What the documents look like
Export tax rebate filing work happens after goods have left the country. The task is to match tax vouchers against what was actually exported, and the material set is stable: the export tax exemption and rebate filing form, the export customs declaration, purchase invoices, and foreign exchange receipt vouchers. These come from the electronic tax bureau, the export rebate filing system, customs electronic information, and the financial ledger. The people doing this work are trade enterprise finance staff, tax accountants, and documentation clerks.
For this page the task is contract and transaction material comparison at the document parsing and chunking step. That means the source materials are split into passages that can be traced back to the original text, then compared item by item on subject, object, period, and field. The consequence of getting this wrong is delayed rebate, denied rebate, or tax risk, so a chunk has to keep the relationship between a condition and the content that condition constrains. Data updates monthly and per batch. This page only checks actual training and index status after materials change, and does not infer batch synchronization or transaction-triggered mechanisms from that cadence.
Constraints at the document chunking step
Chunk completeness is checked against the structure of each specific material. A condition, its explanation, and the content it constrains stay together. Original data and retrieval indexes are separate concerns: one set of original data can map to several indexes, and the indexes field in dataset.datas records the corresponding vector IDs. Retrieval treats the library as the smallest search unit; a collection is only for classifying and managing data and does not affect search results.
Semantic retrieval depends on vector similarity and suits natural-language questions. Full-text retrieval depends on keyword hits and suits numbers, models, and proper nouns. Hybrid retrieval merges multiple result paths through RRF, and ReRank reorders candidate text and returns a 0-1 score. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used, and it is a filter threshold. Similarity and relevance do not perform permission or entity isolation, and retrieval itself is not authorization.
Hypothetical paper exercise: the source text reads, "Party A is a domestic foreign trade enterprise, Party B is an overseas buyer, and on hypothetical date March 2024 the two signed goods sales contract number HT-2024-03, agreeing on delivery in two batches, with the first batch quantity at a hypothetical value of 1,000 units and settlement currency in US dollars. On hypothetical date April 2024 Party A obtained the corresponding purchase invoice, which states a quantity of 1,000 units. The export customs declaration states the same batch quantity of 1,000 units." The draft sentence to be judged is, "The first batch quantity under this contract matches the quantities on the purchase invoice and the export customs declaration and can be used for this rebate filing comparison." This sample is on paper and has not been submitted or tested.
Configuration and implementation checks
| Configuration or check | Relevant value or action on this page | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, or hybrid retrieval | Contract number and quantity fields are better located with full-text or hybrid retrieval |
| Reference limit | Each search references at most n tokens of content | Controls how much reference content reaches the model and keeps unrelated passages out of the comparison |
| Minimum relevance | A 0-1 value, effective only under semantic retrieval or result reranking | Filters low-relevance results and does not perform subject or permission filtering |
| Result reranking | Runs when a ReRank model is available and enabled by configuration | Reorders candidate text, and the 0-1 score can be used for filtering |
| Question optimization | Coreference removal and question expansion before retrieval, adding one model call | Can be enabled for multi-turn follow-ups; for short questions and exact numbers, decide by observed effect |
| Index check | Distinguish original data from indexes and confirm the corresponding index was built at ingestion | After materials change, check actual training and index status; a successful upload does not mean retrieval is updated |
Three common errors
- Treating a similarity score as subject or permission filtering. The input is the three chunked passages from the contract, invoice, and customs declaration in the sample. The comparison object is the subject and quantity fields in each. If a leading vector score alone is used to conclude that all three materials belong to the same transaction, the subject field has not been checked and the conclusion does not hold.
- Treating minimum relevance as an entity isolation mechanism. The input is the contract passage from the sample and one unrelated material passage. The comparison object is the subject field in each. Inferring that the unrelated material does not belong to this enterprise because its relevance falls below the threshold misuses a filter threshold as a permission judgment.
- Treating a successful upload as an updated index. The input is the upload action after the sample materials changed. The comparison object is the actual training state and the index record. Without an actual observation of completed training or a generated index, whether retrieval covers the new content remains unknown.
How to verify the setup
- Chunk completeness check. The input is the three source sentences of the sample material. The comparison object is whether each parsed passage retains subject, object, quantity, and period. The condition is met when every passage can be traced back to its corresponding source sentence and no condition is separated from the content it constrains.
- Field consistency check. The input is the draft sentence from the sample and the three material passages. The comparison object is the quantity value of the same field. The draft sentence is supported when all three materials state 1,000 units and the subjects point to the same transaction. If one material does not state a quantity, that item remains to be verified and is not judged as a contradiction.
- Retrieval observation and failure branch. The input is contract number HT-2024-03 and one natural-language question, each run once. The comparison object is whether the returned reference content contains the corresponding passage. The expectation is that the number query hits the contract passage. If the return is empty or contains only unrelated passages, record that observation and check chunking and index status. First-position hits and full recall are not guaranteed.
An empty-result handoff can use the paper input [] to check downstream design, without claiming that a fictional rule search necessarily returns an empty array. Libraries using different vector models cannot be associated together; check identifiers first, exclude those that do not match, and do not run cross-model retrieval to compare missed recalls. A pending review does not show that operating permission is absent. Signing, reaching agreement, and contract effectiveness are three distinct facts. The sample states no authorization or effectiveness clause, so the related evidence gap remains open.