What the documents look like
Convenience-store fresh food is ordered by the store at short shelf-life and high frequency. Store staff draw on POS sales, the head-office ordering system, store inventory, and cold-chain temperature records to produce fresh-food order forms, damage and loss reports, near-expiry handling sheets, and temperature logs. Because updates occur daily and per shift, a single store's order form and loss report may exist in several versions within one day. Document parsing and chunking must therefore retain a document identity and version that can be traced back to the source text; otherwise a comparison cannot determine whether two materials belong to the same shift scope.
The comparison task aligns subject, object, quantity, status, and validity scope across order forms, loss reports, near-expiry handling sheets, and temperature records. Chunking decides whether those fields can be extracted completely and whether a condition stays attached to the content it constrains.
Constraints at the document chunking step
Chunking follows the structure of each specific material. A condition, note, or qualifier must not be split away from the content it constrains. One group of source data may map to multiple indexes; adding indexes does not change the ownership of the source data. Retrieval returns candidate content and does not perform authorization judgment. The minimum relevance value is a filtering threshold and does not constitute a permission or entity boundary. Hybrid retrieval merges multiple result paths through RRF; RRF is a fusion process and is not configured as an on/off switch. Image retrieval requires the corresponding image input and model capability, and reranking requires a model and must be enabled according to settings. The optional paths shown in the overall document flow diagram do not mean every request executes all of them. Libraries built on different vector models cannot be associated together; the only available action is to check identifiers, exclude non-matching ones, and record the actual observation.
Hypothetical drill sample: the original text of a fresh-food order form for store A, morning shift, dated 2026-03-10, reads "Store A, morning shift, 2026-03-10, fresh-food order: rice balls 120 units, sandwiches 80 units, delivery date 2026-03-11." The original text of the loss report for the same store and same shift on the same date reads "Store A, morning shift, 2026-03-10, loss: sandwiches 6 units, reason near expiry." The draft sentence to be assessed is "Store A reported a loss of 6 sandwiches on the morning shift of 2026-03-10 and has completed the 2026-03-11 order confirmation." This draft sentence combines the loss fact with an order confirmation. The order form original text records only the order quantity and delivery date; it does not record a confirmation status.
Configuration and implementation checks
| Configuration or check item | Value or action on this page | Basis and boundary |
|---|---|---|
| Search mode | Hybrid retrieval; semantic and full-text recall merged through RRF | Official description states hybrid retrieval merges two result paths with RRF |
| Minimum relevance | Hypothetical trial starting point 0.3, compared with 0.5 to observe candidate changes | The value takes effect only in semantic retrieval or with reranking; it is not a permission filter |
| Reference cap | Set in tokens; hypothetical starting point 2000 tokens | Official control uses tokens, not top k |
| Chunk check | Inspect each order form and loss report so conditions and quantities stay attached | Segmentation completeness requirement; a manual check action |
| Index check | Confirm the correspondence between source data and indexes; separate source data from indexes | One group of source data may map to multiple vectors |
| Post-update review | After a material change, check the actual training and index state | A successful upload does not equal an updated retrieval state |
Three common errors
- Treating minimum relevance as a permission filter. Input: store A's morning-shift order form and loss report. Compare the relevance setting with the record ownership identifier. Observable result: records whose store identifier does not match may still be recalled, because relevance does not perform entity isolation.
- Treating a successful upload as an updated retrieval state. Input: the upload record of the 2026-03-10 loss report. Compare the upload action with the actual training and index state. Observable result: while the index state has not updated, retrieval still returns old content.
- Treating the order confirmation in the draft sentence as having occurred. Input: the draft sentence above and the order form original text. Compare "has completed the order confirmation" with the order quantity and delivery date recorded in the original. Observable result: the original records no confirmation status, so the statement lacks a basis.
How to verify the setup
- Input the hypothetical sample's order form original text and loss report original text. Compare store, shift, date, object, and quantity across the two materials. Observable result: for the same store and shift, sandwich quantities are recorded separately as 80 ordered and 6 lost; the two do not conflict.
- Input the draft sentence to be assessed and compare it sentence by sentence with the two originals. Observable result: "reported a loss of 6 sandwiches" can be traced back to the loss report, while "has completed the 2026-03-11 order confirmation" has no corresponding record in either original and remains unverified.
- Input an empty reference result and check how the downstream design handles the no-candidate case. Observable result: the empty result is handled by the established downstream logic; no invented rule is assumed to always return empty. For the failure branch, when retrieval misses or the index has not updated, record the actual observation; first-hit or full recall is not guaranteed.