What the documents look like
Cross-border platform settlement and foreign-exchange work draws on platform settlement centers, bank statements, payment provider back offices, and ERP orders. The recurring documents are platform settlement statements, exchange-rate tables, service-fee invoices, and collection reconciliation sheets. These materials update on weekly and monthly cycles.
A settlement statement typically carries order numbers, settlement currency, deduction detail, and the settlement period. An exchange-rate table records currency pairs with their effective intervals. A service-fee invoice states the charging entity, the amount, and the invoicing status. A collection reconciliation sheet maps bank credits to platform disbursements line by line. The language of these materials shifts with the site, so the same movement of funds can appear under different field names across Chinese and English documents. Document parsing and chunking operates on these multilingual, multi-table, cross-period raw materials, and the chunking result directly affects whether later retrieval can locate the correct order and the correct period.
Constraints at the document chunking step
Chunking follows the structure of the material itself. A condition, its explanation, and the content it governs stay in one data block. The deduction rule in a settlement statement stays with the orders it applies to. The effective interval in an exchange-rate table stays with its currency pair.
Raw data and retrieval indexes are distinct. One group of raw data can correspond to multiple indexes, and increasing the number of indexes does not change the raw data content. Multilingual retrieval depends on the vector model; semantic retrieval can match across languages, while exact queries such as numbers and model designations suit full-text retrieval or hybrid retrieval. Hybrid retrieval fuses multiple recall paths through RRF, then applies ReRank for a second ordering, and the final set is trimmed by similarity filtering and the reference token limit.
Retrieval does not perform authorization judgment. A similarity score reflects content relevance and does not constitute admission for a subject or a period. A relevance threshold is a filtering value, not a final ordering rule; after question optimization, hybrid retrieval, image retrieval, or result reranking is enabled, the final result may pass through multi-path fusion and need not follow a single vector similarity ordering.
Configuration and implementation checks
The settings below concern this page's sample and list only documented settings or implementation checks.
| Configuration item | Hypothetical trial starting point | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains order numbers and natural-language questions, so exact and semantic recall are both needed |
| Minimum relevance | 0.4 | Hypothetical starting value, compared against recall volume with the value left unadjusted |
| Reference limit | 2000 tokens | Hypothetical starting value, estimated from settlement statement chunk length and compared against the actual sample chunk length |
| Question optimization | Off | The sample question has a complete subject and predicate; enabling it adds one model call |
| Result reranking | Enabled | The candidate set is large and requires filtering by the rerank score |
| Chunking basis | Material structure boundary | Compared against fixed-length splitting, to check whether conditions stay with their governed content |
Three common errors
- Treating a successful upload as an updated retrieval state. After a material changes, the actual training and index state must be checked; a completed upload does not mean the index has been rebuilt.
- Treating a similarity score as a permission or entity filter. The score reflects content relevance only and cannot exclude unrelated subjects or records from unauthorized periods.
- Describing RRF in hybrid retrieval as a switch that can be turned on. RRF is a fusion process, not an independent configuration item.
Corresponding checks:
- Input a modified exchange-rate table, compare the upload time against the index state field, and treat the material as retrievable only when the index state shows it has been updated.
- Input a query involving another subject, compare the returned content against the query subject, and note that a mismatched record indicates the filter did not take effect.
- Inspect the configuration interface for a switch named RRF; when no such switch exists, confirm that fusion runs through the default hybrid retrieval process.
How to verify the setup
Hypothetical paper exercise sample. The material text reads: "Settlement statement SC-2024-07, subject: Xinghai Trading, period: July 2024, currency: USD, total deductions 128.50 USD, status: settled." The proposed judgment sentence reads: "The July 2024 settlement statement for Xinghai Trading has completed settlement." The sentence shares the subject, period, and field with the source text, and the status value matches, so it can be judged as supported.
- Verification one: input the source text above and the proposed judgment sentence, compare the subject, period, and status fields, and treat the condition as met when all three agree and the status is settled in both.
- Verification two: input the query "SC-2024-07 deduction amount", compare whether the returned block contains that number and amount, and treat the condition as met when the returned content contains the complete number and 128.50 USD.
- Verification three: input an empty reference result, check whether the downstream design handles it as no reference, and do not infer that a fictional rule must return an empty array.
When retrieval returns nothing, record it as unsupported and preserve the evidence gap. After a material changes, review the training and index state again.