What the documents look like
E-commerce warehouse receiving and putaway handles the first physical-versus-record confirmation when goods enter the warehouse. The main data sources are the WMS, ERP procurement, supplier delivery notes, and barcode scanning. The documents handled day to day include purchase arrival notes, inbound acceptance notes, putaway tasks, and discrepancy reports. These documents determine the retrieval scope for product information and selection questions.
A purchase arrival note records the arrival batch and quantity. An inbound acceptance note records the actual received quantity and any discrepancy. A putaway task records the assigned storage location. A discrepancy report records quantity or quality exceptions. Checking arrival quantity, printing barcodes, and assigning storage locations are the three most time-consuming steps. Records update per batch and per day. Document parsing and chunking must preserve document identity and version so that retrieval results can point back to the source text.
Because receiving is the first account-versus-physical confirmation, a chunk that mixes a planned quantity with an accepted quantity can produce a product answer that contradicts the acceptance record. Chunking therefore has to keep each document's own field values attached to that document's identity.
Constraints at the document chunking step
Segmentation completeness requires checking paragraphs against the structure of the specific document. Conditions, explanations, and the content they constrain must not be split apart. Data and indexes need to be distinguished: in MongoDB, the dataset.datas table stores the original data, and the indexes field records the corresponding vector IDs. One group of data can correspond to multiple vectors. During retrieval, vectors are recalled first, then the original data is found in MongoDB by vector ID. When the same group of original data is matched, the results are merged and the highest score is taken.
Semantic retrieval depends on vector distance. Full-text retrieval depends on keyword matches. Hybrid retrieval combines both through RRF. The minimum relevance value takes effect only during semantic retrieval or result reranking, and it is a filter threshold. Retrieval or form collection does not constitute authorized execution. Business verification, approval, database writes, and external synchronization each need their own downstream implementation.
Chunking also interacts with search mode choice. A barcode or purchase order number is a precise string, so full-text retrieval or hybrid retrieval is the suitable path for those queries, while a natural-language question about a product's handling is better served by semantic retrieval. The chunk boundary determines whether the number and the quantity it belongs to stay in the same retrieved unit.
Configuration and implementation checks
Hypothetical drill sample: on 10 March 2025, a receiving clerk at an e-commerce warehouse receives a supplier delivery note. The source text reads: "Purchase arrival note PO-2025-0310-17, product A barcode 6901234567890, expected quantity 120 pieces; inbound acceptance note YS-2025-0310-17 records actual received 118 pieces, discrepancy 2 pieces pending supplier confirmation." A draft sentence awaiting judgment reads: "This batch of goods has completed inbound putaway, inventory quantity is 120 pieces." Judgment requires the same subject, same period, and same field. The acceptance note's actual received quantity of 118 pieces and the draft sentence's 120 pieces are mutually exclusive values in the same field. The confirmation status of the 2-piece discrepancy is not stated and remains pending verification.
| Configuration item | Setting or implementation check relevant to this page | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, hybrid retrieval | Barcode 6901234567890 suits full-text retrieval |
| Minimum relevance | A 0-1 value, effective only in semantic retrieval or result reranking | Hypothetical starting value 0.3 for this drill, compared against 0.5 |
| Reference upper limit | Each search references at most n tokens of content | Controls returned content length |
| Question optimization | Coreference resolution and question expansion before retrieval | Can be enabled for short questions, adds latency |
| Result reranking | ReRank secondary ordering, score 0-1 | Used when candidates are numerous |
| Index check | Distinguish original data from vector indexes | Confirm training state after document changes |
The hypothetical starting value of 0.3 is chosen for this drill because the acceptance note is short and field-specific; it is compared against 0.5 to observe whether the discrepancy sentence is filtered out. This is a drill starting point, not an official default.
Three common errors
- Treating a successful upload as retrieval already updated. The input is the upload record after a document change; the comparison is the actual training and index state; the observable result is whether retrieval can recall the new content. A successful upload and an updated index are different facts.
- Treating minimum relevance as a permission or entity filter. The input is arrival notes from different suppliers; the comparison is whether returned results contain only the target supplier; the observable result is whether unrelated records appear. A similarity score does not carry authorization responsibility.
- Writing RRF fusion after hybrid retrieval as a toggle. The input is the two result paths from semantic and full-text retrieval; the comparison is the ordering before and after fusion; the observable result is whether content hit by multiple paths ranks higher. RRF is a fusion process, not a standalone switch.
How to verify the setup
- Verification one: use "6901234567890" as input, compare whether the returned content includes the purchase arrival note and acceptance note corresponding to that number, and observe whether mutually exclusive values in the same field appear in the result. If 120 pieces and 118 pieces appear side by side, the discrepancy pending-verification state must be preserved. A first-item hit, full recall, or empty result for unrelated queries cannot be guaranteed.
- Verification two: use "has the 2-piece discrepancy been confirmed" as input, compare whether the returned content includes a discrepancy report or confirmation record, and observe whether a confirming party and date appear in the result. When the confirmation status is not stated, record it as pending verification; it cannot be judged as completed inbound putaway.
- Verification three: use the empty input [] to check the downstream design, compare the system's output behavior when there are no retrieval results, and observe whether an empty reference is returned. Empty-result handling needs a clearly specified paper input; a fabricated rule that retrieval always returns an empty array cannot be claimed.
Across these checks, retrieval is not authorization. Any business verification, approval, database write, or external synchronization remains a separate downstream implementation, and the material needed for verification must be inside an accessible target retrieval scope or a clearly specified manual input. When it is missing, record the gap.