What the documents look like
Regional dealer stock replenishment runs as a daily rolling order flow rather than a seasonal buying meeting. The working materials are replenishment requests, inventory turnover sheets, outbound delivery notes and payment collection plans, updated weekly or monthly. A single replenishment request may carry Chinese and English product names, model numbers, batch identifiers and quantities side by side, and the same request can show both a dealer internal code and a brand material code.
Document parsing and chunking has to absorb these structural differences. In an inventory turnover sheet, the safety stock explanation, the replenishable quantity and the arrival condition are frequently written inside one paragraph. The model field of an outbound delivery note and the amount field of a payment collection plan belong to different materials and must not be merged into one data block. In cross-border work the same model number is written differently across language versions, so chunking keeps the original identifier in place; only then can retrieval point back to a specific document.
Constraints at the document chunking step
Chunking follows the structure of each material. The conditional sentence in a replenishment request stays in the same data block as the quantity and date it constrains, so that a condition and its conclusion are not split apart. Original data and retrieval index are two layers: one group of original data can correspond to multiple indexes. In MongoDB the dataset.datas table stores the original data information, and the indexes field records the corresponding vector IDs. In PostgresSQL a vector field stores the vector. During retrieval the vector is recalled first, then the original data is located in MongoDB by vector ID; when several vectors map to the same group of original data, the results are merged and the highest score is taken.
Cross-language retrieval depends on the cross-multilingual understanding of semantic search. Precise lookups of codes and model numbers suit full-text search or hybrid search. Hybrid search combines multiple result paths through RRF, and reranking requires a model and must be enabled according to the configuration. Image retrieval requires an image input and the corresponding model capability, and does not run on every request.
Configuration and implementation checks
The settings below concern this page's sample and list documented options or implementation check items.
| Configuration item | Assumed trial starting point for this sample | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid search | The sample contains model numbers and natural-language questions; compare recall coverage against semantic search alone |
| Citation limit | 3000 tokens assumed as the starting point | Sample paragraphs are short; compare against 1500 tokens to see whether the full conditional sentence is covered |
| Minimum relevance | 0.4 assumed as the starting point | Compare against 0.2 to observe whether low-relevance fragments are included |
| Question optimization | Assumed enabled | The sample contains multi-turn follow-ups; compare against the disabled state for completeness of the search terms |
| Result reranking | Assumed enabled | Compare ranking position against the non-reranked state when candidates are numerous |
| Index check | Manual review of the indexes field | Confirm that the multiple vectors belonging to one group of original data have been created |
Three common errors
- Treating a successful upload as an updated retrieval state. Uploading and training or indexing are separate stages. The actual training and index state must be checked; a successful upload does not by itself show that retrieval results have changed.
- Treating a similarity score as a permission or entity filter. Minimum relevance only filters low-relevance results and does not exclude unrelated businesses or unauthorized records. Retrieval itself does not perform authorization decisions.
- Associating libraries built on different vector models. Cross-model association is not possible. Identifiers must be checked first and non-matching libraries excluded; a cross-model retrieval run must not be executed to compare missed recalls.
How to verify the setup
Hypothetical paper exercise sample: one replenishment request reads "Dealer A applied on 10 March 2025 to replenish model X200, quantity 400 units, required to arrive at the warehouse before 25 March; this application is pending review by the brand channel manager." A separate inventory turnover sheet reads "Model X200 currently has a replenishable ceiling of 300 units; the excess must be arranged next month." The proposed sentence is "The replenishment of 400 units of model X200 has been approved and will arrive at the warehouse before 25 March." The judgment requires the same subject, the same period and the same field: the source states pending review, the proposed sentence states approved, and the two are mutually exclusive values on the same status field, so the proposed sentence is unsupported.
- Input the proposed sentence and the two source texts; compare the subject, model, quantity, date and review status fields. The source shows pending review and the proposed sentence shows approved, so the observed result is a mutually exclusive status field and the proposed sentence is unsupported.
- Input the search terms "X200 replenishment ceiling" and "X200 replenishment application review status", and run semantic search and full-text search separately; compare whether the returned fragments contain the replenishable ceiling and the pending-review status. If the fragments contain the corresponding conditional sentences, chunking has kept condition and conclusion together; if a conditional sentence and its quantity fall into different blocks, record the chunking as needing adjustment.
- Input an empty-result handover scenario, using the explicit paper input [] to check the downstream design, and observe whether the downstream handles it as an empty result. Do not claim that a fabricated rule search necessarily returns []; record only the actually observed return state and the gap.
This page covers only regional dealer stock replenishment, cross-border materials and multilingual retrieval, and document parsing and chunking. Which materials, operations and requests sit at which authorization state during an operational handover cannot be treated as authorization execution by retrieval or by a form.