What the documents look like
Seller ratings and experience-score maintenance follows consumer feedback after fulfilment. The usual data sources are Douyin Shop experience scores, Tmall service indicators, JD after-sales reviews and customer-service systems. The working documents are negative-review handling records, experience-score breakdowns, service diagnostic reports and rectification ledgers. This page covers the procurement and supplier-material task: turning purchase orders, supplier qualification files, arrival inspection and return records into retrievable knowledge-base content used for after-sales attribution and rectification tracking. Material updates occur daily and weekly. Retrieval returns source text for a question; authorization and execution status belong to downstream business systems.
Two characteristics shape this task. First, the retrieval content mixes structured status fields (inspection conclusion, release opinion, approval state) with narrative records, so a retrieved passage may be quoted out of its original status. Second, the same source chunk can carry several indexes, so an index and its source text need separate comparison. Neither characteristic is resolved by the retrieval step alone.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must share the same vector model. Check the actual model identifier and input type of each base; bases on different models cannot be associated together. Record the identifiers and exclude those that do not match. A single source data block may map to multiple indexes, and the index content is compared against the source text separately. Build index content from complete, focused information; retrieval coverage and index length are weighed against samples.
Image retrieval depends on whether an image description or image vector index was generated at ingestion time, not only on what the query side submits. Semantic recall and the minimum relevance value affect ordering and filtering; they do not perform permission or entity isolation. Hybrid retrieval merges multiple result paths through RRF, which is a fusion process, not a switch. The citation limit is counted in tokens, and an empty citation output has length 0 while the downstream branch still runs.
Hypothetical exercise sample. Source text A: "Supplier A submitted the arrival inspection sheet for batch A on 5 March 2026; the inspection conclusion is pending re-inspection, and no release opinion has been issued." Source text B: "Purchase request B was in pending review status on 6 March 2026; the requester did not attach supplier qualification files." Draft sentence to be judged: "Supplier A batch A passed inspection and was released; purchase request B has been approved." Comparison is made on the same subject, same batch and same field.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Vector model of associated knowledge bases | Assume one shared model identifier | Compare against each base's actual model identifier; exclude non-matching bases from association |
| Index content | Assume inspection conclusion and release status as separate indexes | Compare against the full source sentence; check whether the index covers subject, batch and status |
| Search mode | Assume semantic retrieval first | Compare cited source text against the same question set run under full-text retrieval |
| Minimum relevance | Assume a starting value of 0.3 | Hypothetical exercise starting value; compare cited source text against a run with filtering disabled |
| Citation limit | Assume a starting value of 2000 tokens | Hypothetical exercise starting value; compare cited entries against a smaller value |
| Image index | Assume no image vector was generated at ingestion | Check the ingestion record for an image description or image vector field |
Three common errors
- Treating "passed inspection and was released" in the draft sentence as the source conclusion. Input is source text A and the draft sentence; the compared fields are inspection status and release opinion. Source text A states pending re-inspection and no release opinion, so the draft wording is unsupported.
- Treating "pending review" as approved. Input is the status field of purchase request B; the compared value is the review status. Pending review and approved are mutually exclusive values of the same field, so the draft approval conclusion contradicts the source.
- Using minimum relevance to exclude unrelated businesses or unauthorized records. Input is the search parameter and the records in the base; the compared items are returned entries and the source-data permission field. Relevance is a filter threshold, permission isolation requires downstream implementation, and retrieval itself does not perform authorization.
How to verify the setup
- Input source text A and the draft sentence; compare the inspection status and release opinion fields. The draft sentence is supported only when the source explicitly records a release opinion and a passed status. Here the source states pending re-inspection, so the result is unsupported.
- Input the status field of purchase request B and the draft sentence; compare the review status value. The conclusion holds only when the source records approved. The source records pending review, so the result contradicts the source and the approval fact remains to be verified.
- Input the same question set under semantic retrieval and full-text retrieval; compare the cited source text returned by each. If the cited text matches, record the observation; if it differs, record the difference and preserve the empty-result branch. Do not promise a first-hit result or full recall.