What the documents look like
MCN organizations manage long-term account compliance across many creators, and the people who rely on retrieval include signed creators, store service staff, and internal training participants. Day-to-day materials include creator signing contracts, training records, violation handling forms, and account schedules. In the store service and internal training setting, retrieval has to work across four document types at once: contract clauses, training courseware, violation handling procedures, and scheduling rules. These materials share a common shape: many subjects, long periods, and many status fields, so a retrieved result has to point back to a specific creator and a specific period.
Compared with a single-brand live commerce session, MCN retrieval requests tend to cross creators, cross stores, and cross months. One training record may cover several creators, while one violation handling form covers only one account. If retrieval matches on keywords alone, materials belonging to different creators can be mixed together. The brief states that materials update daily or weekly; this page does not infer any batch synchronization or real-time trigger mechanism from that frequency.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Bases with different vector models cannot be associated together; the vector model identifier must be checked first, non-matching bases excluded, and the actual observation recorded. The retrieval step does not perform permission decisions, and semantic recall and relevance scores do not carry out entity isolation or authorization filtering.
The following is a hypothetical paper exercise sample, used to show how checking works. It does not represent a real customer or a business action that occurred.
Source text one: "Creator A signed a store service training agreement on March 1, 2025. The training period is March 1, 2025 to March 31, 2025. The training location is Store B."
Source text two: "Creator A submitted a violation handling form on March 15, 2025. The form states that account C used prohibited wording during a livestream on March 10, 2025. The handling status is pending review."
Proposed sentence for review: "Creator A's store service training agreement completed all training content on March 31, 2025."
This proposed sentence must be compared with source text one. Source text one states only the training period and location; it does not state a training completion status. The phrase "completed all training content" in the proposed sentence is not supported by source text one. The item remains pending verification and cannot be judged as completed or not completed.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge base | Associate one base whose vector model identifier is M1 | Check each base's vector model identifier first, exclude bases with a different identifier, and do not run cross-model retrieval |
| Search mode | Test semantic retrieval first | Use the same question against semantic, full-text, and hybrid retrieval, and compare whether the hit content and the subject match |
| Reference upper limit | Start at 2000 tokens | Test whether content stays complete using training records and contract excerpts of different lengths, and compare against a shorter limit |
| Minimum relevance | Start at 0.3 | Label relevant and irrelevant samples first, then compare filtered results at different thresholds |
| Question optimization | Off initially | For questions containing references, compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Not enabled for now | Confirm the rerank model is available before comparing ordering changes with it on and off |
The reference upper limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. Question optimization is used for reference resolution and question expansion. RRF is the fusion process for hybrid retrieval results and is not a standalone switch. Image retrieval requires image input and the corresponding model capability; this page's sample contains no image input and does not cover that path.
Three common errors
- Treating a retrieval hit as authorization confirmation. Input: the retrieval request "Creator A training agreement status." Compare the agreement source text returned by retrieval with the proposed sentence. If the source text does not state a completion status, the observed result is pending verification, and the item cannot be judged as completed.
- Treating a pending-review handling form as an effective violation conclusion. Input: source text two. Compare the handling status stated on the form with the proposed sentence. If the proposed sentence says "the violation has been confirmed," the observed result is not supported; pending review does not confirm that a violation has been established.
- Associating knowledge bases that use different vector models. Input: the vector model identifiers of two bases. Compare whether the identifiers match. If they differ, the observed result is that they cannot be associated together; the non-matching base must be excluded and the actual observation recorded.
How to verify the setup
- Input source text one and the proposed sentence "Creator A's store service training agreement completed all training content on March 31, 2025." Compare the completion status in the proposed sentence with the training period stated in source text one. The observed result is that source text one does not state a completion status, so the judgment is pending verification.
- Input source text two and the proposed sentence "Creator A's violation handling form has confirmed that account C violated the rules." Compare the handling status stated on the form with the proposed sentence. The observed result is that the form states pending review, so the judgment is not supported.
- Input the vector model identifiers of two knowledge bases and compare whether the identifiers match. The observed result is that different identifiers mean the bases cannot be associated together and the non-matching base must be excluded; matching identifiers allow the retrieval results to be checked further.
An empty result can be carried downstream by checking with the explicit paper input [], without claiming that a fabricated rule retrieval will necessarily return []. Retrieval checks record only observations and failure branches; they do not guarantee a first hit, full recall, or that an unrelated query returns nothing.