What the documents look like
Fresh-food weighing and sorting happens inside warehouse fulfilment, where the working units are weight and specification rather than order aggregation. The records that matter here come from the warehouse management system, electronic scales, the order system and quality-inspection photos, and they refresh per order and per day. For procurement and supplier records, the relevant materials are picking task sheets, weighing labels, short-weight compensation forms and quality-inspection records.
Citation tracing answers a narrow question: for a given supplier record or purchase record, which material, which version and which status does it come from. A picking task sheet may show a supplier batch and a target weight; a weighing label may show the actual weight recorded at the scale; a short-weight compensation form may show an adjustment tied to one order; a quality-inspection record may show a photo and a disposition. None of these documents, on its own, states that a supplier is approved, that a contract has taken effect, or that a quotation remains open for supply. The tracing step keeps those distinctions visible instead of collapsing them into a single status.
Constraints at the citation tracing step
The dataset search node can associate one or more knowledge bases that share the same vector model. This association is a retrieval scope, not an authorization grant. The node outputs cited content as an array, and the length may be zero; an empty array still lets the downstream path run. The minimum relevance value is a similarity threshold that applies only in semantic search or when result re-ranking is used. It does not filter by permission, entity or business category. RRF is the fusion step for hybrid retrieval results, not a switch that can be turned on or off. Image retrieval requires image input and a model with the corresponding capability, and re-ranking requires a re-rank model enabled in settings. These optional paths are not executed on every request.
A hypothetical paper exercise sample: the knowledge base holds a document titled "Supplier A qualification material (March 2026 version)". The original text reads: "Supplier A, unified social credit code sample value, business licence valid until March 2027, quotation valid until June 2026." A draft sentence under review reads: "Supplier A currently holds valid supply qualification and may continue supplying at the quoted price." Checking compares subject, period and field. The material states a business licence validity and a quotation validity. It does not state a supply-qualification determination or a contract-effective status. The draft sentence remains pending verification.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Associated knowledge bases | Select only the supplier material base with the same vector model | Compare the vector model identifier against another candidate base; exclude mismatches |
| Search mode | Hybrid retrieval | Compare against a semantic-only result; identifier-like terms rely more on full-text hits |
| Minimum relevance | Set to 0.3 for this exercise | Compare against 0.5 and observe changes in recalled items and unrelated fragments |
| Citation limit | Enter a starting token value for this exercise | Compare against a larger value and observe total cited content length |
| Citation output | Accept the array, empty allowed | Compare against an explicit empty array [] and check whether the downstream path continues |
| Material timeliness check | Verify version date item by item | Compare against a historical version document and confirm which version is used |
The numbers in the table are hypothetical starting points for this exercise, not official defaults.
Three common errors
- Treating the minimum relevance value as a permission filter, expecting that raising it will exclude unrelated businesses or unauthorized records. The value is a similarity threshold; permission isolation needs a separate system implementation.
- Treating retrieval output as an already-authorized supply approval or an already-effective contract. Retrieval is recall. Authorization and effectiveness status require manual verification or downstream system confirmation.
- Forcing knowledge bases with different vector models into a shared association. Check the model identifier first, exclude mismatches, and record the actual observation rather than inferring runtime behaviour.
How to verify the setup
- Input the hypothetical sample original text and the draft sentence, and compare subject, period and field. The original states business licence validity to March 2027 and quotation validity to June 2026, and does not state supply qualification or contract effectiveness. The draft sentence is judged pending verification.
- Input a retrieval request and observe whether the citation array returns and whether returned content maps item by item to the original text. With an empty array, check whether the downstream design continues. Do not claim that a fictional rule will definitely return an empty result.
- Input historical version material for the same supplier and compare version dates and field values. Judge a contradiction only when two versions give mutually exclusive values for the same field. When only related-stage evidence exists, record it as unsupported or pending verification.