What the documents look like
For product questions in Convenience fresh-food orders, this page examines FastGPT vector indexes. A large-portion photo example and an order unit do not supply actual serving specifications. The examples are hypothetical.
Constraints at the vector indexes step
A proposed fresh-food photo note says the large portion is illustrative, while an order table records boxes and refers serving size to a product booklet. A question about each box matching the pictured portion lacks actual size evidence. An entry cannot attach large portion to the sale item from the image note, and a box unit cannot be converted into serving contents without support. Preserve photography source, ordered subject and booklet reference separately. A common dish name cannot establish equivalence between a visual example and the specifications used for this order.
Configuration and implementation checks
| Original-data/index check | Specific subject relationship | Acceptance action |
|---|---|---|
| Illustration identity | Large portion for visual use | Do not turn into sold size. |
| Recording unit | Box | Do not invent contents conversion. |
| Specification source | Serving detail in booklet | Obtain actual entry. |
| Period correspondence | Another menu period may differ | Check item and version. |
| New-entry adoption | Actual target after booklet addition | Inspect original and citation. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Embedding model | Knowledge bases associated with one search node must use the same embedding model. |
Original data and indexes | One original data block can have multiple indexes. |
Index content | Index wording should be complete and focused. |
Image indexes | Where image retrieval is used, inspect descriptions or image vectors. |
Version and result check | After a change, reuse questions to compare originals. |
Three common errors
First, an illustrative portion may be indexed as a sales specification; inspect the actual booklet entry. Second, an each-box question may return only box-based registration; inspect whether original data includes serving detail rather than a unit alone. Third, specifications from another menu period may be used here; inspect item, period and applicable version. These require attribute provenance, information coverage and subject-time correspondence. A visual or entry name cannot determine actual contents, and the exercise must not add weights, quantities or findings about the physical food.
How to verify the setup
Acceptance should ask what the note means, how orders are recorded and which source confirms actual portions. Keep entry and citation scope visible. The illustration and box unit can be explained, while serving detail still needs the booklet. After adding it, inspect item applicability and actual processing before testing whether indexes reach the new entry. Upload does not establish adoption. Several entries containing box or large portion still need every original read; repeated photography notes cannot be counted as independent specification evidence or as confirmation of the order contents.
| Material context | Scope |
|---|---|
| Document types | Fresh Food Order Form, Spoilage Report, Near-Expiry Processing Sheet and Temperature Records |
| Candidate sources | POS Sales, Headquarters Ordering System, Store Inventory and Cold Chain Temperature Control Records |
| Technical baseline | FastGPT v4.17.2 |