Peak-sale fulfillment: model connections for multilingual work

What the documents look like

Peak-sale warehouse and fulfillment scheduling centers on short-term peak capacity and the coordination of people, vehicles and sites. The working materials named in the generic brief are the peak-sale forecast table, staff rosters, courier support plans and exception daily reports, updated per campaign and per day. In cross-border work, one set of materials can carry fields in several languages, so model-assisted retrieval and key-point extraction become part of the daily routine. The high update frequency means each retrieval session faces the current version of a document, and the connection configuration has to let the model read that current text reliably.

This page addresses one step only: model connection and configuration inside the cross-border materials and multilingual retrieval task for peak-sale fulfillment. It does not describe inventory synchronization across warehouses, and it does not treat a retrieval result as a business authorization.

Constraints at the model connections step

FastGPT connects to model providers through AI Proxy. From V4.17.0, AI Proxy is a required dependency. Both the App service and an Admin service that reuses the App/Service configuration must set AIPROXY_API_ENDPOINT and a non-empty AIPROXY_API_TOKEN; a missing item causes startup validation to fail. The endpoint value is the AI Proxy service root address, for example http://aiproxy:3000, and it must not carry a /v1 suffix. The token is an administrator token that must match the server configuration and must not be empty. Older OpenAI environment variables or a single-model custom request address cannot replace these two settings.

The system needs at least one language model and one index model to operate. Model configuration covers five types: language, index, rerank, speech synthesis and speech recognition. A model ID is the value of the model field in the request body and is globally unique; a model name is a display label that can be customized. A model can be configured under more than one channel to achieve load balancing. Channel priority ranges from 1 to 100, and a larger value is more likely to be selected. Model status probing applies only to currently enabled system models, retries at most 3 times after a first failure, and judges a model abnormal only when all 4 attempts fail. A relevance score does not carry permission or entity isolation duties, and retrieval itself does not constitute authorization.

A model connection can be verified by a channel test, which outputs each model's test result and request duration. The model status panel reports three health states: normal when the test succeeds and latency is at most 30 seconds, high latency when the test succeeds but latency exceeds 30 seconds, and abnormal when four consecutive requests all fail.

Hypothetical paper exercise sample. A warehouse fulfillment team intends to check retrieval results against one current peak-sale forecast table. The source material contains three sentences: "This campaign's peak-sale forecast table is numbered P-2405, covers three warehouses in East China, is valid from May 1 to May 7, and its owning body is the scheduling group." "Staff roster R-2405 corresponds to P-2405, with 120 temporary workers reporting for duty." "Courier support plan C-2405 states 30 pickup vehicles and covers only May 1 to May 3." The draft sentence to be judged is: "The vehicle support in C-2405 covers the entire peak-sale campaign." This draft sentence and the third source sentence address the same document and the same field, and the source states coverage of May 1 to May 3 only, so the two values are mutually exclusive.

Configuration and implementation checks

The table below ties each row to this page's documents. Every value in the second column is a hypothetical starting point for this paper exercise, chosen for comparison purposes, and none of them is an official default.

Channel priority, model ID uniqueness and the retry count of 3 after a first failure are documented conditions, not values to be chosen here. A pending request is not an approval, and an offered option is not an execution.

Three common errors

1. Writing AIPROXY_API_ENDPOINT as a full model request address with a /v1 suffix. The input is an address carrying /v1; the comparison object is the documented root-address format; the observable result is whether startup validation passes.
2. Using an application API key in place of AIPROXY_API_TOKEN. The input is an application key; the comparison object is the administrator-token requirement; the observable result is whether the value is accepted as a non-empty administrator token.
3. Treating a retrieval relevance score as a permission filter. The input is one retrieval request; the comparison object is the statement that a relevance score does not carry permission or entity isolation duties; the observable result is whether unauthorized records are wrongly excluded or unauthorized records are wrongly included.

How to verify the setup

1. For the draft sentence "The vehicle support in C-2405 covers the entire peak-sale campaign," take that sentence and the third source sentence as input, compare the coverage-date field, and treat the draft as supported only when the two values agree. In this exercise the values are mutually exclusive, so the draft is recorded as conflicting with the source.
2. For the second source sentence "120 temporary workers reported for duty," take that sentence and the draft sentence as input, compare the subject and the period, and record the field as unsupported when the draft does not address it. An unsupported field is not a negation.
3. For the retrieval scope, take the current P-2405 materials and one query that reaches beyond the validity period as input, compare the returned document numbers and validity periods, and observe whether only the current version is returned. A paper input of [] can be used to check the downstream design for an empty result; this does not assert that a retrieval must return an empty list.

Across different vector models, datasets cannot be jointly associated. Check the identifiers first and exclude those that do not match, then record what was actually observed. Image retrieval requires a corresponding image input and model capability, and reranking requires an available model enabled under its settings; the optional paths in the overall flow diagram are not all executed on every request. Where a required document field is absent from the source, the field remains unknown, and the evidence gap is recorded rather than filled by an assumed authorization or effective clause.

Related reading

• self-host/config/env
• self-host/config/model/intro
• FastGPT model connections