Temu semi-managed sellers: conversation logs for operating reviews

What the documents look like

Semi-managed sellers on Temu maintain overseas inventory and arrange shipments themselves, so the operating record is spread across Temu Seller Center, overseas warehouse WMS, logistics tracking and purchase cost sheets. The working documents for this page are the product declaration form, the overseas inventory sheet, the price declaration and the platform fulfillment order. These update daily and per order. For operating reports and exception reviews, the practical need is to place the inventory deduction, shipment status and price declaration for one fulfillment order under the same conversation record. Conversation logs and audit work at this step confirm that the session, run detail and field values behind a review actually come from that conversation, and that page sessions are not counted together with API sessions.

The relevant industry facts are: overseas inventory and shipment are the seller's own responsibility rather than platform supply; the data sources are Seller Center, overseas warehouse WMS, logistics tracking and purchase cost sheets; and the consequence of error is that goods cannot be sold, shipments run late, or platform penalties follow. These facts shape what an audit must be able to trace, because the fulfillment order, the inventory sheet and the price declaration must be checked against the same conversation rather than against separate exports.

Constraints at the conversation logs step

The chat interface decides whether FastGPT context is used through chatId. When chatId is not passed, context is built entirely from the supplied messages. When chatId is a non-empty string, the session is taken from the FastGPT database and only the last item in messages is used as the user question; the remaining messages are ignored. The caller must keep chatId unique and shorter than 250 characters.

detail=true returns intermediate values and responseData; detail=false does not return them. responseChatItemId, when passed, becomes the response message ID for that conversation and must be unique under the current chatId. Session source is distinguished with source=api, which returns sessions created through the API and not sessions created on the page. Clearing application sessions clears only sessions created through the API key. Run detail is obtained through getResData by appId, chatId and dataId. These fields determine what can be checked during audit. Business data that is not returned cannot be assumed to have been retained completely.

A relevance score is not a category, entity or permission filter. Retrieval is not authorization. Forms collect values; business verification, approval, database writes and external synchronization require their stated downstream implementation.

Hypothetical drill sample: a semi-managed seller has one fulfillment order dated 10 March 2025. The product is a "folding storage box". The overseas inventory sheet records 120 sellable units for that day. The platform fulfillment order status is "awaiting shipment". The draft sentence to be judged is: "This fulfillment order has deducted inventory and completed shipment." The source material contains only the three records above and does not state a deduction action or a shipment completion time. In judging that draft sentence, the subject is the same fulfillment order, the period is 10 March 2025, and the fields are inventory quantity and fulfillment status. The source does not state deduction or shipment completion, so the sentence is unsupported.

Configuration and implementation checks

The table below is tied to the sample above. It lists only documented settings or implementation checks relevant to this feature and the primary task. The values are hypothetical starting points for this drill, not official defaults.

Three common errors

1. Treating a detail=false response as containing complete run detail. The input is a request with detail=false; the comparison object is whether responseData exists; the observable result is that the field is absent, so module-level intermediate values cannot be checked from it.
2. Merging page sessions with API sessions in a count. The input is a getHistories request with source=api; the comparison object is whether the returned chatId values belong to the API source; the observable result is that page sessions do not appear in that list, and merging them inflates the session count.
3. Treating retrieval similarity as a permission or entity filter. The input is responseData containing similarity; the comparison object is whether the datasetId in quoteList belongs to the target library; the observable result is that a high or low similarity score does not change record ownership, and it cannot be used to decide that a record has been authorized.

How to verify the setup

1. Call getResData with the sample appId, chatId and dataId. Compare the returned moduleName and moduleType against the expected nodes. The result meets the condition when the returned conversation record matches the fulfillment order in the sample. If an empty list is returned, record it as run detail not found.
2. Compare the sample draft sentence against the three source records. Check whether subject, period and field each have an explicit value. The result meets the condition when the source is confirmed not to state deduction or shipment completion and the draft sentence is judged unsupported.
3. Request the same chatId once with detail=true and once with detail=false. Compare whether the responseData field is present. The result meets the condition when the field appears only with detail=true. If neither request returns the field, record it as pending verification and do not assume retention is complete.

The audit scope here is limited to what the conversation interface actually returns. Reconciliation, retention and compliance requirements belong to a separate complete business plan; the existence of a log does not by itself complete a compliance audit.

Related reading

• openapi/chat
• FastGPT conversation logs