What the documents look like
Short-video affiliate distribution relies on long-term content exposure and product-link conversion, without depending on live explanation. Content operators, creator business development staff and product distribution specialists handle short-video scripts, affiliate product lists, commission plans and violation feedback as their daily materials. These materials update daily or weekly. Product selling points in scripts, listed products in the product list, rates and validity periods in commission plans, and handling status in violation feedback together form the objects checked during selection Q&A. When document versions fall out of alignment, promotion can fail, commission can be wasted, and videos can be taken down. Citation tracing therefore requires each conclusion to correspond to specific source text and its validity scope.
Constraints at the citation tracing step
The knowledge base search node can select one or more knowledge bases that use the same vector model, and outputs citations as an array whose length can be zero, meaning the output path still completes even when no content is found. The citation limit is controlled in tokens. The minimum relevance score is a filtering threshold that takes effect only during semantic retrieval or result reranking. Hybrid retrieval merges multiple result paths through RRF. Reranking requires a rerank model and must be enabled according to settings. Image retrieval requires image input and model capability. These optional paths do not mean every request executes all of them. Retrieval itself does not constitute authorization. After the node outputs citation content, whether to adopt it still requires manual review combined with the valid version.
Hypothetical exercise sample: the source text for checking is "Affiliate product list version A: product number SP-2024-018, affiliate status listed, applicable script script A, list effective date 1 June 2024 to 30 June 2024." "Commission plan version B: product number SP-2024-018, commission rate 12%, plan validity 1 June 2024 to 30 June 2024." The draft sentence to be judged is: "Product SP-2024-018 currently has affiliate status listed, commission rate 12%, and can be used directly for script A promotion." This request does not attach an authorization approval record, and does not state who approved affiliate and commission execution.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Checking method and reason |
|---|---|---|
| Associated knowledge bases | Select the product materials base and the selection Q&A base | First check whether both bases use the same vector model; if not, exclude and do not execute cross-model retrieval |
| Search mode | Hybrid retrieval | Product number questions need semantic and keyword recall together, merged by RRF as documented |
| Citation limit | Hypothetical starting point 800 tokens | This value is the starting point for this exercise, adjusted after comparison with sample source length, not an official default |
| Minimum relevance | Hypothetical starting point 0.4 | This value is the starting point for this exercise, adjusted after comparison with rerank scores, not an official default |
| Result reranking | Enable when a rerank model is available | After enabling per settings, observe rerank scores; when not enabled, do not filter on that basis |
| Citation content output | Accept the search node output array | Check that array length can be zero; an empty array does not equal a workflow error termination |
Three common errors
- Treating a retrieval hit as authorization. Error manifestation: after the draft sentence cites list version A and commission plan version B, it directly writes "can be used directly for script A promotion." The input is the two source texts and the draft sentence. Compare whether the draft sentence exceeds the affiliate status and commission rate stated in the materials. The observed result is that the draft sentence adds a promotion authorization not stated in the materials. The check is: compare the draft sentence against the source text sentence by sentence, and mark authorization statements not stated in the materials as unsupported.
- Associating knowledge bases with different vector models. Error manifestation: selecting the product materials base together with another base whose vector model differs. The input is the vector model identifiers of the two bases. Compare whether the two are identical. The observed result is that the identifiers do not match. The check is: verify identifiers and exclude non-matching ones, record the actual observation, and do not fabricate the runtime result of joint association.
- Treating pending review as approved. Error manifestation: the handover request states "the commission plan is approved and can be executed." The input is the commission plan version B source text and the handover request. Compare whether the materials state an approval status. The observed result is that the materials state only the rate and validity period, and do not state approval. The check is: mark the approval status as pending verification and retain the evidence gap.
How to verify the setup
- Use the hypothetical sample source text to check the draft sentence. The input is list version A, commission plan version B source text and the draft sentence. Compare whether the three items in the draft sentence — affiliate status, commission rate and promotion authorization — each appear in the materials. The observed result is that affiliate status and commission rate are visible, promotion authorization is not stated, and the sentence is judged unsupported.
- Use an empty array to check downstream acceptance. The input is an explicit paper input []. Compare whether the downstream answer step continues to complete with a citation array length of zero. The observed result is that the path completes without error. Do not claim that a fictional rule retrieval necessarily returns [].
- Use the two base identifiers to check the association condition. The input is the vector model identifiers of the product materials base and another base. Compare whether the two are the same. The observed result is that when identifiers differ, exclude that base, record only the actual observation, and do not infer missed recall or redundant results.