What the documents look like
Price and display audits for chain stores take place during ordinary trading periods, so the material records how a published standard continues to be applied after launch. Supervision systems hold visit records, POS price tables show the price a store actually charges, store cameras and visit photographs serve as evidence, and head-office promotion calendars define the price and display requirements in force for the period. Price tag lists, display standards and rectification notices form the comparison and closure material. These records update weekly and monthly, and in cross-border settings a single audit may involve price tags, standard texts and rectification records written in several languages.
Two features of this material shape the model connection step. First, multilingual retrieval needs an index model that produces comparable vector representations for Chinese, English and other languages, and a retrieval store and its vector model must come from the same source; stores built on different vector models cannot be associated together. Second, model access depends on AI Proxy, and both the App and any Admin service reusing App/Service configuration must set AIPROXYAPIENDPOINT and a non-empty AIPROXYAPITOKEN; a missing item causes startup validation to fail. Retrieval results carry recall only and do not decide permissions, and a similarity setting does not exclude unrelated business lines or unauthorized records. Image retrieval requires corresponding image input and model capability, reranking requires a rerank model enabled by configuration, and the optional paths shown in the documented flow diagram do not all execute on every request.
Constraints at the model connections step
The connection step is where a multilingual audit either reaches the right material or returns something the reviewer cannot use. AI Proxy is a required dependency from V4.17.0, and the older OpenAI environment variables or a single custom request address cannot replace AIPROXYAPIENDPOINT and AIPROXYAPITOKEN. The endpoint is the service root address without an appended /v1. The token is an administrator token that must be non-empty and match the server configuration; an application API key is not a substitute. The system requires at least one language model and one index model before it works normally.
Model identifiers, model names and model channels are distinct: the model ID is the value of the model field in a request body and is globally unique, the model name is a display label, and a channel is a provider protocol. A model mapping such as {"gpt-4o-test": "gpt-4o"} sends gpt-4o-test to AI Proxy and gpt-4o upstream. Channel priority runs from 1 to 100, and a disabled channel no longer serves models.
Model status probing applies only to currently enabled system models, retries a failed request up to three further times within one round, and treats a single success as valid. Normal status means success with latency at or below 30 seconds; high latency means success above 30 seconds; abnormal means four consecutive failed requests. Fault and recovery notifications fire on edge changes, with event types model_status_error and model_status_recovered, sent as separate POST requests per model with a five-second timeout. Without a configured webhook address, no push occurs.
Hypothetical paper exercise sample: a regional supervisor submits material for checking. The source text reads: "Store A executed head-office promotion calendar P-2603 in the second week of March 2026; the price tag list shows the store's price tags for the period were replaced; the visit photograph was taken on 11 March 2026." A draft sentence awaits judgement: "Store A has completed rectification closure for this period." The material does not state the issue or reply record of any rectification notice, so the draft sentence's completion claim lacks supporting evidence.
Configuration and implementation checks
The table lists documented settings or implementation checks relevant to this page's documents and primary task. The starting values are hypothetical choices for this exercise, not official defaults.
| Configuration item | Hypothetical starting value for this exercise | Reason for the choice and comparison object |
|---|---|---|
| AIPROXYAPIENDPOINT | AI Proxy service root address, no /v1 appended | The document requires the root address; comparing against a value with an appended path reveals the validation failure |
| AIPROXYAPITOKEN | Non-empty administrator token matching the server | The document requires a non-empty matching value; comparing against an application API key identifies misuse |
| Index model | One index model enabled | The document requires at least one language model and one index model; compare against a disabled state |
| Retrieval store and vector model | One retrieval store per vector model | The document requires same-source association; comparing store identifiers against vector model identifiers excludes mismatches |
| Model probing | Automatic probing enabled with an interval inside 5 to 60 minutes | The document accepts integers in that range; compare against the unenabled empty state |
| File parsing concurrency | Queue concurrency as documented for price tag and standard text processing | The document lists DATASETPARSEMAX_PROCESS and related queue variables; compare against a lowered value for processing speed |
Three common errors
- Treating a similarity score as a permission or entity filter. Input: a retrieval request over Store A's visit photographs from the sample. Compare: whether returned records include other stores or unauthorized material. Observable result: the similarity value does not change record ownership.
- Associating retrieval stores built on different vector models. Input: two retrieval store identifiers created with different vector models. Compare: whether the identifiers match. Observable result: mismatched identifiers are excluded, and no cross-model retrieval is executed to compare missed recall.
- Treating submitted or pending rectification material as completed closure. Input: the rectification notice in the sample that lacks issue and reply records. Compare: the draft sentence "completed rectification closure" against the status recorded in the material. Observable result: the completion claim is unsupported.
How to verify the setup
- Verification one. Input: the sample source text and the draft sentence. Compare: the completion status asserted by the draft sentence against the price tag replacement and photograph date recorded in the source. The source does not state the issue or reply record of a rectification notice, so the completion conclusion is unsupported and the evidence gap must be recorded.
- Verification two. Input: the retrieval request for Store A in the sample and the returned record list. Compare: whether the store identifier and vector model identifier of each returned record match the request store. Records with mismatched identifiers must be excluded. The observable condition is a returned list containing only same-source store records.
- Verification three. Input: a paper retrieval request explicitly defined as returning an empty result, expressed as an empty list. Compare: whether downstream handling accepts an empty list by design. The observable condition is that downstream provides an explicit branch for the empty result; no claim is made that a fictional rule search always returns an empty list.
Model probing fault and recovery notifications follow the documented edge-change trigger, and no push occurs when no webhook is configured. Retrieval and forms collect values; business verification, approval, database writes and external synchronization require their stated downstream implementation.