What the documents look like
Wholesale stall trading is fragmented. Bargaining happens at the stall, goods move by consignment, and paperwork is spread across several steps: purchase slips, handwritten sales notes, stall lease contracts, and payment receipts. A single after-sales conversation may touch order quantities, consignment arrangements, and outstanding balance collection at the same time. Conversation logs therefore need two levels of identification. A chatId locates one conversation, and a dataId locates one message inside that conversation. Records update daily and per order, so log review is done per individual transaction rather than inferred from a batch total.
A hypothetical paper exercise sample is used below. It is not a real customer record and no request was executed.
Materials supplied for comparison:
"12 March 2025 handwritten sales note, item number A-17, quantity 30 pieces, unit price 45 yuan, total 1350 yuan, purchaser has collected the goods."
"14 March 2025 WeChat payment record, amount 800 yuan, note: partial balance payment."
"Stall lease contract term 1 January 2025 to 31 December 2025."
Draft sentence to be assessed: "This payment was settled in full on 14 March."
The sales note total and the payment record amount are the same field for the same counterparty and the same transaction period. The sales note states 1350 yuan; the payment record states 800 yuan. The two values conflict, so the draft sentence is not supported. The stall lease contract term concerns a different subject and does not serve as evidence for payment settlement.
Constraints at the conversation logs step
In the conversation interface, when chatId is a non-empty string, the system takes the conversation from the database and uses the last item in the messages array as the user question; the remaining messages are ignored. When chatId is not passed, context is built entirely from the supplied messages. The responseChatItemId, when passed, must be unique under the current chatId. With detail=true, intermediate values are returned; in non-stream mode the result is held in responseData, and in stream mode it is distinguished through event.
These conditions shape what a log can show. A log entry existing does not mean a compliance audit is complete. Reconciliation, retention, and regulatory requirements belong to a full business solution and are handled elsewhere. The conversation interface does not return actual token consumption; detail=true allows manual calculation from the tokens values inside responseData. The model and temperature fields passed in a request have no effect, because orchestration decides them.
Interaction nodes add a further condition. When a workflow reaches an interaction node it returns immediately. The interactive object returned to an external caller is a display configuration containing type and params; internal runtime fields such as entryNodeIds, memoryEdges, nodeOutputs, and nodeResponseId are not returned. A pending interaction is a waiting state, not a completed submission. Continuing the workflow requires another request with the same chatId.
Configuration and implementation checks
| Item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| chatId | "stall-20250312-01" | Kept under 250 characters and tied to one transaction reference; compare whether different entry points mix into the same chatId |
| responseChatItemId | "resp-20250314-01" | Must be unique under the current chatId; compare whether a repeated request reuses the same value |
| detail | true | Needed to inspect intermediate values in responseData; compare against false to see whether module status is absent |
| stream | false | Keeps the full response in one body for retention; compare against true to see how event separation affects what is stored |
| source | api | Retrieves conversations created through the API; compare whether page-created conversations are excluded |
| loadCustomFeedbacks | true | Reads custom feedback; compare against false to see whether feedback fields are missing |
These are hypothetical starting values for this exercise, not official defaults. The items correspond only to fields described in the conversation interface and conversation CRUD documentation. Database internals are not part of this page.
Three common errors
- Treating the messages-built context used when chatId is absent as if the conversation had been retrieved from the database. Check: send the same chatId in one request and omit chatId in another, then compare whether historyPreview matches. A mismatch shows the context source differs.
- Treating a detail=false response as a complete run record. Check: send the same question with detail=false and detail=true, then compare whether responseData is present. Its absence means intermediate values were not returned.
- Treating a similarity score as a permission or entity filter. Check: send the same query and compare whether every dataset_id in quoteList belongs to a knowledge base the caller may access. An unauthorized base appearing there is outside the intended scope. Retrieval is not authorization, and a relevance score is not a category, entity, or permission filter.
How to verify the setup
- Verification one. Input the hypothetical materials above together with the draft sentence "This payment was settled in full on 14 March." Compare the sales note total of 1350 yuan and the payment record amount of 800 yuan as the same amount field for the same counterparty and period. If the values differ, the draft sentence is not supported and the gap must be recorded. The lease contract term is not part of this comparison.
- Verification two. Input chatId "stall-20250312-01" with responseChatItemId "resp-20250314-01", then repeat under the same chatId. Compare whether the responseChatItemId value recurs. A recurrence means the uniqueness condition is not met.
- Verification three. Input detail=true with stream=false. Compare whether responseData contains module status and the complete result. If those are absent, intermediate values were not returned and the request parameters need review.
For an empty-reference case, a paper input of [] can be used to check downstream design. This page does not claim that a retrieval against a fictional rule set will return []. Cross-model association is not available; check identifiers first and exclude entries that do not match. Image retrieval requires a corresponding image input and model capability. Reranking requires a model and must be enabled by configuration. Retrieval checks describe observations and failure branches only; first-hit success and full recall are not guaranteed.