What the documents look like
Franchise supervision data centers on the comparison between headquarters standards and store execution. Store visit forms record display, ordering and rectification items. Franchise contracts define the rights and obligations of both parties. Rectification notices carry a problem description and a deadline. Display standards give product positions and promotional wording. Regional supervisors collect and use these materials on a weekly or monthly cadence, drawing on the supervision app, POS sales, the franchise system and store photographs. During copy drafting, citable source text must be recalled from these materials so that a proposed sentence can be checked against what the materials actually say.
The distinction from direct-operated convenience store fresh-food ordering is that franchise supervision focuses on headquarters oversight of how franchisees execute standards. Errors here can lead to franchise disputes, damage to brand image and contract breach. That is why retrieval in this category is not a general search convenience; it is the step that decides whether a draft sentence has a traceable basis in a supervision document.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Search modes include semantic search, full-text search and hybrid search. Hybrid search merges multiple result paths through RRF. A rerank model performs a second ordering of candidate text, and the rerank score can be used for filtering. The minimum relevance value takes effect only in semantic search or when result reranking is used. The reference limit is measured in tokens and controls the maximum amount of content referenced in a single search. Question optimization performs coreference resolution and question expansion, adding one model call before retrieval. Image retrieval requires image input and the corresponding model capability. Reranking requires a model and runs according to its settings. Retrieval output is an array whose length can be zero, so the output path continues even when no content is found.
Hypothetical drill sample. Source text one: "Appendix 3 of the franchise contract states that fresh-food price tags must match the headquarters display standard then in effect, and the supervisor checks this during the monthly store visit." Source text two: "Rectification notice number ZG-2024-0317 records that the store's cold cabinet temperature log was missing for three days, and requires the log to be completed and submitted within five working days." Proposed draft sentence: "This store's fresh-food price tags now fully comply with headquarters standards, and the cold cabinet temperature log is complete." The comparison objects are the two source texts above. The period is the five working days stated in the rectification notice. The fields are price tag consistency and temperature log completeness.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | One base each for store visit forms, franchise contracts, rectification notices and display standards | The four bases must use the same vector model to be associated together; check the vector model identifier base by base |
| Search mode | Test semantic search and full-text search separately on the same question | Compare what each mode recalls for "price tag" and for "ZG-2024-0317" |
| Reference limit | Set the hypothetical starting point to a token amount covering one full rectification notice | Test with rectification notices of different lengths to see whether content is truncated, then adjust |
| Minimum relevance | Label relevant and irrelevant samples first, then set the threshold | Takes effect only in semantic search or with reranking; compare candidate counts before and after filtering |
| Question optimization | Enable for multi-turn follow-up scenarios, disable for clear single-turn questions | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable when a rerank model is available, executed according to its settings | Compare ordering before and after reranking; the rerank score is used for filtering |
Three common errors
- Treating retrieval recall as an authorization decision. A recalled contract fragment does not mean the proposed sentence has contract authorization. The error appears as using recalled content directly as a publishable basis. Check method: input the proposed sentence and the recalled source text, compare whether subject, object and status match, and observe whether any statement lacks support.
- Associating bases that use different vector models. The error appears as placing bases with different vector models into the same search node. Check method: verify each base's vector model identifier, exclude those that do not match, and record the actual observation. Do not run cross-model retrieval to compare missed recall.
- Writing a pending-review state as completed. The error appears as describing the submission action in a rectification notice as a completed log entry. Check method: input the rectification notice source text and the proposed sentence, compare the deadline stated in the notice with the completion status claimed in the sentence, and observe whether completion evidence is absent.
How to verify the setup
- Input source text one, source text two and the proposed draft sentence from the hypothetical sample. Compare "price tags now fully comply" in the proposed sentence with the checking requirement stated in Appendix 3 of the contract. Observe whether the recalled content contains source text supporting that statement. If the recalled content shows only the checking obligation and not a checking result, the statement is unsupported.
- Input rectification notice number ZG-2024-0317. Compare how full-text search and semantic search recall that number, and observe whether full-text search matches the number. If neither mode returns content corresponding to that number, record it as not recalled, and do not conclude that it does not exist.
- Input a store visit form fragment that contains no price tag or temperature log content. Compare the recalled result with the topic of the proposed sentence, and observe whether the returned reference content is an empty array. If an empty array is returned, check the downstream design against the node description that output length can be zero, and do not claim that a fictitious-rule search necessarily returns an empty array.
The primary task on this page is copy drafting for franchise store supervision. Whether a specific promotional sentence in product copy is supported by the materials is the question to inspect. This page does not become an identity certification tutorial, a report analysis procedure or any other task. RRF is a fusion process for hybrid results and is not described as an on/off switch. The minimum relevance value is a filtering threshold, not a category, entity or permission filter, and it does not exclude unrelated businesses or unauthorized records. Retrieval is not authorization. The same-vector-model requirement for associated knowledge bases does not extend to business registration websites or any other external system. Optional paths shown in the overall retrieval flow diagram do not mean every request executes all of them. Image retrieval requires the corresponding image input and model capability. Reranking requires a model and runs according to its settings.