What the documents look like
Store cold-chain receiving is organized around each arriving batch. The working materials are cold-chain temperature records, arrival acceptance sheets, rejection sheets, and supplier delivery notes. Data originates from vehicle temperature control, WMS, the store receiving system, and quality-inspection photographs. Compared with fruit purchasing, cold-chain receiving focuses on transport temperature and arrival batch, and does not cover origin price inquiries. These materials set what may and may not appear in product and marketing copy.
In FastGPT v4.17.2, the form input node is a user-interaction node. When triggered, the conversation enters an interaction state, the workflow state is recorded, and execution waits until the user completes the interaction; after submission, the collected form information is passed to subsequent nodes. The user selection node is also a user-interaction node. When triggered, the conversation enters an interaction state and the workflow state is recorded; when the user makes a choice, the node evaluates that choice and executes the “yes” branch. Neither node includes a business database function. Field validation, approval, write-back, and batch operations require an external system or subsequent nodes.
Constraints at the interactive forms step
The material fields needed for copy drafting must be collected through interactive nodes. The nodes do not judge whether a material is authentic. Values from the temperature record, acceptance sheet, rejection sheet, and delivery note enter downstream processing after form submission; whether downstream checks them and how approval proceeds are separately implemented business actions. The user selection node determines branch direction and does not act as a personnel directory or a business-data selector.
Hypothetical paper exercise sample: a store receiver submits materials for one arriving batch. The supplied source text is: “The cold-chain temperature record shows 2 to 8 degrees Celsius throughout transport; the arrival acceptance sheet states the batch contains 40 boxes and 38 boxes passed acceptance; the rejection sheet states 2 boxes were rejected because of packaging damage; the supplier delivery note states the same batch contains 40 boxes.” One draft sentence to be assessed is: “This batch of fresh goods was cold-chain throughout and all passed acceptance.” The subject required for assessment is the batch; the object is temperature and acceptance status; the effective scope is the 40 boxes of the batch. The materials show 2 boxes rejected, so the draft sentence’s “all passed acceptance” does not agree with the rejection sheet record. The sentence is unsupported.
Configuration and implementation checks
The table lists only official documented behavior relevant to this page’s primary task, or implementation checks. The hypothetical starting point is used only for this paper exercise and is not an official default value.
| Configuration item | Nature | Relation to this page’s sample |
|---|---|---|
| Form input node | Official documented behavior | Collects temperature record, acceptance sheet, rejection sheet, and delivery note fields and passes them downstream |
| User selection node | Official documented behavior | Executes a branch according to the choice, determining which subsequent path the copy takes |
| Required-field check | Implementation check | Check whether batch, quantity, temperature range, and rejected quantity are complete in the sample |
| Interaction state waiting | Official documented behavior | After the node is triggered, state is recorded and execution waits for the interaction to finish before continuing |
| Downstream validation division | Implementation check | Field validation, approval, and write-back are implemented by an external system or subsequent nodes |
| Hypothetical starting point: draft-sentence assessment threshold | Hypothetical starting point for this exercise | Chosen as “literal comparison against the source material text,” because this task only needs to check whether a promotional sentence is supported by the materials, compared with the rejection sheet quantity field |
Three common errors
- Treating a temperature value collected by the form input node as an already verified fact. The node only collects and passes values; verification requires separate implementation. Check: input the sample temperature record source text, compare the node output field with the source value, and observe whether only transfer occurred without any verification marker.
- Treating the branch result of the user selection node as a business approval conclusion. The node executes a branch according to the choice and does not produce approval effect. Check: input the sample choice on whether to submit a rejection sheet, compare the branch direction with approval records, and observe whether an independent approval document exists after branch execution.
- Treating “all passed acceptance” in the draft sentence and “38 boxes passed acceptance” on the acceptance sheet as equivalent. The quantity scopes differ. Check: input the draft sentence and the rejection sheet source text, compare the passed box count with the total box count, and observe whether the difference caused by 2 rejected boxes is identified.
How to verify the setup
- Input the four material source texts and the draft sentence from the hypothetical sample. Compare the draft sentence “all passed acceptance” with the rejection sheet statement that 2 boxes were rejected because of packaging damage. The expected observation is that the quantity-scope mismatch is identified and the sentence is assessed as unsupported. This is a paper expectation and was not actually tested.
- Input the sample materials and trigger the user selection node. Compare the choice result with the subsequent branch. The expected observation is that the conversation enters an interaction state, records state, and continues downward after the choice. If the branch does not follow the choice, record the actual observation and do not infer a cause.
- Input the sample materials with the temperature range field missing. Compare the required form fields with the submission result. The expected observation is that submission cannot be completed when a required field is missing, or downstream receives an empty value; the specific behavior depends on actual node behavior. If downstream design needs to accept an empty result, a paper input of [] can be used for checking, without claiming that retrieval necessarily returns [].
For retrieval and association, only libraries using the same vector model can be associated together. For cross-model libraries, check the identifier first and exclude those that do not match; do not run cross-model retrieval to compare missed recalls. A relevance score does not perform permission or entity isolation, and retrieval does not constitute authorization. Optional paths shown in the documented flow diagram, such as image retrieval and reranking, require the corresponding input and model capability and do not mean every request executes all of them.