What the documents look like
Overseas stock replenishment management for third-party warehouses serves several sales channels from one pool of stock, and the seller assigns that stock across channels. The working materials come from warehouse management systems, ERP sales forecasts, freight forwarder systems and carrier bills. The documents handled day to day are replenishment plans, inbound appointment records, inventory ageing tables and last-mile delivery orders. Copy drafting for products and marketing draws on these documents to check that a proposed sentence about quantity, timing, cost or responsibility can be traced to source wording. Material updates are weekly or daily. This page does not derive any automatic synchronization mechanism from that cadence.
Three features of this category shape the retrieval step. One pool of stock serves multiple channels, so a retrieved passage may describe a channel other than the one a draft sentence names, and the retrieved text does not by itself decide which channel is in scope. Replenishment plans state intended arrangements, while delivery orders and carrier bills record later stages, so a draft sentence can move a plan into a completed outcome that the plan text does not contain. Inbound appointments and inventory ageing tables carry dates and quantities whose scope is limited to a named warehouse and a named batch, so a draft sentence that drops the warehouse or the batch is not supported by the passage it cites.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases with different vector models cannot be associated together. At the check stage, confirm the vector model identifier and exclude knowledge bases that do not match; do not run a cross-model retrieval to compare recall differences.
Search mode is compared across semantic retrieval, full-text retrieval and hybrid retrieval using the same test question. No single mode is assumed to suit all materials. The citation limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used, and it is a filter threshold rather than a final ordering rule. Question optimization performs coreference resolution and question expansion; compare the original question with the optimized question and check whether the subject has been altered. Image retrieval requires an image input and matching model capability. Reranking requires a configured model and must be enabled in the settings. When retrieval returns nothing, the cited content output has length 0 and that path still completes. Retrieval does not perform permission or entity isolation, and a similarity score does not constitute an authorization decision.
Hypothetical paper exercise sample: a replenishment plan text reads "This replenishment plan batch covers the German warehouse, the planned inbound date is 10 March 2025, and last-mile delivery is arranged by the contracted carrier within three working days after inbound." A draft sentence reads "The batch will complete last-mile delivery within three working days after inbound." The check compares subject, object and time scope between the draft sentence and the source. The source subject is a planned arrangement; the draft sentence states a completed result. These are not the same fact, and the draft sentence is not supported by the source text. The same sample is used in the configuration table and in verification below.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the replenishment plan and inbound appointment bases that use the same vector model | Confirm the vector model identifier first, exclude non-matching bases, then associate the remainder one by one |
| Search mode | Test semantic retrieval first, then hybrid retrieval | Use the same test question and compare the retrieved content under the two modes |
| Citation limit | Start at a token amount that covers one complete replenishment plan paragraph | Test with materials of different lengths and observe whether content is truncated, then adjust |
| Minimum relevance | Start at 0.5 for this exercise | Label relevant and irrelevant samples first, compare what remains after filtering, then adjust |
| Question optimization | Enable first in multi-turn follow-up scenarios | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable when candidates are numerous | Compare text result order before and after reranking, and confirm the model is configured |
The minimum relevance starting value of 0.5 is a hypothetical starting point chosen so that filtering behaviour can be observed against labelled samples; it is not a documented default. The citation limit starting value is chosen to cover one complete replenishment plan paragraph so that truncation is visible when a longer passage is retrieved; it is also not a documented default.
Three common errors
- Treating a retrieval score as a permission filter. Input a draft sentence that concerns only channel A, compare whether the retrieved content includes material belonging to other channels, and observe whether score level matches content ownership. The score does not perform entity isolation, so material ownership must be checked separately.
- Writing a planned arrangement as a completed result. Input the hypothetical sample source text and the draft sentence above, then compare subject and state fields. The source states a planned inbound date and an arranged delivery; the draft sentence states completed delivery, which is not supported.
- Associating knowledge bases across different vector models. Input two knowledge bases whose vector model identifiers differ, compare whether the identifiers match, and exclude the non-matching base. Do not run a retrieval across the two models to compare missed recall.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence, then compare subject, object and time scope fields. The completed state expressed in the draft sentence has no corresponding record in the source, so the result is recorded as unsupported and the evidence gap is noted.
- Input a test question containing an explicit identifier, and observe retrieved content under semantic retrieval and under hybrid retrieval separately. Compare whether the identifier is matched in full. If it is not matched, record the observation under that mode rather than concluding a definite failure.
- Input a paper empty result
[]and check how the downstream node is designed to handle cited content of length 0. Observe whether the downstream continues on the empty-citation path. Do not claim that a fictional rule retrieval necessarily returns an empty array.