What the documents look like
Amazon FBA preparation moves inventory ahead of demand into platform warehouses, so the operating risk concentrates on inbound receiving and inventory age. The working documents are FBA inbound plans, carton labels, inventory reports and compensation claims. Source systems include Amazon Seller Central, FBA inventory reports, freight forwarder tracking and ERP purchase orders. When product and marketing copy drafting cites this material, retrieval typically reaches inbound lead times, carton label specifications, inventory status and claim progress.
The brief records daily and weekly update frequencies for these materials. This page does not change its retrieval logic because of update frequency. Whether a specific promotional sentence is supported by the material depends on checking the original text for subject, object, status and validity scope.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Search modes are semantic retrieval, full-text retrieval and hybrid retrieval. Hybrid retrieval merges multiple result paths through RRF. Result reranking uses a ReRank model. The citation cap is measured in tokens. The minimum relevance threshold takes effect only under semantic retrieval or when result reranking is used. Question optimization handles coreference resolution and question expansion, and it adds an extra model call.
Semantic recall and relevance scores do not perform permission or entity isolation. The same-vector-model requirement applies to knowledge base association only, and it does not extend to external systems. Image retrieval requires corresponding image input and model capability. Reranking requires a model and is used according to configuration. The optional paths shown in the documented flow diagram do not mean every request executes all of them.
Hypothetical paper exercise sample
Material text for cross-checking:
- An FBA inbound plan for a batch states: planned inbound date 10 March 2025, destination warehouse ONT8, carton label version V2.
- The inventory report for the same period states: 600 units in transit for this batch, 0 units available for sale.
- A compensation claim record states: for 50 units lost in February 2025, the application status is pending review.
Draft sentence to be assessed: This batch has been fully received and is ready to ship immediately.
Configuration and implementation checks
| Configuration item | Purpose on this page | Hypothetical starting point and reason for this exercise |
|---|---|---|
| Associated knowledge bases | Associate the knowledge bases holding inbound plans, carton labels, inventory reports and compensation claims | First check each knowledge base's vector model identifier, associate only those with matching identifiers, exclude and record mismatches |
| Search mode | Compare semantic, full-text and hybrid retrieval on the sample question | For queries containing dates, warehouse names and label versions, try full-text retrieval first; for natural-language phrasing, try semantic retrieval; do not assume one mode fits all material |
| Citation cap | Control the total tokens of returned citations | Set a hypothetical starting point of 2000 tokens because this batch includes tables and identifiers; compare with 1000 tokens to observe whether inbound date, warehouse name and quantity are truncated |
| Minimum relevance | Filter low-relevance results | Label relevant and irrelevant samples first, then adjust the threshold; it takes effect only under semantic retrieval or result reranking |
| Question optimization | Handle coreference resolution and question expansion in follow-up turns | Compare the original question with the optimized question and check whether the subject was altered; it can be turned off when the current question is already clear |
| Result reranking | Reorder candidate text | Use when a ReRank model is available and enabled per configuration; the rerank score can be used for filtering |
Three common errors
- Treating "fully received" in the draft sentence as a fact supported by the material. The material states only the planned inbound date, destination warehouse and carton label version; it does not state actual inbound completion. The check inputs the draft sentence and the material text, compares whether subject, object and status match, and observes whether any field can support "fully received." When the field is not stated, record it as unsupported.
- Treating "pending review" on the compensation claim as approved or already paid. Pending review does not demonstrate absence of operating authority, and it does not equal completed approval. The check inputs the claim record, compares the status field value, and observes whether an approval or payment record exists. When only pending review is present, record it as pending verification.
- Associating knowledge bases with different vector models directly. The check inputs each knowledge base's vector model identifier, compares whether the identifiers match, excludes mismatches and records them. Do not run cross-model retrieval to compare missed recalls.
How to verify the setup
- Input the hypothetical material text above and the draft sentence "This batch has been fully received and is ready to ship immediately." Compare whether the material states actual inbound completion status, and observe whether the retrieval result returns only the planned inbound date, destination warehouse and carton label version. If the returned content contains no actual inbound completion field, judge the draft sentence unsupported.
- Input the compensation claim record, compare the application status field value, and observe whether the retrieval result returns "pending review" and whether an approval or payment record exists. When only pending review is present, judge it pending verification and preserve the evidence gap.
- Input each knowledge base's vector model identifier, compare whether the identifiers match, and observe whether the association operation includes only knowledge bases with matching identifiers. For empty results, use the explicit paper input [] to check the downstream design; do not claim that a fictional rule's retrieval necessarily returns []. System errors or automatic business actions not stated in the documents remain unknown.