Fresh-food weighing and sorting: shared access for after-sales questions

What the documents look like

Fresh-food weighing and sorting produces fulfillment records built around weight and specification. The data sources named in the brief are the WMS, electronic scales, the order system, and quality-inspection photographs. The working documents are sorting task sheets, weighing labels, short-weight compensation forms, and quality-inspection records. Weighing and labelling happen inside the warehouse, and a short shipment can lead to compensation payouts, margin loss, and poor customer reviews. After-sales answering therefore depends on matching an order, its sorting task, and its weighing result.

The shared-access feature hands a conversation entry point to outside users, and the resulting charges fall under the team that owns the application. The no-login window generates a localId for each user and pulls conversation records from the cloud. That mechanism only holds within the same device and the same browser; switching devices or clearing browser cache loses those records. The system permits pulling only the most recent 30 days and 20 records. When a short-weight complaint spans devices, continuity of the conversation record cannot be guaranteed by this mechanism.

Constraints at the shared access step

Identity verification uses a POST root address. Initialization, conversation start, and conversation finish each send a request to a specific endpoint under that address. The server interface only needs to return whether verification succeeded. A successful response contains success and a uid inside the data object. The uid is the user's unique credential and must be a string that excludes the "|", "/", and "\" characters and does not exceed 255 bytes in length; otherwise the system returns an Invalid UID error. The authToken travels in the share link query, and FastGPT passes token=[authToken] in the body of the authentication request. This feature is available only in the commercial edition, and parameters such as expiry time, QPM, and points cap are configurable only in the commercial edition.

Retrieval results serve answering and do not carry authorization. Similarity, relevance scores, and quoted fragments do not constitute identity, entity, or permission filtering. The requirement that a retrieval library use the same vector model cannot be extended to external systems. Pulling conversation records depends on the uid, and cross-device records need separate identity and session management.

Hypothetical paper exercise sample: a sorting task sheet excerpt from a front warehouse dated 12 March 2025 reads "Order A-2031, specification 500g±20g, actual weighed 438g, sorter Li, quality-inspection status pending review"; a short-weight compensation form excerpt from the same day reads "Order A-2031, difference 62g, compensation application submitted, not approved." A proposed reply sentence reads "Order A-2031 short weight has been verified, compensation has been approved, the customer can be told it is handled." The materials needed for judgment are the sorting task sheet, the short-weight compensation form, and the approval record, all of which must be obtained within an accessible target retrieval scope or an explicit manual input.

Configuration and implementation checks

The starting values are hypothetical settings for this exercise, used for comparison against measured values, and are not official defaults. The result-reporting endpoint has no specified return value and can handle logic on its own; this does not mean a charge or a write has occurred.

Three common errors

1. Treating retrieval relevance as authorization filtering. The input is a retrieval request containing a short-weight compensation form; the comparison is whether the scope is limited by uid and business permission; the observable result is that an unauthorized record is returned on similarity alone. Permission judgment must be implemented separately.
2. Treating pending approval as approved. The input is the compensation form above reading "compensation application submitted, not approved"; the comparison is whether the reply claims compensation has been approved; the observable result is that the proposed sentence contradicts the material. The reply must state it is pending verification.
3. Treating the same-device record boundary as cross-device synchronization. The input is pulling conversation records after switching devices; the comparison is whether history can still be obtained; the observable result is that the records are lost. Identity and session management must be designed separately.

How to verify the setup

1. Input the sorting task sheet and short-weight compensation form text above together with the proposed reply sentence; compare whether the proposed sentence claims compensation has been approved. Only when the material explicitly records approval and the same order and same field agree does the answering condition hold; otherwise record it as unsupported.
2. Input the share link carrying authToken and the authentication endpoint response; compare whether success is true and whether the uid format excludes the "|", "/", and "\" characters and does not exceed 255 bytes. When Invalid UID is returned or success is not true, judge it as not passing.
3. Input the hypothetical expiry time, QPM, and points cap settings from this exercise; compare the link's actual usable state and usage data. When the link is expired or unusable, or usage exceeds the set value, record it as non-conforming; parts not actually measured remain pending verification.

Retrieval checks describe only observations and failure branches and do not guarantee a first hit or that an unrelated query returns empty. An empty result can be checked against an explicit paper input of [] for downstream design; this does not claim that a fictional rule retrieval necessarily returns []. Different vector models cannot be jointly associated; check identifiers first and exclude non-conforming ones.

Related reading

• guide/build/publish/link
• FastGPT shared access