What the documents look like
Livestream violation appeals respond to penalties that have already been issued, so the evidence chain is reconstructed after the fact. Source material comes from the platform violation center, livestream recordings, customer service records, and the product qualification library. Within this task, inventory procurement and supplier records serve two purposes: showing the actual purchasing source and supplier entity behind the product at issue, and stating the procurement date, quantity, and authorization status of that inventory batch. These materials typically consist of purchase orders, supplier qualification files, purchase vouchers, and handover notes. They are updated irregularly and gathered separately for each violation.
The physical form of the material directly affects parsing and chunking. Violation notices and appeal statements are mostly continuous narrative text. Supplier qualifications and purchase vouchers frequently contain precise fields such as numbers, model codes, and unified social credit codes. Full-text retrieval suits number and proper-noun queries, while semantic retrieval suits natural-language descriptions. If chunking separates a supplier name from the validity period of its corresponding qualification, a retrieval hit cannot point back to the complete constraint relationship.
Constraints at the document chunking step
Chunking must preserve the document identity and version that allow a chunk to point back to the source text. The same supplier document may exist as both a scanned file and an entered text version. These are treated as separate data items and indexed separately, with no assumption that one substitutes for the other. One data item can map to multiple vector indexes. Longer text can be split into several index groups, and a hit on any one group recalls that data item.
Exact-number queries and natural-language queries require different retrieval modes. Hybrid retrieval merges multiple result paths through RRF, then applies ReRank for a second ordering. The rerank score is 0-1 and can be used for filtering. The minimum relevance value takes effect only under semantic retrieval or result reranking. It is a filtering threshold and does not perform permission or entity isolation. The association between a retrieval library and a vector model is limited to verifying identifiers within the library. This requirement does not extend to external business registration systems having to use the same model.
Hypothetical exercise sample: the source text reads, "Supplier A issued a Supply Confirmation Letter on March 1, 2025, stating that it supplied 200 units of batch A goods to this store, with the letter valid until June 30, 2025. Purchase order B-0315 records the same batch purchase quantity of 200 units, signed March 5, 2025." The draft sentence to be assessed is, "Supplier A has confirmed supply of batch A goods beyond June 30, 2025." This draft sentence exceeds the validity period stated in the confirmation letter and is unsupported.
Configuration and implementation checks
The following are hypothetical starting points for this exercise, not official default values. They were chosen to make the effect of chunking on retrieval observable.
| Configuration item | Hypothetical starting point | Check and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Search with the number "B-0315" and with the natural phrase "batch A supplier"; compare the chunks hit |
| Minimum relevance | 0.3 | Compare against retrieval with filtering off; observe whether low-relevance fragments decrease |
| Reference limit | 800 tokens | Compare against 400 tokens; observe whether long supplier clauses are truncated |
| Question optimization | On | Compare against off; observe whether "this batch" is resolved in multi-turn follow-ups |
| Chunking method | One segment per qualification file | Compare against fixed-length splitting; check whether validity period and entity stay in one chunk |
| Post-update review | Manual check of training and index status | After upload, search the material to confirm whether it is actually retrievable |
Three common errors
- Treating a successful upload as an updated index. Upload, training, and index creation are separate stages. After a material change, an actual search is needed to verify retrieval.
- Treating minimum relevance as a permission filter. This value filters low-relevance results only. It does not exclude unrelated businesses or unauthorized records.
- Applying question optimization to exact numbers. Number and model-code queries are better served by full-text or hybrid retrieval. Question optimization can alter the meaning of the original query.
How to verify the setup
- Enter the hypothetical source text and the draft sentence. Compare the draft phrase "supply beyond June 30, 2025" with the confirmation letter's stated "valid until June 30, 2025." The draft exceeds the stated range, so it is judged unsupported, not contradictory.
- Enter the number "B-0315" as a search. Compare whether the returned chunk contains both the purchase order signing date and the quantity. If the chunk contains only the quantity while the entity has been split away, adjust the chunking method.
- Enter an empty reference list
[]to check the downstream display design. Observe how the system handles the empty result path. This check does not assert that any fictional rule necessarily returns an empty result.