What the documents look like
Marketplace advertising adjusts bids against traffic and conversion in near real time, and platform promotion enrollment is not the controlling gate. Daily material for this category includes campaign plans, keyword reports, audience package descriptions and conversion attribution tables. The public brief places update frequency at daily or hourly, which matters here only where a handover depends on which version of a report a store operator is holding.
Store service and internal training hands these materials from a media buyer to store execution staff. Citation tracing decides whether a quoted line, a training statement or a handover instruction can be matched back to a specific source document and version. When versions drift, a store can act on an older keyword report. The brief lists wasted ad spend, distorted return on investment and cut campaign budgets as consequences, and the handover step is where those consequences concentrate. Conversion attribution tables carry attribution reconciliation work, so a training conclusion that cites attribution content not present in the original table loses its basis.
The eight-dimension brief is descriptive of a category, not of a named customer. The confidence marker on that brief is approximate, so the material list and the frequency statement are treated as category context rather than as verified records for any specific store.
Constraints at the citation tracing step
The knowledge base search node outputs cited content as an array, and that array can have a length of zero. When nothing is retrieved, the output path still completes, so downstream design must accept an empty array rather than treat it as a terminated run. Associated knowledge bases must share the same vector model; bases built on different vector models cannot be associated together. The identifier is checked first, and non-matching bases are excluded.
Semantic retrieval depends on vector distance. Full-text retrieval depends on keyword hits. Hybrid retrieval merges both through RRF, and reranking reorders candidate text and returns a score between 0 and 1. The minimum relevance value applies only under semantic retrieval or when reranking is in use, and it acts as a filter threshold rather than a final ordering rule. The citation limit is expressed in tokens, not as a top k item count. Retrieval is not authorization, and a form collecting values is not business approval or a database write.
Hypothetical paper exercise. Source text one: "From 1 March 2025 to 31 March 2025, the keyword report version for store A's campaign plan is V3, and bid adjustment records follow V3." Source text two: "For the same period, the conversion attribution table is marked as pending regional review, and it is not used as a basis for training conclusions until review completes." Draft sentence under assessment: "Store A completed bid adjustments according to the V3 keyword report between 1 March and 31 March, and the conversion attribution table passed review."
Aligned on the same subject, the same period and the same fields, the draft sentence asserts two facts the source texts do not carry: that bid adjustment was completed, and that review passed. Source text one states which version governs bid adjustment records, and source text two states a pending review status. The completion claim and the approval claim remain unsupported.
Configuration and implementation checks
| Check item | Setting or check on this page | Relation to the exercise |
|---|---|---|
| Associated knowledge bases | Read the vector model identifier of each selected base and exclude bases whose identifiers differ | The exercise materials must fall inside a base set that can be associated |
| Citation output | Confirm the search node citation array may have length zero and that an empty array is not treated as a terminating error | The exercise retrieval may return an empty array, and downstream must carry it |
| Citation limit | Set in tokens; for this exercise the hypothetical starting value is 2000 tokens, chosen to cover keyword report and attribution table fragments, compared against 500 tokens to observe recall completeness | Fragment length in the exercise materials drives the starting choice |
| Minimum relevance | Applies only under semantic retrieval or reranking; for this exercise the hypothetical starting value is 0.3, compared against 0.5 to observe remaining items after filtering | Fields touched by the draft sentence must stay inside the candidate set |
| Search mode | Use full-text or hybrid retrieval for identifiers and version numbers | The V3 version string in the exercise suits keyword hits |
| Citation handover | Check whether retrieval output is passed to the answering step, then compare each answer line against the cited source text | The draft sentence must be compared line by line with source texts one and two |
The two numeric starting values above are hypothetical exercise inputs with stated reasons and stated comparison values. They are not documented defaults, and no limit, unit or menu beyond the documented token-based citation limit is asserted here.
Three common errors
- Treating retrieval output as an authorization result. Input: source texts one and two, plus a handover request. Comparison: the retrieval citation array against the handover request. If a non-empty citation array is taken to mean bid adjustment was completed, the conclusion is wrong. Retrieval is not authorization, and whether bid adjustment ran is a separate fact.
- Writing a pending review status as passed. Input: source text two, which reads "pending regional review." Comparison: the draft sentence's "passed review" against that field. The same field carries mutually exclusive values, so the draft sentence does not hold.
- Associating knowledge bases that use different vector models. Input: the vector model identifiers of two knowledge bases. Comparison: whether the two identifiers match. If they differ and association is still performed, the same-vector-model requirement is violated; the non-matching base is excluded and the observation recorded.
How to verify the setup
- Input source texts one and two, then compare the draft sentence against both texts on the same subject, the same period and the same fields. If the draft sentence contains "completed" or "passed" values that the materials do not carry, the sentence is unsupported, and the evidence gap is recorded. This is a paper comparison; no live retrieval run is claimed.
- Input a paper retrieval result of an empty array, then compare whether the downstream answering step carries the empty array without a terminating error and without fabricating citations. If downstream completes and invents no citation, the empty-result handover condition is met. This does not assert that any particular query returns an empty array.
- Input two knowledge bases whose vector model identifiers differ, then compare the identifiers. If they do not match, exclude that base and record the actual observation. Cross-model retrieval is not executed to compare missed recall, because the documented requirement is that such bases cannot be associated at all.
Across all three checks, retrieval and relevance scores do not act as permission or entity isolation, and the same-vector-model requirement for associated bases does not extend to any external system. RRF is the merge process for hybrid results, not a switch. The thinking budget parameter belongs to model reasoning configuration and is not a citation token limit. Optional paths in the overall retrieval flow, such as image retrieval or reranking, run only when the corresponding input and model capability are present and the setting is enabled.