What the documents look like
Reverse returns inspection concerns returned parcels and the condition of goods. Its data comes from the WMS returns module, platform after-sales records, carrier return shipments, and the inspection bench. Each returned item produces a returns inbound record; an inspector takes inspection photographs; a grade determination table supplies the grade; and an inventory administrator decides on resale listing or scrapping based on that grade. Records update per item and per day. This page's retrieval function does not change because of that frequency.
The contract and transaction material comparison task asks for a field-by-field comparison of the returns inbound record, inspection photographs, grade determination table, and resale listing sheet against materials already held in a knowledge base. The knowledge base search node can associate one or more knowledge bases that use the same vector model. Retrieval returns cited content as an array, and the length may be zero, meaning the output path still runs even when nothing is found. Retrieval only locates candidate materials. Authorization status, approval outcomes, and downstream business actions need separate system implementation.
Constraints at the retrieval step
Semantic retrieval relies on vector distance and suits natural-language descriptions. Full-text retrieval relies on keyword hits and suits exact queries such as return numbers and grade codes. Hybrid retrieval uses both recall paths and merges them through RRF. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used; it is a filter threshold, not a sorting rule, and it does not perform permission or entity isolation. Associated knowledge bases must use the same vector model, and identifiers that do not match should be excluded first. Cross-model retrieval is not executed to compare missed recalls. Image retrieval requires an image in the input and the corresponding model capability. The optional paths listed in the documentation do not mean every request executes all of them.
Hypothetical paper exercise sample: an after-sales warehouse inspector processes a returns inbound record on the hypothetical date 10 March 2025, with the hypothetical return number RT-2025-0310-017, for a hypothetical model A-100 earphone. The inspection photograph shows a scratch on the casing. The grade determination table marks it as hypothetical grade B. The resale listing sheet is drafted to read "this item is relisted at grade B." The draft sentence to be assessed is "this return has been refunded and is permitted for resale." The source material for comparison reads: "returns inbound record RT-2025-0310-017 states a signed-in quantity of 1 item; the grade determination table states grade B; the refund status field in the system is empty." This sample only illustrates the comparison method and was not actually executed.
Configuration and implementation checks
| Configuration item | Purpose on this page | Hypothetical trial starting point and comparison method |
|---|---|---|
| Associated knowledge bases | Select the bases holding returns inbound records, grade determination tables, and similar materials | First check that the vector model identifiers of each base match, and exclude those that do not |
| Search mode | Compare semantic, full-text, and hybrid retrieval against test questions | Try full-text retrieval for number-type questions and semantic retrieval for status-description questions, then compare hits |
| Citation limit | Control the length of cited content per search, in tokens | A hypothetical starting point of 1000 tokens, chosen to hold several short materials; compare against smaller values to see whether content is truncated |
| Minimum relevance | Filter low-relevance results; takes effect only in semantic retrieval or result reranking | Label relevant and irrelevant samples first, then adjust the threshold and observe whether irrelevant materials decrease |
| Question optimization | Coreference resolution and question expansion | Compare the original question with the optimized question and check whether the subject or return number was altered incorrectly |
| Result reranking | Re-sort candidate text | When a rerank model is available and enabled by configuration, compare the ordering before and after reranking |
Three common errors
- Treating retrieval results as an authorization conclusion. Retrieval returns an array of cited content whose length may be zero, and this does not mean a refund was approved or a resale was authorized. Check: input the draft sentence "this return has been refunded and is permitted for resale," compare the refund status field, and when that field is empty record the claim as unsupported.
- Associating knowledge bases across different vector models. The documentation requires associated knowledge bases to use the same vector model. Check: verify each base's model identifier, exclude those that do not match, and record the actual observation without inferring runtime results.
- Confusing signing, cooperation reaching agreement, and contract effectiveness. Check: compare the same material field by field, judge a contradiction only when the same field carries clearly mutually exclusive values, and record fields that are not stated as pending verification.
How to verify the setup
- Verification one: input the hypothetical source text "returns inbound record RT-2025-0310-017 states a signed-in quantity of 1 item; the grade determination table states grade B; the refund status field in the system is empty," then compare the draft sentence "this return has been refunded and is permitted for resale" against the refund status field in the source. When that field is empty the condition is met, the claim is recorded as unsupported, and it is not judged a contradiction.
- Verification two: input the question "what is the grade of RT-2025-0310-017" under full-text retrieval mode, then compare whether the returned citations include grade determination table content. The condition is met when the returned citations include grade B. When an empty array is returned, record that result and check whether the data has completed training.
- Verification three: input a mixed text-and-image query containing the inspection photograph, then compare whether the returned citations include image description results or image vector results. The condition is met only when the model has the corresponding capability and an image index was built at ingestion time and the returned citations include the corresponding content. Otherwise record the current capability as insufficient.