What the documents look like
Store count and loss control work centers on book-to-physical differences and shrinkage. Data comes from POS inventory, WMS, monitoring systems, and the SB/T count specification. The daily materials are count sheets, variance analyses, shrinkage write-off approvals, and monitoring access records. This page covers contract and transaction material comparison: placing variance lists, write-off approval records, and monitoring access records into one retrieval scope alongside related contract or transaction materials.
Materials update monthly or quarterly. That frequency affects only when retraining and index review occur. It does not imply batch synchronization or transaction-triggered mechanisms. Loss prevention, store inventory staff, and financial audit are the typical users. Errors produce distorted inventory, inflated gross margin, or employee fraud. Chunking must therefore preserve subject, object, status, and validity scope so retrieved results can point back to source text.
Constraints at the document chunking step
Hypothetical drill sample: a store's March 2025 variance analysis states "Store A, count date 2025-03-31, SKU-4471 book quantity 120 units, physical count 96 units, variance -24 units, cause pending investigation." A same-month write-off approval record states "Store A, SKU-4471, write-off requested 24 units, approval status is pending review." The sentence to be checked is: "Store A completed the 24-unit write-off approval for SKU-4471 in March 2025."
During chunking, the conditional clause in the variance analysis and the approval status in the write-off record must stay in the same data block as their respective subject, object, and period. One set of source data can map to multiple indexes. A shorter index improves precision but sacrifices retrieval range. The retrieval library uses PostgresSQL with the PG Vector plugin as its vector retriever, indexed by HNSW. MongoDB's dataset.datas table stores source data and the indexes field. Semantic retrieval and minimum relevance affect only similarity ranking and filtering. They do not perform permission or entity isolation, and retrieval itself is not authorization. Image retrieval requires image input and model capability. Reranking requires a model and must be enabled by configuration.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting value | Reason for choice and comparison object |
|---|---|---|
| Search mode | Hybrid search | Sample contains identifier SKU-4471, requiring keyword matching; compared against semantic search alone |
| Minimum relevance | 0.3 | Hypothetical starting value, compared against 0.5, to observe whether pending-review records are filtered out |
| Reference limit | 1500 tokens | Hypothetical starting value, compared against 800, to observe whether variance analysis and write-off records co-occur |
| Result reranking | Enabled | Second-pass ordering when candidates are numerous; compared against disabled to observe ranking changes |
| Question optimization | Disabled | Sample question has a complete subject; observe retrieval results without enabling first |
| Chunking method | Segmented by material structure | Preserves subject, period, and approval status; compared against whole-document without segmentation |
Three common errors
- Reading "pending review" in the write-off approval record as completed approval, causing the checked sentence to be judged as supported. That status only indicates the request awaits processing.
- Splitting the cause condition from the variance value in the variance analysis into different data blocks during chunking. Retrieval returns only "variance -24 units" while losing "cause pending investigation," causing the evidence scope to be misread.
- Treating minimum relevance as a permission or entity filter, assuming a lower threshold excludes other stores' records. That parameter performs similarity filtering only.
How to verify the setup
- Input the sample source text and the checked sentence. Compare whether retrieval results contain both "cause pending investigation" from the variance analysis and "pending review" from the write-off record. The condition is satisfied when both source passages can be traced back and the status has not been rewritten.
- Use "SKU-4471" as full-text search input. Compare whether returned blocks preserve store, period, and variance value. The condition is satisfied when subject and fields remain complete within the same data block. If the return is empty, record the empty result and check downstream design. Do not infer that a fictional rule necessarily returns an empty set.
- Input "Store A March 2025 write-off approval." Compare returned content against the source approval status field. The condition is satisfied when returned text matches the source. The checked sentence is judged unsupported because the status does not match.
This function has verified public technical facts: check source structure and parsing results first, preserving document identity and version that can be traced back to source; inspect segmentation by specific material structure, avoiding separation of conditions, explanations, and the content they constrain; distinguish source data from retrieval indexes, where one set of source data can map to multiple indexes; retrieve separately using terms, identifiers, and natural language to check the effect of segmentation on actual questions; after material changes, verify actual training and index status, and do not treat successful upload as retrieval already updated.
This page addresses only: store count and loss control / contract and transaction material comparison / document parsing and chunking. During operational handover, which materials, operations, or requests are in what authorization state cannot be treated as authorized execution through retrieval or forms.