What the documents look like
Livestream violation appeals respond to penalties that have already been issued, so the work is a retrospective review of the evidence chain rather than a forward-looking compliance exercise. The main data sources are the platform violation center, livestream recordings, customer service records and the product qualification library. Procurement and supplier materials sit inside that qualification library and cover purchase contracts, supplier business licenses, purchase vouchers and inventory batch records. In an appeal these materials are used to show the origin of the product concerned, its inventory status and the supplier's qualifications, which then supports the factual claims made in the appeal statement.
These materials are updated irregularly, whenever a violation occurs. Their form is mostly text, and they can include scanned license images. At the vector model and index stage, text materials receive indexes generated through a vector model. Whether an image can be retrieved depends on whether an image description index or an image vector index was generated at ingestion time, and on whether the current vector model accepts image input. A knowledge base consists of library, collection and data. The smallest search unit is the library, while a collection only classifies and manages data and does not affect search results. One original data block can be associated with several indexes, and the index content and the original text content need to be checked separately.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Libraries with different vector models cannot be associated together; the operator first checks the model identifier and excludes those that do not match, rather than running a cross-model retrieval to compare missed recalls. Semantic retrieval computes similarity through vector distance, depends on model training quality, and is affected by keywords and sentence completeness. Full-text retrieval suits exact queries such as numbers, models and proper nouns. Hybrid retrieval uses semantic recall and full-text recall together and merges results through RRF. Reranking requires a model and must be enabled in the settings; it reorders candidate text and produces a score between 0 and 1. The minimum relevance value applies only in semantic retrieval or when result reranking is used, and it is a filter threshold rather than a final ordering rule. The reference limit controls the maximum tokens of content cited per search. Image retrieval requires the corresponding image input and model capability, and an image without a matching index at ingestion time cannot be retrieved. Retrieval and similarity do not perform permission or entity isolation, and the same-vector-model requirement for associated knowledge bases does not extend to other external systems.
Hypothetical exercise sample: the source text to check reads, "Supplier A issued a Supply Confirmation Letter to Store B on 1 March 2025, stating that the inventory goods with batch number A-20250301 were purchased through regular channels and remain valid until 31 December 2025." The draft sentence to be assessed reads, "This batch of goods has obtained supply authorization from Supplier A, and the procurement channel is compliant." In this sample the parties are Supplier A and Store B, the object is batch number A-20250301, the status is supply confirmation, and the validity scope is before 31 December 2025. The draft sentence renders "supply confirmation" as "supply authorization." Whether these describe the same fact must be judged against the source text, which contains no occurrence of "authorization," so that claim is not supported.
Configuration and implementation checks
The table below lists documented settings or implementation checks related to vector models and indexes for this page's sample. The values are starting points for this hypothetical exercise, not official defaults.
| Configuration item | Hypothetical starting point | Reason and comparison method |
|---|---|---|
| Vector model | A model matching the identifier of the already associated knowledge base | Check the actual model and input type first, and exclude libraries with a different model identifier |
| Index content | Index built on batch number, supplier name and validity period | Compare sentence by sentence with the source text to confirm the index covers the fields needed for the judgment |
| Search mode | Hybrid retrieval | The batch number is an exact query and the supplier name is a semantic query, so the two are combined |
| Minimum relevance | 0.5 | Compare cited source text for the same question set at 0.3 and 0.7 and observe the filtering effect |
| Reference limit | 2000 tokens | Compare citation completeness at 1000 and 4000 tokens |
| Result reranking | Enabled | Compare candidate ordering with reranking disabled and observe changes in the relevance score |
Three common errors
- Writing "supply confirmation" directly as "supply authorization." The source text only states that the supplier issued a confirmation letter and contains no occurrence of authorization. When checking, input the draft sentence and the source text, compare whether "confirmation" and "authorization" are synonymous, and observe whether the source text contains any authorization wording. If the source text has no authorization wording, the claim is not supported.
- Associating libraries that use different vector models. During operation, check the vector model identifier of each library, compare whether the identifiers match, and exclude those that do not. If the identifiers differ, do not run a cross-model retrieval; record the actual observation and do not infer missed recalls or redundant results.
- Treating retrieval results as a basis for permission or entity isolation. Retrieval returns cited content and does not judge whether the material belongs to the current appeal subject. When checking, input the retrieval result and the appeal subject identifier, compare whether the two correspond, and observe whether unauthorized records were recalled. Retrieval does not perform authorization.
How to verify the setup
- Input the hypothetical source text and the draft sentence, compare the wording difference between "supply confirmation" and "supply authorization," and observe whether the source text contains any authorization wording. If the source text has no authorization wording, the draft claim is not supported and is recorded as pending verification.
- Input the vector model identifiers of the knowledge bases, compare whether the identifiers match, and observe whether any library carries a different model identifier. If one does, exclude it, do not run a cross-model retrieval, and record the actual observation.
- Input retrieval requests for the same question set at minimum relevance values of 0.3, 0.5 and 0.7, compare whether the cited source text contains batch number A-20250301 and the validity period, and observe how citation completeness changes across thresholds. An empty result can be checked against a clearly stated paper input of [] to verify the downstream design; this does not assert that a fictional rule retrieval necessarily returns [].