What the documents look like
Wholesale stall trading is more fragmented than regional dealer replenishment. Bargaining happens on site, and goods move by consignment. The records that matter for cross-border material and multilingual retrieval are purchase notes, handwritten sales slips, stall lease contracts, and payment receipts. These are handled by different people, so formats are not uniform. One consignment may carry a Chinese product name, a foreign-language consignment note, and a handwritten amount at the same time.
Document parsing and chunking must first preserve a document identity and version that can be traced back to the original text, then split by the structure of each record type. The brief gives a daily and per-order update frequency for this category; that frequency does not by itself establish any bulk synchronization, full synchronization, real-time, or transaction-triggered mechanism, and none is described here. When a record is re-uploaded, the actual training and indexing state has to be checked separately. Upload success is not the same fact as retrieval having been updated.
Constraints at the document chunking step
Chunking has to avoid separating a condition, note, or qualifier from the content it constrains. A single data record can map to more than one index, and the original data has to be distinguished from the retrieval index. The documented storage structure keeps original data in the MongoDB dataset.datas table, where an indexes field records the corresponding vector IDs as an array. Vectors are stored in a vector field, and retrieval recalls vectors first, then looks up the original content by vector ID and merges records that map to the same original data, taking the highest score.
Semantic retrieval relies on vector distance. Cross-language understanding is a documented advantage, so a Chinese question can match an English knowledge item, but precision is affected by keywords and sentence completeness. Full-text retrieval suits numbers, models, and proper nouns. Hybrid retrieval combines semantic and full-text recall through RRF. Rerank requires a model and has to be enabled by configuration; it is not a step that runs on every request. Image retrieval likewise requires an image input and the relevant model capability.
Retrieval and similarity scores do not carry permission or entity isolation. A minimum relevance value is a filter threshold, not a final ranking rule, and it does not exclude unauthorized records. Libraries built on different vector models cannot be associated together; the identifier has to be checked and non-matching libraries excluded rather than queried across models to compare missed recall.
Configuration and implementation checks
Hypothetical drill sample for review, not a real record and not an executed test. Material text: "On 5 March 2024, stall A issued a handwritten sales slip to buyer B for 200 women's coats. The payment receipt states an unpaid balance of RMB 8,000. The stall lease contract runs to 31 December 2024." Draft sentence to be assessed: "Buyer B has settled all payments in full." This sentence and the unpaid-balance state recorded in the payment receipt take mutually exclusive values on the same field.
| Review item | Relevant setting or implementation check on this page | Relation to the sample |
|---|---|---|
| Segment completeness | Check paragraphs against the structure of each purchase note, sales slip, contract, and receipt | Check whether the payment state cited by the draft sentence is kept in the same segment as the receipt |
| Data and index | Distinguish original data from vector IDs recorded in the indexes field | Confirm the receipt original data and its index can be traced back |
| Search mode | Semantic retrieval, full-text retrieval, or hybrid retrieval | Search with "balance", "8000", and "unpaid" separately |
| Reference limit | Each search cites at most n tokens of content | For this drill, a hypothetical starting value of 2000 tokens, chosen to cover several short slips; compare against a smaller value to observe truncation |
| Minimum relevance | A 0-1 value, effective only in semantic retrieval or with rerank | Not used to exclude unauthorized records |
| Question optimization | Coreference resolution and question expansion before retrieval | Check the completed result for multilingual follow-up questions |
Three common errors
- Treating a successful upload as an updated index. Input: a re-uploaded receipt. Compare the upload state with the actual training and indexing state, and observe whether retrieval still returns the older version.
- Treating a similarity filter as a permission filter. Input: a stall record outside the permitted access scope. Compare the minimum relevance setting with the access scope, and observe whether unrelated or unauthorized content is returned.
- Treating a pending request as approved. Input: a handover request reading "please change the balance status to settled". Compare the request state with the authorization boundary, and observe whether a change is executed when no approval record exists.
How to verify the setup
- Input the sample material text and the draft sentence. Compare "unpaid balance of RMB 8,000" with "settled all payments in full" for the same subject, the same period, and the same field. A contradiction is found only when the two take mutually exclusive values on that one field. If the field is not stated, or only evidence of a related stage exists, the result is unsupported or pending verification, not a negation or a conflict.
- Input the term "balance", the number "8000", and the natural-language question "how much is still owed on this consignment". Compare whether each retrieval path returns content covering the receipt paragraph. When a path does not cover it, record the gap. Do not assume the first result is a hit, that all relevant content is recalled, or that an unrelated query returns an empty set.
- Input a paper-level empty result
[]. Compare whether the downstream design distinguishes an empty reference from unauthorized access and from untrained data. Do not assert that retrieval under a hypothetical rule necessarily returns[].
For the multilingual follow-up path, the check is to confirm that question optimization has produced a completed question before retrieval, and to record the completed result rather than assume it. Where a rerank model is not configured or not enabled, the reranked path is not part of the request, and the check records that state instead of a score. Where the library identifier shows a different vector model, the check records the mismatch and excludes that library; no cross-model retrieval is run to compare missed recall. Any change to a payment status, any approval, and any database write are downstream business actions that require their own stated implementation, and none is claimed here.