What the documents look like
Livestream violation appeals respond to penalties that have already taken effect, so the work centers on reconstructing an evidence chain. The primary data comes from the platform violation center, livestream recordings, customer service records, and the product qualification library. The documents handled day to day are violation notices, livestream screen recordings, product qualifications, and appeal statements. These materials update irregularly, and each violation triggers a fresh round of organization. In the appeal context, contract and transaction material comparison concentrates on whether the product, time, and conduct stated in the violation notice can be mutually corroborated against the livestream recording, the product qualification, and the appeal statement. Document parsing and chunking determine whether these materials can be recalled accurately after ingestion, which in turn determines whether comparison can retrieve a complete passage.
A violation notice may carry a violation number, a product name, a penalty type, and an effective date, and these fields are scattered across the title, body, and appendix. If chunking separates the penalty type from the product it constrains, retrieving the penalty type fails to bring back the corresponding product, and the comparison lacks its object. Product qualifications often carry a validity period and a scope of application; chunking needs to keep the qualification subject, the applicable product, and the valid period within the same passage. When livestream recording transcripts are segmented by timestamp, the correspondence between a segment and the violation time point needs to be preserved.
Hypothetical drill sample: a shop receives a platform violation notice whose original text reads "Violation number XF-2024-0871; product involved: a certain insulated cup; violation conduct: claimed during livestream that the insulation duration is 48 hours; penalty: the product is banned from livestream for 7 days, effective from June 10, 2024." A separate product qualification reads "Test report number JC-2024-0233; commissioning party: a certain shop; sample name: a certain insulated cup; test conclusion: measured insulation duration 12 hours; report date: May 20, 2024." The draft sentence to be assessed is "This appeal asserts that the insulation duration is governed by the test report, and the 48 hours stated in the violation notice does not hold."
Constraints at the document chunking step
The search mode matters because violation numbers and test report numbers are exact identifiers, which full-text retrieval fits better, while violation conduct descriptions are natural language, which semantic retrieval supplements. Problem optimization adds one model call and overall latency, so a single-turn, already-clear question can proceed without it. The minimum relevance value is a 0-1 number that takes effect only under semantic retrieval or result reranking, and it acts as a filter threshold rather than a final ordering rule. The reference cap controls how many tokens are cited per search, which avoids the instability that arises from top k when chunk lengths differ greatly across a mixed knowledge base. Result reranking requires a ReRank model and applies when enabled by configuration; the rerank score is a 0-1 value usable for filtering. Chunk verification is a manual action that checks, document by document, whether the passages of the violation notice, the qualification, and the recording transcript remain complete, confirming that conditions and the content they constrain are not split apart.
Configuration and implementation checks
| Configuration item | Value or check item for this page | Basis and explanation |
|---|---|---|
| Search mode | Hybrid retrieval, semantic and full-text together | Violation numbers and test report numbers are exact identifiers suited to full-text retrieval; violation conduct descriptions are natural language supplemented by semantic retrieval |
| Problem optimization | Decide whether to enable based on actual effect | The documentation states problem optimization adds one model call and overall latency; a clear single-turn question can leave it off |
| Minimum relevance | This hypothetical trial starts at 0.3 and compares against 0.5 to observe recall changes | The value is a 0-1 number effective only under semantic retrieval or result reranking; the starting point is hypothetical for this trial, not an official default |
| Reference cap | This hypothetical trial starts at 2000 tokens | The documentation controls citation volume by tokens to avoid top k instability from chunk length differences; the starting point is hypothetical |
| Result reranking | Used when a ReRank model is available and enabled by configuration | Reranking requires model support, and the rerank score of 0-1 can be used for filtering |
| Chunk verification | Check passage completeness in the violation notice, qualification, and recording transcript one by one | A verification action confirming that conditions and the content they constrain are not split apart |
Three common errors
- Placing the penalty type of the violation notice and the product it constrains into different chunks. The input is the notice text above; compare whether retrieving "banned from livestream" also returns "a certain insulated cup." If only the penalty type returns while the product is absent, the comparison object is missing.
- Splitting the test report's valid period from its test conclusion. The input is the qualification text above; compare whether retrieving "insulation duration" also brings out the report date and commissioning party. If the conclusion returns alone while the subject and date are missing, it cannot be determined whether the report corresponds to this product and period.
- Treating a successful upload as an updated index. The input is the state after the qualification material is re-uploaded; compare the actual training and index state. If only upload completion appears without a training completion record, whether retrieval already includes the new content remains unknown, and the gap needs to be recorded.
How to verify the setup
- Input the violation notice text above together with the draft sentence, and compare the draft sentence's claim that "48 hours does not hold" against the notice's "claimed insulation duration 48 hours" and the qualification's "measured 12 hours." The three share the same subject, a certain insulated cup, and the same field, insulation duration; the notice and qualification hold mutually exclusive values for that field, which is a contradiction. The draft sentence's negation of the notice's content needs the qualification conclusion as its basis; the notice itself does not state that the claim has been withdrawn or corrected, so that point remains to be verified.
- Input the qualification text above, and compare retrieving "JC-2024-0233" against retrieving "insulated cup test conclusion." Number retrieval follows the full-text path, natural language retrieval follows the semantic path; observe whether both return the same passage containing the commissioning party, sample name, conclusion, and date. If natural language retrieval returns only the conclusion while dropping the subject, the chunking is incomplete.
- Input a handover request that does not state its authorization status, for example "please transfer this appeal material to the legal team for handling," and compare the request against whether the material states a transfer authorization. The request itself does not constitute authorization; when the material states no authorization, the transfer remains pending authorization and must not be treated as executed. If the material separately contains a signature or authorization clause, it can be judged only after checking the same subject and the same period.
Knowledge bases built on different vector models cannot be associated together; verify identifiers first and exclude those that do not conform, and do not run cross-model retrieval to compare missed recalls. Retrieval or a form does not constitute authorization; business verification, approval, database writes, and external synchronization each require their own downstream implementation.