What the documents look like
Peak-sale warehouse and delivery scheduling revolves around short-term peak capacity and arrangements for people, vehicles and space. The rhythm differs from multi-warehouse inventory synchronization, which is a continuous reconciliation process. The main data sources are historical peak-sale orders, platform campaign registrations, WMS capacity and courier pickup capacity, updated per campaign and per day. In order fulfillment and after-sales question handling, this rhythm shapes multi-turn dialogue: a user in one session often follows up on the scheduling basis of a specific campaign, the staffing of a specific day, or a specific courier arrangement. The session has to carry the campaign, date, warehouse or carrier mentioned in the previous turn into the next turn. The documents listed for this category are the peak-sale forecast table, staffing roster, courier assurance plan and exception daily report. These four materials determine what an answer can cite and what it cannot.
Constraints at the multi-turn prompts step
Multi-turn dialogue requires checking the context that is actually carried in. In the chat interface, when chatId is a non-empty string, the system takes the session from the FastGPT database and uses the last item in the messages array as the user question, ignoring the remaining messages. When chatId is not passed, context is built entirely from the supplied messages. The memory turns setting in AI configuration controls how many earlier rounds of chat the AI may reference at most; customer service and knowledge-base question-answering applications usually keep a small number of history turns. Prompts need to state the material scope, the answering task and how to handle missing evidence, so that the model does not supply business facts on its own. Question optimization in continuous knowledge-base dialogue requires checking referent resolution and question expansion; not every short question is suited to it. Reply format follows what the downstream step consumes; choosing json_schema requires filling in the corresponding format requirement and verifying the returned structure. Retrieval returns relevant knowledge and does not constitute authorization. Forms collect values; business verification, approval, database writes and external synchronization each need their own stated downstream implementation.
Configuration and implementation checks
Hypothetical exercise sample: the source material reads, "On 18 June 2025 peak sale, East China Warehouse No. 1 has 42 staff scheduled that day, and the courier assurance plan sets carrier A's pickup ceiling at 8,000 pieces that day. The 17 June exception daily report records a two-hour sorting line stoppage at Warehouse No. 1." The draft sentence to be judged is, "On 18 June, Warehouse No. 1 has 42 staff scheduled, carrier A's pickup ceiling is 8,000 pieces, and it can take on all orders for that day." The point of judgment is that the material gives only the staffing number and the pickup ceiling, not the order volume for that day, and not any conclusion authorizing someone to approve taking on the orders.
| Configuration item | Hypothetical starting point for this exercise | How to check and why |
|---|---|---|
| AI model | Choose a model that supports the required context length | Compare the maximum context and tool-calling information shown in the configuration dialog against the length of the sample material |
| Memory turns | Start with a small number of turns | Compare the turns actually carried in historyPreview against the number of turns the sample follow-up needs |
| Temperature | Start with a low value | Customer service and knowledge-base scenarios are advised to be low; compare against the stability the draft sentence requires |
| Reply format | Keep the default | The sample has no downstream node reading a fixed structure, so json_schema is not selected |
| Stop sequence | Not set | The sample does not need output to end at a fixed marker |
| Multimodal recognition | Decide whether to enable it based on model capability | The sample material is text; after enabling, requests are still filtered by model capability before sending |
These values are hypothetical starting points for this exercise, not official defaults.
Three common errors
- Treating a retrieval hit as authorization. The input is the sample source material and the draft sentence; the comparison point is whether the material states the order volume and the approval to take on the orders; the observable result is that the material shows only the staffing number and the pickup ceiling, so "can take on all orders for that day" in the draft sentence is unsupported.
- Treating a similarity score as an entity or permission filter. The input is the
scoreanddataset_idinquoteList; the comparison point is whether the score changes the accessible range; the observable result is that the score reflects relevance only and does not exclude unrelated businesses or unauthorized records. - Treating a pending review as an existing operating permission. The input is a "pending review" status description in a handover request; the comparison point is whether that status equals approval; the observable result is that pending review does not show an absence of operating permission and does not constitute approval.
How to verify the setup
- Input the sample source material and the draft sentence; compare whether each assertion in the draft sentence has a basis in the source under the same subject, same period and same field; the observable result is that "42 staff scheduled" and "pickup ceiling 8,000 pieces" can be checked, while "can take on all orders for that day" remains to be verified because the order volume is missing.
- Input a follow-up containing a referent; compare the turns actually carried in
historyPreviewagainst the memory turns setting; the observable result is that if the campaign or date the follow-up refers to is not carried in, the answer cannot continue from the earlier text. - Input an explicit paper empty result
[]; compare how the downstream node is designed to handle an empty reference; the observable result is that how the downstream replies on an empty reference must be checked against the actual configuration, without claiming that a fictional rule retrieval necessarily returns empty.