What the documents look like
Store cold-chain receiving for fresh produce produces four core materials: cold-chain temperature records, arrival acceptance sheets, rejection sheets and supplier delivery notes. These materials are generated per arrival batch. The receiving and quality-inspection steps record temperature measurement results, sampling quality conclusions and rejection reasons onto the corresponding sheets. Temperature-control data comes from the refrigerated vehicle, acceptance and rejection sheets come from the store receiving system, and quality-inspection photographs are captured by the inspector and archived.
In the store service and internal training setting, these materials act as citation sources. Trainers extract original wording from the sheets to explain acceptance standards, rejection conditions and registration requirements to store staff. Citation tracing addresses one question: can a temperature value, acceptance conclusion or rejection reason quoted in training material be traced back to a specific batch, a specific sheet and a specific date range.
Two industry facts shape this task. First, the category difference from fruit purchasing is that cold-chain receiving focuses on transport temperature and arrival batch rather than origin price inquiry, so the citation evidence is temperature and batch identity, not sourcing negotiation. Second, the consequences of error include spoiled food, supplier disputes and regulatory penalties, so a quoted temperature range or acceptance conclusion that cannot be tied to one batch and one date carries real operational weight. Data update frequency is per batch and daily; where update frequency does not affect this page's function it is omitted.
Constraints at the citation tracing step
The knowledge base search node requires selecting one or more knowledge bases that share the same vector model. If cold-chain receiving materials are spread across several knowledge bases, the vector model identifier of each base must be checked first; bases with inconsistent identifiers cannot be associated together. The search node outputs cited content as an array, and the length may be zero. An empty array means the output path still runs through; it does not by itself mean the workflow failed or terminated.
The citation upper limit controls the maximum amount of content cited per search, expressed in tokens. Minimum relevance is a filter threshold that takes effect only under semantic search or result re-ranking. It does not perform permission filtering or entity isolation. Whether a retrieved result is adopted still requires manual checking against the valid version of the material.
A hypothetical drill sample for this page: a store received a cold-chain arrival on 12 March 2025. The supplier delivery note states batch number A-0312. The cold-chain temperature record shows a transport temperature of 2°C to 4°C. The arrival acceptance sheet records a sampling conclusion of pass. No rejection sheet exists. A draft training sentence reads: "The transport temperature of this batch stayed at 2°C to 4°C throughout, and acceptance passed." The question to judge: can the temperature range and the acceptance conclusion in this draft sentence be matched to original wording in the supplier delivery note, the cold-chain temperature record and the arrival acceptance sheet, with all three materials pointing to the same batch A-0312 and the same date 12 March 2025.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this page | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Select store acceptance knowledge bases whose vector model identifiers match | Check each base's vector model identifier first, exclude inconsistent ones, do not run cross-model retrieval |
| Search mode | Hybrid search | Temperature values and batch numbers are exact queries; semantic search supplements natural-language phrasing |
| Citation upper limit | Set to 2000 tokens | Hypothetical starting point for this page; compare whether different values cover the original wording of all three materials |
| Minimum relevance | Set to 0.5 | Hypothetical starting point for this page; compare whether filtering before and after omits the batch number or temperature value |
| Question optimization | Enabled | Training questions may omit the batch number; complete the reference before retrieval |
| Result re-ranking | Enabled | When candidates are numerous, re-sort; compare citation order before and after re-ranking |
These values are hypothetical trial starting points, not official defaults. The citation upper limit is measured in tokens and is unrelated to thinking budget, which is a reasoning budget and must not be reused as a citation token limit. RRF is a fusion process for combining multiple retrieval channels; it is not a switch to turn on. Optional paths in the overall retrieval flow, such as image retrieval or re-ranking, do not run on every request; image retrieval requires image input and matching model capability, and re-ranking requires a model and enabled settings.
Three common errors
- Treating minimum relevance as a permission filter. Minimum relevance is a similarity threshold; it does not distinguish which store or supplier a material belongs to. Check: input the same batch number and observe whether cited content under different relevance values still contains other batches or unrelated materials, confirming the threshold affects only similarity ordering.
- Treating an empty citation output from the search node as a workflow failure. The cited content array length may be zero, and an empty array still runs through the output path. Check: use a paper input of an empty array to review the downstream node design and confirm whether the downstream handles empty citations, without inferring that a fictitious retrieval necessarily returns an empty array.
- Associating knowledge bases that use different vector models. Associated knowledge bases require the same vector model, and cross-model bases cannot be associated together. Check: verify each base's vector model identifier, exclude inconsistent ones, and record the actually observed cited content without fabricating run results after cross-model association.
How to verify the setup
- Input the draft sentence from the hypothetical sample, "The transport temperature of this batch stayed at 2°C to 4°C throughout, and acceptance passed," and compare whether the retrieved cited content contains batch number A-0312, date 12 March 2025, the original temperature record wording and the original acceptance conclusion wording. Condition satisfied: the cited content shows all of these fields together and all point to the same batch and the same date.
- Input the same batch number A-0312 and date 12 March 2025, and compare the cited content returned under different minimum relevance values. Condition satisfied: the temperature value and acceptance conclusion in the cited content match the original wording of the three materials, with no material from another batch or another date mixed in.
- Use a paper input of an empty array to review the downstream node design and compare whether the downstream treats empty citations as normal output. Condition satisfied: the downstream node has a clear path for receiving the empty array and does not judge empty citations as retrieval failure or workflow termination.
These checks describe what to input, what to compare and what observable result satisfies the condition. They are not claims that any test, deployment or approval has occurred. A pending request is not approval, an offered option is not execution, and an unsigned termination is not a completed termination. Retrieval is not authorization; whether a cited result is adopted still depends on manual checking against the valid version of the material.