What the documents look like
Central chain procurement and distribution operates around headquarters purchasing and the distribution center. The daily materials are centralized procurement contracts, replenishment plans, delivery notes and store requisition forms. The data comes from ERP purchasing, WMS, store POS and supplier delivery records. These materials point to the same batch of products while being filled in by different parties at different stages. A single product record may appear under a purchasing formulation in a contract and under a store formulation in a requisition form. Document parsing and chunking must place these formulations for the same product into passages that can be traced back to the source text, so that selection questions can cite the correct passage. Materials update daily or weekly. A successful upload only shows that a file entered the system; it does not show that training and indexing have completed.
Constraints at the document chunking step
The constraints concentrate on two actions: segmentation and indexing. Segmentation must follow the structure of the specific material, keeping the applicable store scope, the effective conditions and the constrained procurement quantity in the same passage, so that conditions are not separated from the content they govern. Indexing must distinguish original data from retrieval indexes. In MongoDB, the dataset.datas table stores original data, and the indexes field records the corresponding vector IDs. One set of data can correspond to multiple vectors, so the same product passage can carry multiple index groups. During retrieval, vectors are recalled first, then the vector ID is used to find the original data in MongoDB. If several vectors point to the same original data set, the results are merged and the highest vector score is taken. Semantic retrieval relies on vector distance, full-text retrieval relies on keyword hits, and hybrid retrieval merges the two paths through RRF. RRF is a fusion process, not a separate switch that can be turned on. The minimum relevance value takes effect only under semantic retrieval or result reranking; it is a filter threshold and does not perform permission or entity isolation. Retrieval itself does not constitute authorization.
Hypothetical paper exercise sample: the following is paper material and does not come from any real customer or system record. Source sentences: "This centralized procurement contract applies to all directly operated stores in the East China region, the procurement category is ambient dairy products, and the contract period is March 1, 2025 to February 28, 2026." "Store requisition forms are submitted by the store manager after the replenishment plan is confirmed, and the distribution center arranges delivery by wave." Draft sentence to be judged: "This contract covers the chilled dairy procurement of all franchise stores." The judgment points: the subject changes from directly operated stores to franchise stores, and the category changes from ambient dairy products to chilled dairy products. Both differ from the corresponding source field values, so the draft sentence is not supported by the source text.
Configuration and implementation checks
| Configuration or check item | Assumed starting point for this exercise | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | Product materials contain both codes and natural language descriptions; compare candidate coverage against semantic retrieval alone |
| Minimum relevance | 0.3 | Assumed starting point for this paper exercise only; compare candidate count against running without a threshold |
| Citation limit | 3000 tokens | Assumed starting point; controlled by tokens; compare citation completeness against 1500 tokens |
| Question optimization | Enabled | Selection follow-up questions often contain references; compare retrieval term completeness against disabled |
| Result reranking | Enabled | Hybrid retrieval produces many candidates; compare ranking position against no reranking |
| Segmentation check | Manual review of each document | Check whether conditions and the content they govern stay in the same passage; compare against treating the whole document as one passage |
Three common errors
- Treating a successful upload as an updated index. Upload only produces a file record; training and index status must be checked separately. The error is inferring retrieval availability from file existence.
- Treating minimum relevance as a permission filter. The value is a 0-1 relevance threshold that only affects whether low-relevance results are kept. The error is inferring that unrelated business records are excluded from a similarity setting.
- Jointly associating libraries that use different vector models. Cross-model joint association is not possible. The error is concatenating retrieval without checking identifiers. The correct step is to check identifiers first, exclude those that do not match, and record the actual observation.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence, then compare the subject and category of the draft sentence against the corresponding source fields. The condition is met only when the draft sentence field values match the source text. In this sample the two items do not match, so the judgment is not supported.
- Input the term "ambient dairy products", a code-type search term and a natural language question, then compare whether the returned passages can be traced back to the source text. The condition is met only when a passage keeps the applicable store scope and the contract period together. When the return is empty, record the empty result and do not infer that a rule must return an empty array.
- Input a paper empty citation list [] and check the downstream design for handling empty citations, comparing whether the output clearly marks that there is no cited content. The condition is met only when the downstream handles the empty-citation branch. This check does not claim any actual return result from a fictional rule.