What the documents look like
Platform multi-store matrix management draws on four document families: store authorization lists, price control tables, cross-territory complaint tickets, and monthly sales reports. Sources sit in each platform's merchant backend, ERP channel pricing, brand anti-counterfeit systems, and distributor reports. A single product record often constrains several stores at once, so authorization scope, price floor, and supply target are fields that reference each other. Materials update daily or monthly, and the update action itself does not trigger any synchronization on the retrieval side.
For document chunking, this means a chunk must keep the document identity and version that allow it to be traced back to the source. Paragraph splitting must avoid separating an authorization condition or a price range from the object it constrains. Raw data and retrieval indexes are two layers: one raw data group can map to multiple vector indexes, and a change to an index does not change the raw data. At retrieval time, vectors are recalled first, then the vector ID is used to fetch raw data from MongoDB dataset.datas; when the same raw data group is hit on multiple paths, results merge and the vector score takes the highest value. A retrieval result represents similarity only and does not constitute an authorization determination.
Assume a paper exercise sample. The source text is: "Authorization list A-2024-07: stores S-101 through S-108 belong to the East China authorization scope, valid from July 1, 2024 to December 31, 2024; price control table P-07 states that the retail price floor for S-101 through S-108 is 199 yuan; cross-territory complaint ticket C-0912 records that S-107 sold the designated product at 179 yuan on September 3, 2024." The draft sentence to be judged is: "S-107 applied the retail price in the price control table during the valid authorization period." The comparison object is the same subject S-107, the same date September 3, 2024, and the same field retail price: the source shows 179 yuan, the floor is 199 yuan, and the draft sentence is not supported. Authorization scope and complaint record are two separate fields; whether the complaint is substantiated is not stated in the source and remains to be verified.
Constraints at the document chunking step
Chunking must preserve the document identity and version that allow each segment to be traced to its source. A paragraph boundary that splits an authorization condition from its constrained store list, or a price range from the product it applies to, breaks the ability to compare fields at retrieval time.
The raw data layer and the index layer must be treated separately. One raw data group can correspond to multiple vector indexes. A change on the index side does not alter the raw data. During retrieval, the vector is recalled first, then the vector ID is used to look up the raw data in MongoDB dataset.datas. When the same raw data group is hit on several paths, the results merge and the vector score takes the highest value.
Retrieval output reflects similarity only. It does not act as an authorization filter, an entity filter, or a permission check. The minimum relevance value is a filtering threshold and does not exclude unauthorized stores. A relevance score is not a category, entity, or permission filter, and retrieval is not authorization.
Configuration and implementation checks
| Configuration or check item | Assumed starting point for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Materials contain both identifiers and natural-language phrasings; compare semantic recall hits against full-text recall hits |
| Reference upper limit | 3000 tokens | Assumed starting point for this exercise only; compare reference content before and after trimming against the actual referenced amount before trimming |
| Minimum relevance | 0.35 | Assumed starting point for this exercise only; used to observe filtering during semantic retrieval or rerank; compare candidate counts before and after filtering |
| Question optimization | Enabled | Follow-up questions contain references; compare the retrieval query and recalled content before and after enabling |
| Result rerank | Enabled according to settings | Second-pass ordering when candidates are numerous; compare rerank score against vector score |
| Segmentation check | Manual review against material structure | Inspect whether authorization conditions and price ranges sit in the same segment as the object they constrain |
Three common errors
- Treating a successful upload as an updated retrieval index. An upload produces raw data only; training and index status must be checked separately. Compare the material change time against the actual index status; when the two do not match, the result is unknown.
- Treating a similarity score as an authorization or entity filter. The minimum relevance is a filtering threshold and does not exclude unauthorized stores. Compare the recalled content against the subject in the authorization list.
- Treating the RRF fusion in hybrid retrieval as a toggle. RRF is a multi-path result fusion process. Compare the candidates from each path against the fused ordering, and do not assume every request traverses all paths.
How to verify the setup
- Input the source text and the draft sentence given above, and compare the three fields S-107, September 3, 2024, and retail price. The condition is satisfied when retrieval returns the price control table source text and the draft sentence is marked as not supported.
- Input a fictional empty reference list []. Compare against the downstream reference trimming design. The condition is satisfied when the output is empty and no business action is produced. Do not assume that a fictional rule retrieval necessarily returns [].
- Input the same identifier question and run semantic retrieval and full-text retrieval separately, then compare the hits. When the identifier hit depends on the full-text path, record the observation. Do not guarantee a first-position hit or complete recall.
These checks observe behavior and failure branches; they do not claim that any test, deployment, approval, or refund has occurred.