What the documents look like
Franchise fee and deposit settlement work centers on money movement and breach deductions. The material set comes from the finance system, franchise contracts, bank statements, and store violation records. Day-to-day documents include franchise fee invoices, deposit ledgers, deduction notices, and contract payment schedules. This category differs from franchise filing administration: settlement handles funds and breach deductions, and it does not carry government filing duties. Errors here produce funding disputes, franchise litigation, and deposit refund conflicts.
For store service and internal training, retrieval and recall must locate and quote this material accurately. A staff member preparing a settlement answer needs the invoice, the ledger entry, the deduction notice, and the payment schedule to be retrievable as separate, identifiable documents. The retrieval layer decides which of these appear in the quoted context. It does not decide whether a payment occurred, whether a deduction was valid, or whether a deposit should be released.
The eight-dimension brief places this work under chain stores and franchising, with a monthly update cycle and an update on each deduction. Those frequencies describe how the source records change. They do not create any batch synchronization, full synchronization, real-time, or transaction-triggered mechanism inside the retrieval feature.
Constraints at the retrieval step
A knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases on different vector models cannot be associated together. The correct action is to check the vector model identifier and exclude any knowledge base that does not match; running a cross-model search to compare missed recalls is not a valid step.
Semantic retrieval computes distance between vectors and suits natural-language questions and semantically similar content. Full-text retrieval relies on keyword hits and suits identifiers, model numbers, proper nouns, and error codes. Hybrid retrieval uses both and merges the two result sets through RRF. RRF is the fusion process applied to hybrid retrieval results, not a toggle that an operator switches on.
The minimum relevance value is a 0-1 number that filters low-relevance results. It takes effect only under semantic retrieval or when result reranking is used. It is a filtering threshold, not a final sorting rule, and it is not an entity or permission filter. It does not exclude unrelated businesses or unauthorized records. Retrieval results do not constitute permission, approval, or an authorization to execute.
The citation limit is measured in tokens, not in a top-k item count. The search node outputs quoted content as an array, and the length can be zero. When no content is found, the output path still runs through. Image retrieval requires image input and the corresponding model capability. Reranking requires a rerank model and must be enabled according to the configuration. The optional paths in the documented retrieval flow are not all executed on every request.
Configuration and implementation checks
The following table uses one hypothetical paper exercise sample. It is a constructed case for checking this page's task, not a real record and not evidence that any test was run.
Hypothetical exercise sample. Source text to be checked: "In March 2024, franchisee A paid a franchise fee of 50,000 yuan; the deposit ledger records 30,000 yuan; the deduction notice states a deduction of 2,000 yuan for a violation found during inspection." Draft sentence to be judged: "A has completed all settlement obligations for March 2024." The source text carries a subject, an object, a period, and three separate record types. The draft sentence asserts completion of all obligations. No contract payment schedule, bank statement, or deduction justification is included in the sample.
| Configuration item | Hypothetical starting value for this exercise | Comparison and reason |
|---|---|---|
| Associated knowledge bases | Only the settlement library on the same vector model | Check that the vector model identifier matches; exclude any knowledge base that does not match |
| Search mode | Hybrid retrieval | Compare recall differences against semantic-only and full-text-only runs on the same question |
| Citation limit | 3,000 tokens for this exercise | Compare against a shorter value to observe whether quoted content still covers subject and period |
| Minimum relevance | 0.4 for this exercise | Label relevant and irrelevant samples first, then adjust; this value takes effect only under semantic retrieval or reranking |
| Question optimization | Enabled | Compare the original question with the optimized question and check whether the subject is altered |
| Result reranking | Enabled per configuration | Check question completeness and candidate count before relying on the rerank score |
The starting values above are exercise settings chosen to make a comparison possible. They are not official defaults, and no numeric value here was tested in a live system.
Three common errors
- Treating a retrieval hit as proof that settlement is complete or as an authorization basis. The draft sentence "A has completed all settlement obligations for March 2024" requires the contract payment schedule, bank statement, and deduction justification to be checked together. Retrieving an invoice does not replace that judgment. The sample contains no payment schedule, no bank statement, and no deduction justification, so the draft sentence is unsupported and remains pending verification.
- Treating the minimum relevance value as an entity or permission filter. That value only filters low-relevance results. It does not exclude unrelated businesses and does not exclude unauthorized records. A relevance score is not a category, entity, or permission filter.
- Describing RRF as a toggle. RRF is the fusion step applied to hybrid retrieval results. It is not a checkbox, and it is not a control that an operator turns on or off.
How to verify the setup
- Input the hypothetical source text and the draft sentence given above. Compare whether the retrieved quoted content includes the franchise fee, the deposit ledger, and the deduction notice, and observe whether the quoted content covers the subject and the period. A single category appearing in the results does not satisfy the check. If the quoted content covers only one of the three record types, the check fails.
- Input an explicit paper empty result set, written as an empty array, against the downstream design. Compare whether the downstream path continues when the quoted output length is zero. Observe whether the flow proceeds. If the flow stops, the check fails. This is a check of the documented zero-length output path; it does not assert that any particular query returns an empty array.
- Input two knowledge base identifiers that use different vector models. Compare whether the system permits associating them together. Observe whether the non-matching knowledge base is excluded and the observation is recorded. If a cross-model search is executed instead of excluding the non-matching base, the check fails. No claim is made about what a cross-model search would return.
These checks state what to input, what to compare, and what observable result satisfies the condition. They do not assert that any run has occurred, that any approval exists, or that any settlement action has been executed. Where the sample lacks a payment schedule, a bank statement, or a deduction justification, that evidence remains missing and the corresponding conclusion remains unverified.