What the documents look like
Peak-sale warehouse and delivery scheduling revolves around short-term peak capacity and the arrangement of people, vehicles, and space. It differs from routine multi-warehouse inventory synchronization, which follows a daily balancing logic. The main inputs are historical peak-sale orders, platform campaign registrations, WMS capacity, and courier pickup capability. The documents handled day to day are peak-sale forecast tables, staff rosters, courier support plans, and exception daily reports. Forecasting order peaks, arranging temporary staff, and coordinating courier vehicles consume the most time. These materials update per campaign, while exception daily reports update daily. The people involved are e-commerce supply chain staff, warehouse managers, and courier commercial contacts. Errors can lead to warehouse overflow, late shipment, and a drop in campaign ratings.
For retrieval, one campaign can contain several documents that reference each other, and after-sales questions arrive as spoken follow-ups about scheduling, pickup, and responsibility for delays. The retrieval step has to return usable reference content from this material without treating a hit as any kind of confirmation.
Constraints at the retrieval step
A knowledge base consists of libraries, collections, and data. The smallest search unit is the library; collections only classify data and do not affect search results. The knowledge base search node can associate one or more knowledge bases that use the same vector model. Libraries with different vector model identifiers cannot be associated together. The correct action is to check identifiers and exclude those that do not match; there is no cross-model retrieval performed to compare missed recalls.
Vector storage uses the PostgresSQL PG Vector plugin with an HNSW index. The MongoDB dataset.datas table stores original data and an indexes field, so one group of data can map to multiple vectors. When vectors resolve to the same original data, the results merge and the highest score is kept. The retrieval chain can include question optimization, semantic retrieval, full-text retrieval, hybrid retrieval, image retrieval, RRF fusion, and ReRank reordering. These are optional paths and do not all execute on every request. Image retrieval requires image input and the corresponding model capability; reordering requires a model and must be enabled in settings.
Reference content is output as an array and its length can be zero. When nothing is found, the output path still completes. Semantic recall and relevance scores do not perform permission or entity isolation, and a retrieval result does not constitute authorization.
Configuration and implementation checks
Hypothetical drill sample: a warehouse forecast table states, "This campaign's expected order peak falls on the first day of the activity; temporary staff rosters cover the first and second days; the courier support plan applies to Courier A for overtime pickup at the first-day peak." A draft sentence under review reads, "Temporary staff rosters for this campaign cover the first day, and Courier A has confirmed overtime pickup." The differences between the draft and the source are the roster coverage range and the courier confirmation status. The table below is a hypothetical starting point for this drill and is not an official default.
| Configuration item | Hypothetical starting point for this drill | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only libraries containing the forecast table, roster, support plan, and exception daily report with matching vector model identifiers | Check vector model identifiers library by library and exclude mismatches |
| Search mode | Run one round each of full-text retrieval and hybrid retrieval first | Compare hits on identifiers, dates, and subjects before deciding |
| Reference cap | Set in tokens to a value covering a complete passage | Test with actual materials of different lengths to see whether content is truncated, then adjust |
| Minimum relevance | Applies only in semantic retrieval or when result reordering is used; label relevant and irrelevant samples first | Observe whether filtered references still contain irrelevant fragments |
| Question optimization | Enable for consecutive follow-ups and short questions | Compare the original question with the optimized question and check whether the subject was altered |
| Result reordering | Enable when candidates are numerous and a reordering model is configured | Compare relevance scores and ordering before and after reordering |
Three common errors
- Associating knowledge bases with different vector models. Input: two libraries whose vector model identifiers do not match. Compare their identifiers. If the identifiers differ and an attempt is still made to associate them together, that is a configuration error. Exclude the non-matching library and record the actual observation without asserting a runtime result.
- Treating a retrieval hit as authorization. Input: the draft sentence "Courier A has confirmed overtime pickup." Compare it with the source text "applies to Courier A for overtime pickup at the first-day peak." The source records only the application action and does not record a confirmation status. The confirmation conclusion in the draft is unsupported.
- Treating minimum relevance as a permission or entity filter. Input: a high-similarity but unrelated business record. Compare that record with the current authorization scope. Minimum relevance is a filtering threshold and does not exclude unrelated business records or unauthorized records.
How to verify the setup
- Input the forecast table source text and the draft sentence "Temporary staff rosters for this campaign cover the first day, and Courier A has confirmed overtime pickup." Compare the roster coverage range and the courier status fields. The source states coverage of the first and second days; the draft states coverage of the first day. These are mutually exclusive values in the same field, which constitutes a contradiction. The courier confirmation status is not stated in the source, so it is unsupported.
- Input a short question that contains no subject and no date, with question optimization enabled. Compare the optimized question with the source subject and object. If the optimized question replaces the subject or rewrites the date, the optimized result cannot be used for judgment and the original question must be checked again.
- Input a paper query with no matching material. The expected reference content is an array of length zero. Check whether the downstream path still completes. The retrieval check records observations and failure branches only. It does not guarantee a first hit, full recall, or that an unrelated query must return an empty array, and it does not claim that a fictional rule retrieval necessarily returns an empty array.
This page addresses only peak-sale warehouse and delivery scheduling, order fulfillment and after-sales Q&A, and knowledge base retrieval and recall. At operational handover, the authorization status of materials, actions, and requests must be established separately; retrieval and forms are not authorization execution.