What the documents look like
Receiving and put-away covers the first book-to-physical confirmation when goods enter the warehouse. Data comes from the WMS, ERP purchasing, supplier delivery notes and barcode scanning. The daily documents are purchase arrival notes, receiving inspection sheets, put-away tasks and discrepancy reports. The retrieval objects for product data and selection questions are the item names, specifications, barcodes, suppliers and location information that appear batch by batch in those documents. Checking arrived quantities, printing barcodes and assigning locations take the most time, and retrieval precision affects whether a receiving clerk can quickly find the supporting product record. Material updates follow a per-batch and daily rhythm; this page does not derive batch synchronization, real-time or transaction-triggered mechanisms from that rhythm.
For vector indexes, the practical point is that a knowledge base search runs against an entire library, while collections inside it only group data and do not change search results. A single data block can carry several indexes, so the index and the source content are checked separately. Image retrieval depends on whether an image description or image vector index was generated at ingestion time; uploading an image at query time is not sufficient on its own.
Constraints at the vector indexes step
Knowledge bases linked for retrieval must use the same vector model. Check the actual model and its input type first, and exclude libraries whose model identifiers do not match. A group of source data can be associated with multiple indexes; verify the index-to-source relationship rather than assuming one index per block. Build indexes from complete and focused information, and weigh retrieval coverage against index length using samples.
Semantic retrieval relies on vector distance. Full-text retrieval suits numbers, models and proper nouns. Hybrid retrieval combines both through RRF, and reranking requires a model and must be enabled according to settings. The minimum relevance value is a filter threshold that takes effect only under semantic retrieval or when result reranking is used; it does not perform permission or entity isolation. Retrieval is recall, not authorization.
The documented flow lists optional paths that do not all execute on every request. Image retrieval requires a corresponding image input and model capability. Reranking requires a rerank model and its configured enablement. Cross-model library association is not permitted, so verify identifiers, exclude non-matching libraries and record what is observed instead of running cross-model retrieval to compare missed recalls.
Configuration and implementation checks
Hypothetical drill sample: a purchase arrival note states "Supplier A, arrival on 12 March 2025, Product A, specification 500 ml, expected 200 cases." A receiving inspection sheet states "actually received 198 cases, discrepancy 2 cases, pending confirmation by discrepancy report." A draft sentence awaiting judgment reads "the arrived quantity has been fully confirmed and put-away can be completed." The table below lists hypothetical starting points and check items for this drill; these are not official defaults.
| Configuration or check item | Hypothetical starting point and reason | Comparison method |
|---|---|---|
| Linked knowledge base | Link only the product data library with the same vector model | Check the model identifier and exclude mismatches |
| Search mode | Start with hybrid retrieval to cover item names and barcodes | Compare against semantic and full-text results |
| Reference limit | Control by tokens; start with a smaller value to observe trade-offs | Compare with a larger value for source completeness |
| Minimum relevance | Start at a middle value and observe what remains after filtering | Compare with a lower value to see whether the discrepancy record is kept |
| Index content | Build separate indexes for the inspection sheet and the discrepancy report | Check the index-to-source correspondence |
| Image index | Start disabled; confirm whether ingestion generated one | Check the ingestion-side index field |
Three common errors
- Treating "pending confirmation by discrepancy report" as a confirmed quantity. The inspection sheet states 198 cases received and 2 cases pending, while the draft sentence says the quantity is fully confirmed. The same field holds mutually exclusive values, which is a contradiction.
- Treating a retrieval hit as authorization to release. Returning the arrival note text only shows that recall succeeded. It does not mean the discrepancy has been handled or put-away has been approved.
- Linking knowledge bases across different vector models. The documents require the same vector model. Libraries with non-matching model identifiers should be excluded and recorded, without running cross-model retrieval to compare missed recalls.
How to verify the setup
- Input the purchase arrival note and inspection sheet text above, then compare the draft sentence "the quantity has been fully confirmed" with the inspection sheet's "discrepancy 2 cases pending confirmation." The condition is met if the result is judged contradictory and the discrepancy is kept pending verification. It is not met if the result is judged ready for put-away.
- Input "Product A specification 500 ml" as the search term and compare the returned reference with the arrival note text. Recall is satisfied if the reference contains that subject and specification. If an empty array is returned, record the miss without inferring that the rule necessarily returns empty.
- Input a handover request "please complete put-away according to the inspection sheet" and compare the request status with the authorization boundary. The condition is met if it is identified as pending review and not approved. It is not met if it is treated as already executed. An empty-result downstream design can be checked with the paper input [], without claiming that retrieval necessarily returns [].
After any model or index adjustment, compare references against the same set of questions rather than relying on vector scores alone.