What the documents look like
Group-buy presale collection takes orders first and organizes purchasing and fulfillment afterward. The daily paperwork centers on the presale product table, the collection result, the purchasing plan, and the cut-off notice. Cross-border material and multilingual retrieval work handles multilingual versions of those documents, overseas supplier material, and compliance files. Retrieval output feeds directly into purchasing and scheduling decisions.
Material updates proceed daily or per collection round. This page does not infer batch synchronization, full synchronization, real-time updates, or transaction-triggered mechanisms from that cadence. The retrieval configuration needs to accommodate the pace of document change, and the model connection layer must be checked against what the application actually sends and receives.
Constraints at the model connections step
Model connections depend on AI Proxy. Among the App/Admin shared variables, AIPROXYAPIENDPOINT holds the AI Proxy service root address and does not append /v1. AIPROXYAPITOKEN holds a non-empty administrator token that matches the server-side configuration. Missing either item causes startup validation to fail, and legacy OpenAI environment variables cannot substitute for these two settings.
The system requires at least one language model and one index model to operate normally. A model ID is the value of the model field in the request body and is globally unique. A model name is for display and can be customized. Model capabilities are checked against the input the application needs, and multimodal support and tool calling are verified separately. A same-named field in a chat-compatible interface does not override the orchestration configuration.
Hypothetical paper exercise sample: a platform operator receives presale product material from an overseas supplier. The source text contains three sentences: "This presale batch is frozen shrimp, specification 2 kg per carton, origin Ecuador. The quotation is valid until 30 June 2026. Supplier qualification documents are attached separately." A draft sentence awaits judgment: "The supplier has confirmed supply for this batch and promised delivery before 30 June." The retrieval library uses one index model, the vector store is Milvus, and full-text retrieval uses BM25. The task is to check whether the draft sentence is supported by the source text.
Configuration and implementation checks
The values below are hypothetical starting points for this exercise, not official defaults. Each row states the reason for the starting value and the comparison method.
| Configuration item | Hypothetical starting value | Reason and comparison method |
|---|---|---|
| AIPROXYAPIENDPOINT | http://aiproxy:3000 | Root address without /v1; compare against the documented format requirement |
| AIPROXYAPITOKEN | A non-empty administrator token | Compare against the server-side configuration to confirm a match and non-empty value |
| Index model | One index model enabled | Compare against the requirement of at least one index model |
| Language model | One language model enabled | Compare against the requirement of at least one language model |
| MILVUSLANGUAGEIDENTIFIER | lingua | Compare against documented options to confirm the value is in range |
| Model probe interval | 30 minutes | Compare against the documented 5 to 60 minute integer range |
Model probe scope covers only currently enabled system models. Disabled models are not probed and produce no history. A probe request retries up to three times after the first failure, for a maximum of four attempts in one round. A single success marks the round as valid; only four failed attempts mark the model as abnormal. Health states are normal (green, success with latency at or below 30 seconds), high latency (yellow, success above 30 seconds), and abnormal (red, four consecutive errors).
Three common errors
- Setting AIPROXYAPIENDPOINT to a full request address that includes /v1. Check: read the variable value, compare it against the documented requirement that /v1 is not appended, and flag any occurrence of /v1 as non-conforming.
- Using an application API key in place of AIPROXYAPITOKEN. Check: read the variable value, compare it against the documented requirement of a non-empty administrator token matching the server-side configuration, and flag an empty value or a mismatched token as non-conforming.
- Treating the quotation validity date as a supply deadline. Check: read the draft sentence "promised delivery before 30 June," compare it against the source text "quotation is valid until 30 June 2026," and note that the subject and field differ. The draft sentence is not supported by the source text.
How to verify the setup
- Input the three source sentences and the draft sentence from the hypothetical sample. Compare the draft claim of confirmed supply and promised delivery against the source text, which states only a quotation validity date. The source text contains no supply confirmation and no delivery promise. The draft sentence remains unsupported and pending verification.
- Input the hypothetical configuration value
http://aiproxy:3000. Compare it against the documented requirement that /v1 is not appended. The format condition is met only when the value contains no /v1. A value containing /v1 does not meet the condition. - Input the hypothetical retrieval library identifier and the index model identifier. Compare whether the two identifiers refer to the same model. When the identifiers differ, exclude that library and record the identifier difference. Do not run cross-model retrieval to compare missed recall. An empty result can be checked against an explicit paper input
[]to verify downstream design. This page does not claim that a fictional rule-based retrieval always returns[].
A relevance score is not a category, entity, or permission filter, and retrieval is not authorization. The same-vector-model requirement for a retrieval library does not extend to business registration sites or other external systems. The total flow diagram lists optional paths that do not all execute on every request: image retrieval requires a corresponding image input and model capability, and reranking requires a model enabled by configuration. Libraries using different vector models cannot be jointly associated; check identifiers, exclude non-conforming libraries, and record actual observations.