking; observe changes in quoted content after adjustment |
| Quote limit | Set to 2000 tokens as the starting point for this drill, compared against a smaller value | Quote volume is controlled in tokens; observe result stability across chunks of different lengths |
| Question optimization | Enabled for multi-turn follow-up scenarios, not enabled for a single clear question | This step adds one model call before retrieval; observe the effect on retrieval in continuous conversation |
Three common errors
- Treating a retrieval result as an authorization conclusion. The draft sentence says the commodity names on the purchase invoice and the customs declaration are consistent, while the source text only gives two different expressions and does not state that the two have been determined to be the same commodity. Compare the item name, specification, and unit of measure fields on the two materials, and observe whether the same field carries mutually exclusive values. When only stage-related evidence exists, record it as unsupported or pending verification.
- Forcing libraries under different vector models into one association. Knowledge bases associated for retrieval must use the same vector model. When model identifiers differ, exclude the ones that do not match and record the actual observation. Do not run cross-model retrieval to compare missed recalls, and do not infer the runtime result of a joint association.
- Mixing the quote limit with the thinking budget. The quote limit controls how much content each search quotes, measured in tokens. The thinking budget is a model reasoning parameter. Their fields and scope differ, so check them separately and do not substitute one for the other.
How to verify the setup
The three checks below all use the complete hypothetical source text and draft sentence given above. They state the input, the comparison object, and the observable result, and do not claim that testing has been performed.
- Input the draft sentence "The commodity names on this purchase invoice and the export customs declaration are consistent and can be used directly for filing," and compare against the "stainless steel cookware" stated on the customs declaration and the "stainless steel tableware set" stated on the purchase invoice. If the quoted content returned by retrieval contains both original expressions, the index covers both fields. If only one appears, record it as insufficient index coverage and add the corresponding index before comparing again.
- Input a filing record in the same batch that is explicitly marked as reviewed and approved, and compare the status field of that record against the authorization state the draft sentence relies on. If the status field in the retrieval result shows reviewed and approved, the record can be used for cross-checking. If the status field is missing or shows pending review, record it as an evidence gap and do not treat authorization as completed on that basis.
- Input a paper-constructed empty result quote array, and compare it against the downstream node's design for handling an empty array. If the downstream node still completes its output path when it receives a quote array of length 0, the empty-result handling matches the documented description. If the downstream node stops or reports an error, record it as a downstream implementation that differs from the documented description and check it separately.