What the documents look like
Cross-border platform settlement and foreign-exchange work concerns the collection of funds from completed orders. It does not handle fraud chargebacks, which belong to payment risk control. The data sources named in the brief are the platform settlement center, bank statements, payment institution back office, and ERP orders. The everyday documents are platform settlement statements, exchange-rate tables, service-fee invoices, and receipt reconciliation tables. Typical practitioners are cross-border seller finance staff, tax accountants, and platform operations staff. Errors land on profit calculation, tax filing deviation, and cash shortage.
Within the inventory procurement and supplier materials task, the objects of parsing and chunking are procurement contracts, supplier reconciliation statements, and goods-receipt notes. The brief gives weekly and monthly update rhythms. Those rhythms state an update cycle only. They do not establish batch synchronization, full synchronization, or transaction-triggered behavior. Parsing and chunking must keep a document identity and version that can be traced back to the source text, and must check paragraphs against the actual structure of each material so that a condition, an explanation, and the content it constrains are not split apart.
The confidence level in the brief is marked as broadly so. That marking is a background condition, not a verified operational fact.
Constraints at the document chunking step
In FastGPT, a knowledge base consists of a library, a collection, and data. The smallest search unit is the library. A collection only classifies and manages data and has no effect on search results. Vector storage uses the PostgresSQL PG Vector plugin with an HNSW index. MongoDB stores the original data in the dataset.datas table, and an indexes field records the corresponding vector IDs, which is an array, so one group of data can map to multiple vectors. At retrieval time the system recalls vectors first, then looks up the original data in MongoDB by vector ID. If entries correspond to the same group of original data they are merged, and the vector score takes the highest value.
Semantic retrieval and the minimum relevance value affect only the ordering and filtering of similar content. They do not perform permission isolation or entity isolation. The reference limit is measured in tokens. The minimum relevance value is a number from 0 to 1 and takes effect only under semantic retrieval or result reranking. Full-text retrieval suits exact queries such as numbers, models, proper nouns, and error codes. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process, not a switch that can be turned on. Image retrieval requires the corresponding image input and model capability. Reranking requires an available model and must be enabled according to settings. The optional paths shown in the overall document flow diagram do not mean that every request executes all of them.
A hypothetical paper exercise sample follows. A supplier procurement reconciliation statement carries the header "Example Supplier A" and covers the object "March 2025 procurement batch." The body states: "Total amount payable for this period is RMB 120,000, which includes a quality deduction of RMB 3,000; the deduction basis is the acceptance record dated 12 March 2025." A draft sentence is submitted for judgment: "This reconciliation statement has confirmed that there is no quality deduction for this period." The judgment compares the source wording "includes a quality deduction of RMB 3,000" with the draft wording "no quality deduction." The same subject, the same period, and the same field carry mutually exclusive values. The draft sentence conflicts with the source text and is not supported.
Configuration and implementation checks
The table below lists settings already explained in the official documentation, or implementation checks, tied to this page's sample. The values are hypothetical starting points for this exercise, with the reason for choosing each and the comparison object stated.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains a proper noun, "Example Supplier A," so full-text and semantic paths are both used; compare whether the two candidate sets cover the same original data |
| Reference limit | Hypothetical starting point of 3000 tokens | The sample statement paragraphs are short; this value is chosen to hold a complete paragraph, and is compared with a smaller value to see whether a conditional sentence is truncated |
| Minimum relevance | Hypothetical starting point of 0.3 | Used to filter low-relevance results; compared with a higher value to see whether a paragraph containing the deduction condition is missed |
| Question optimization | Not enabled for now | The sample question has a clear subject; enabling it adds one model call, so retrieval time and recall differences are compared before and after |
| Result reranking | Not enabled for now | With few candidates, the original ordering is observed first, then compared with the ordering after enabling to see whether paragraph order changes |
| Index check | Inspect the indexes field | Confirm the sample original data has generated a vector index; compare retrieval before and after training to see whether the same group of data is hit |
Three common errors
- Treating the draft sentence "This reconciliation statement has confirmed that there is no quality deduction for this period" as the source conclusion. The source states a quality deduction of RMB 3,000 is included. The same field carries mutually exclusive values, so the draft sentence is not supported. The check supplies the source text and the draft sentence, compares whether subject, period, and field match, and observes whether a conflict appears.
- Treating a successful upload as an updated retrieval state. After material changes, the actual training and index state must be checked. The check supplies the updated sample, compares whether the indexes field has generated the corresponding vector ID, and observes whether retrieval hits the new content.
- Treating the minimum relevance value as a permission or entity filter. That value only filters low-relevance results. The check supplies the sample question, compares whether returned content still contains paragraphs about other suppliers, and observes whether out-of-scope content appears.
How to verify the setup
- Supply the sample source text and the draft sentence, compare "includes a quality deduction of RMB 3,000" with "no quality deduction" for the same subject, same period, and same field, and observe whether a conflict is determined.
- Supply "Example Supplier A" and "March 2025 procurement batch" from the sample, run full-text retrieval and semantic retrieval separately, compare whether the two candidate sets point to the same group of original data, and observe whether the merged score takes the highest value.
- Supply the updated sample, compare the indexes field and retrieval results before and after training, and observe whether the new content is recalled. If the return is empty, use the explicit paper input [] to check the downstream design, without claiming that a fictional rule necessarily returns []. Libraries built on different vector models cannot be associated together; check identifiers only, exclude non-matching entries, and record the actual observation.