What the documents look like
This page covers document parsing and chunking for inventory procurement and supplier records within foreign-exchange receipt, settlement and trade directory work. The material set for this category includes foreign-exchange receipt slips, trade contracts, invoices and foreign-exchange business registration records. The data sources named in the brief are State Administration of Foreign Exchange policy, bank online banking, enterprise financial ledgers and customs export data. The typical roles handling this material are foreign-trade finance staff, bank relationship managers and foreign-exchange specialists. Errors at this stage can lead to bank rejection of documents, abnormal foreign-exchange classification and funds that cannot be credited.
For parsing and chunking, these facts mean that the semantic fields of receipt counterparty, contract object, invoice amount and registration-record status need to survive segmentation. A receipt slip and a contract are separate documents with separate identity, and a chunk that carries the amount while dropping the payer or payee cannot support later reconciliation. The brief records that data updates occur per transaction and monthly. Where an update frequency does not affect this page's function, it is not treated as a trigger for any synchronization behavior.
Constraints at the document chunking step
Chunking follows the structure of the specific record so that conditions, notes and the content they constrain are not split apart. In the documented storage model, one set of original data can map to multiple vectors. The original vector data is stored in the MongoDB dataset.datas table, whose indexes field records the corresponding vector IDs as an array. Vectors are stored in the PostgresSQL vector field, with the HNSW index used for retrieval. Retrieval recalls vectors first, then uses vector IDs to find original data in MongoDB; when records correspond to the same original data they are merged, and the vector score takes the highest score.
The minimum relevance value is a 0–1 filter that takes effect only in semantic retrieval or when result reranking is used. It is a similarity threshold and does not perform permission or entity isolation. The reference upper limit controls the amount of cited content by tokens rather than by top k. The RRF process merges results from multiple recall paths; it is a fusion step, not a switch. Reranking requires a ReRank model and must be enabled according to settings. Image retrieval requires image input and matching model capability, and does not run on every request.
Hypothetical paper exercise sample. A foreign-trade company finance staff member receives an export payment. The material text reads: "Foreign-exchange receipt slip: payer Company A, payee Company B, amount USD 50,000, date 10 March 2025." "Trade contract: seller Company B, buyer Company A, goods are inventory electronic components, contract number HT-2025-0310." The draft sentence to be assessed is: "This receipt corresponds to contract HT-2025-0310, the parties match, and settlement can be processed." The question is whether the draft sentence is supported by the material.
Configuration and implementation checks
| Configuration item | Setting or check relevant to this page | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval or hybrid retrieval | The sample contains the identifier HT-2025-0310, so full-text and semantic recall can be compared |
| Question optimization | Coreference resolution and question expansion can be enabled | The sample question contains "this receipt", so subject and object need completion |
| Minimum relevance | A 0–1 value, effective only in semantic retrieval or with reranking | Used to filter low-relevance fragments, not to exclude unauthorized records |
| Reference upper limit | Controls cited content by tokens | The sample text is short; used to observe whether subject and amount are carried in full |
| Result reranking | Requires a ReRank model and must be enabled per settings | Used for second-pass ordering when candidates are numerous; the sample allows before-and-after comparison |
| Segmentation method | Split by record structure, preserving conditions and constraints | The slip and the contract should each form their own segment so party fields are not split apart |
Three common errors
- Treating a successful upload as retrieval already updated. Input: the sample receipt slip is uploaded and retrieval is run immediately. Compare the actual training and index status, and observe whether retrieval returns the slip content. If training has not completed, retrieval may not return it, and this is recorded as pending verification rather than as a confirmed absence.
- Treating minimum relevance as a permission or entity filter. Input: the sample draft sentence is used for retrieval. Compare whether returned content includes both Company A and Company B as parties, and observe whether unrelated parties appear. A relevance score does not carry the function of excluding unauthorized records.
- Treating a pending or draft sentence as an approved conclusion. Input: the draft sentence "settlement can be processed". Compare whether the material states a settlement authorization or approval status, and observe whether an authorization field exists. When it is not stated, record the evidence gap as unsupported; do not judge it as a denial or a conflict.
How to verify the setup
- Input the sample receipt slip text and contract text. Compare whether each segment after chunking preserves payer, payee, amount, date and contract number, and observe whether party and object fields remain complete. If fields are split apart, the segmentation method needs adjustment.
- Input the sample draft sentence. Compare whether retrieved content includes HT-2025-0310 and both parties, and observe whether mutually exclusive values appear for the same party, same period and same field. When authorization is not stated, record the evidence gap. A judgment of contradiction requires the same party, same period and same field with clearly mutually exclusive values; related-stage evidence alone remains unsupported or pending.
- Input an empty-result downstream scenario using the paper input [] to check the downstream design. Compare whether the downstream treats the empty result as pending verification, and observe whether any automatic business action occurs. Retrieval does not perform authorization, approval or database writes. A pending request is not an approval, and an offered option is not execution.