What the documents look like
Livestream violation appeals address penalties that have already been issued, so the evidence chain has to be reconstructed after the fact. The materials for this work come from the platform violation center, livestream recordings, customer service records, and the product qualification library. Updates are irregular and are triggered by each violation, so a batch of documents appears whenever a new notice arrives. In cross-border operations, the violation notice, product qualification files, and appeal explanation may exist in different languages. Retrieval therefore has to connect a Chinese-language question with knowledge points recorded in English or another language. Semantic retrieval supports cross-language understanding, which means a Chinese question can match an English knowledge point. This capability directly shapes how citation tracing is designed for this category.
The citation tracing step requires that every answer can be traced back to the original material. The knowledge base search node outputs cited content as an array, and the length can be zero, which means the output path still completes even when no content is found. The retrieval output is then handed to the answer stage, where each answer statement is checked against the cited original text. The minimum relevance score is a filtering threshold and does not perform permission or entity isolation. The knowledge base association requires the same vector model, and this condition only governs whether knowledge bases can be associated together; it does not extend to external systems. Hybrid retrieval merges multiple result paths through RRF, and RRF is a fusion process rather than a switch that can be turned on.
Hypothetical paper exercise sample: a store receives a platform violation notice whose original text states that during the livestream on March 12, 2025, from minute 47 to minute 52, the product qualification was not displayed, and that an appeal must be submitted within 7 days. In the product qualification library, the qualification file for that product is marked with a validity period of January 1, 2025 to December 31, 2025. A draft sentence reads: "The product qualification had already expired during the violation period." The original text supplied for comparison only states that the qualification is within its validity period. The draft sentence's "already expired" and the original text's "within validity period" are mutually exclusive values of the same field, so the draft sentence is unsupported and the evidence gap must be recorded.
Constraints at the citation tracing step
Citation tracing needs the retrieval output to be passed to the answer stage so that each answer statement can be compared against the cited original text. The search node itself only returns cited content; business verification, approval, database writes, and external synchronization each require their own downstream implementation. A retrieval result is not an authorization conclusion.
The minimum relevance score is a threshold that filters low-relevance results, and it applies only under semantic retrieval or when result reranking is used. It does not exclude unauthorized records or unrelated businesses. The same-vector-model requirement for associating knowledge bases is limited to whether those knowledge bases can be jointly associated; it does not imply that external systems such as business registration sites must use the same model.
Hybrid retrieval merges semantic and full-text recall through RRF, and the merged result is not necessarily ordered strictly by a single vector similarity. Reranking produces a 0-1 score that represents the relevance between the searched content and the question, and that score can be used for filtering. The optional paths shown in the overall retrieval flow do not all execute on every request: image retrieval requires corresponding image input and model capability, and reranking requires a rerank model that is enabled according to the settings.
The citation limit is expressed in tokens, not in a top-k item count. The minimum relevance score is a filtering threshold rather than a final ordering rule. When question optimization, hybrid retrieval, image retrieval, or result reranking is enabled, the final result may pass through multi-path fusion and is not necessarily ordered strictly by a single vector similarity.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Select the violation notice library and the product qualification library that use the same vector model | The documentation states that only knowledge bases with the same vector model can be associated; check the identifier first and exclude those with different models |
| Search mode | Hybrid retrieval | Precise queries such as violation numbers and time points rely on full-text recall, while natural-language questions rely on semantic recall |
| Minimum relevance score | Set 0.3 as a starting point | No official default value is provided; compare with 0.5 to observe changes in recall count and noise |
| Citation limit | Set 2000 tokens as a starting point | The documentation controls the citation limit in tokens; compare with 4000 to observe citation completeness |
| Result reranking | Enable when a rerank model is available | The documentation states that reranking produces a 0-1 score that can be used for filtering |
| Question optimization | Enable for multi-turn follow-ups or short questions | The documentation states that question optimization adds one model call and improves retrieval for continuous dialogue |
Three common errors
- Treating retrieval output as an authorization conclusion. Retrieval only returns cited content, and business verification, approval, database writes, and external synchronization require their own downstream implementation. Check method: input one retrieval request, compare whether the output array contains only cited text, and observe whether any authorization status field appears.
- Treating the minimum relevance score as a permission filter. The minimum relevance score is a filtering threshold and does not exclude unauthorized records. Check method: run the same question against two libraries, one containing a certain material and one not containing it, compare the returned entries, and observe whether changing the threshold changes unauthorized visibility.
- Describing RRF as a switch. RRF is the result fusion process of hybrid retrieval. Check method: inspect the configuration interface for a control named RRF, compare the documented description with the interface items, and observe whether such a control exists.
How to verify the setup
- Citation tracing check. Input the violation notice original text and the draft sentence from the hypothetical sample, compare whether the draft sentence's "already expired" and the original text's "within validity period" are mutually exclusive values of the same field, and observe whether the judgment result is unsupported.
- Empty-result handling check. Input an explicit paper-based empty array [], compare whether the downstream answer stage can still complete, and observe whether an error or interruption appears.
- Cross-model association check. Input two knowledge base identifiers that use different vector models, compare whether the association operation is permitted, and observe whether the only available action is to check the identifiers and exclude the non-matching one, without executing a cross-model retrieval to compare missed recall.