What the documents look like
Franchise supervision in the convenience-store category centers on the headquarters' oversight of how franchisees carry out head-office standards. The headquarters and the franchise owner sit in a contractual relationship, and the working documents are store-visit forms, franchise contracts, rectification notices and display standards. Inventory procurement and supplier records are spread across the supervision app, POS sales, the franchise system and store photographs. A retrieval result is a lead for cross-checking the same store, the same period and the same field across those sources. It is not an authorization decision, and it does not settle a dispute, protect brand image or resolve a contract breach on its own.
Constraints at the retrieval step
A knowledge base is organized as library, collection and data. The smallest search unit is the library; collections only classify data and do not affect search results. The knowledge base search node can associate one or more libraries that use the same vector model, and libraries built on different vector models cannot be associated together. The retrieval chain combines problem optimization, semantic retrieval, full-text retrieval, hybrid retrieval, RRF fusion, Rerank, similarity filtering and a reference cap; these are optional paths, and a single request does not necessarily run all of them. Image retrieval requires image input and model capability. Rerank requires a model and runs when enabled. The reference cap is measured in tokens. The minimum relevance value applies only under semantic retrieval or when result reranking is used. Retrieval itself does not perform permission or entity isolation.
Hypothetical paper exercise: a supervisor is checking a store-visit form that reads "Week 2 of March 2024, Category A store, freezer display temperature record missing, store manager reminded on site to backfill." A rectification notice in the same batch reads "Regarding the missing display temperature record above, the store is required to complete the backfill and return photographs by 20 March 2024." The draft sentence to be judged is "The store has completed rectification and the display temperature record has been backfilled." The source texts record only a reminder and a requirement; they do not state that the backfill was completed, so the draft sentence is unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Select the store-visit form library and the rectification notice library that share one vector model | Libraries on different vector models cannot be associated; check the vector model identifier first and exclude non-matching libraries |
| Search mode | Start with hybrid retrieval | The materials mix identifiers and natural language; compare semantic, full-text and hybrid modes on the same sample question |
| Reference cap | Set a starting value that can hold one full store-visit record in tokens | Test with short and long materials to see whether content stays complete, then adjust the cap |
| Minimum relevance | Label relevant and irrelevant samples first, then tune the threshold | This value applies only under semantic retrieval or result reranking; it is not a filter for unrelated businesses |
| Problem optimization | Enable for multi-turn follow-up scenarios | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Use when a model is available and the setting is enabled | RRF is a fusion process, not a switch; the rerank score can be used for filtering |
Three common errors
- Treating a retrieval hit as authorization to act. In the sample, the rectification notice only states a requirement to complete the backfill by a date. A hit on that notice does not show the backfill was completed and does not grant the store any operating permission. Compare the notice deadline against whether a returned photograph exists, and record the evidence gap when no return record is present.
- Treating minimum relevance as a permission or entity filter. The parameter is a 0-1 filtering threshold that applies only under semantic retrieval or result reranking. It cannot exclude unrelated franchise stores or unauthorized records. First confirm whether the retrieval scope covers the target store, then compare the score against the threshold.
- Forcing an association across libraries on different vector models. The documentation states that only libraries using the same vector model can be associated; a cross-model association is not executable. Check each library's vector model identifier, exclude non-matching ones and record the actual observation without inferring runtime behavior after association.
How to verify the setup
The three checks below use the hypothetical sample above. Each states the input, the comparison and the condition that satisfies it.
- Subject and status check. Input the draft sentence "The store has completed rectification and the display temperature record has been backfilled." Compare it against the store-visit form text "store manager reminded on site to backfill" and the rectification notice text "required to complete the backfill and return photographs by 20 March 2024." Satisfied when the completion status in the draft sentence has no corresponding field in the source texts; mark it unsupported and keep the evidence gap.
- Empty-result handling check. Input an explicit paper empty array [] as the retrieval output and check whether the downstream design lets the reference chain proceed as documented. Satisfied when the downstream can handle reference content of length 0 without breaking; do not claim that a fictional rule makes retrieval always return [].
- Cross-model association check. Input the vector model identifiers of two knowledge bases and compare whether they are the same. Satisfied when only identical identifiers are associated together; libraries with different identifiers are excluded, no cross-model retrieval is run to compare missed recalls, and only the actual observation is recorded.
All three checks address the primary task on this page and do not drift into identity certification, report analysis or another task. Retrieval checks record observations and failure branches; they do not guarantee a first-hit result, full recall or that an unrelated query returns nothing.