What the documents look like
Brand-run livestream rooms carry their own scripts, inventory and after-sales work, without relying on creator commissions as the main commercial basis. The daily document set for this category is a livestream script, a product explanation card, a shift schedule and a livestream review. For the product information and selection Q&A task, these materials supply product names, specifications, price ranges, usage scenarios and explanation wording that later become retrievable question-and-answer content. Supporting data comes from store operations dashboards, advertising platforms, livestream comments and transaction details, with updates per session or hourly.
Before any model connection work, each material needs its source, version and applicable scope confirmed. An expired explanation card or a product arrangement that has not taken effect can be mistaken for current product information. The materials also carry different roles: a script states what will be said on a given session, a card states what the product supports, and a schedule states who is responsible on that date. These are separate facts and cannot substitute for one another.
Constraints at the model connections step
Model access depends on the AI Proxy service. Both the main application and an Admin service that reuses App/Service configuration must set AIPROXY_API_ENDPOINT and a non-empty AIPROXY_API_TOKEN. Missing either item causes startup validation to fail. Legacy OpenAI environment variables or a single-model custom request address do not replace these two settings. The system needs at least one language model and one index model to operate normally.
Model capabilities are checked against the input types the application actually uses. Multimodal input, tool calling and reasoning configuration are verified separately. Application parameters are set in the application or node configuration; a same-named field on a compatible chat interface cannot be assumed to override orchestration. For retrieval, libraries that share the same vector model identifier can be associated together. Libraries using different vector models cannot be associated, and a library whose identifier does not match should be excluded first and the observation recorded.
Retrieval itself is not authorization. A relevance score is not a category, entity or permission filter, and a similarity setting does not exclude unrelated businesses or unauthorized records. Forms collect values; business verification, approval, database writes and external synchronization each need their stated downstream implementation.
Configuration and implementation checks
Hypothetical exercise sample: a brand livestream team's script for the 10 March 2026 session states that the main product is Model A vacuum flask, capacity 500 millilitres, price 129 yuan, with a cup brush as the gift. The product explanation card states that Model A is suited to commuting and office use, and is not suited to carbonated drinks. The shift schedule lists the host as Person A and the floor controller as Person B for that session. A draft sentence under review reads: "Model A vacuum flask can hold carbonated drinks, priced at 99 yuan." The review compares this sentence against the original wording of the script and the explanation card.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
AIPROXY_API_ENDPOINT | Enter the AI Proxy service root address without a /v1 suffix | Check whether a path suffix has been appended |
AIPROXY_API_TOKEN | Enter a non-empty administrator token matching the server side | Check for emptiness and for mistaken use of an application API key |
| Language model | At least one language model enabled | Compare against the system requirement that one be enabled |
| Index model | At least one index model enabled | Compare against the system requirement that one be enabled |
| Model capability | Check multimodal and tool calling against the input types the application needs | Compare against the input types used by product information Q&A |
| Retrieval library identifier | Check the vector model identifier used by each library taking part in retrieval | Exclude libraries with mismatched identifiers and record the observation |
Three common errors
- Treating a legacy OpenAI environment variable or a single-model custom request address as a substitute for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. The input is the startup configuration, the comparison object is the two required items stated in the documentation, and the observable result is whether startup validation passes. - Associating libraries that use different vector models directly. The input is each library's vector model identifier, the comparison object is whether the identifiers match, and the observable result is whether mismatched libraries are excluded and recorded.
- Treating a retrieval result or a form entry as an authorized action. The input is the retrieval return or the form value, the comparison object is whether a stated downstream implementation exists, and the observable result is whether the authorization state remains pending verification.
How to verify the setup
- For the draft sentence in the hypothetical sample, "Model A vacuum flask can hold carbonated drinks, priced at 99 yuan," input the sentence together with the original script and explanation card wording. Compare the price 129 yuan against 99 yuan, and compare whether the usage scope includes carbonated drinks. If both points disagree, the sentence is recorded as unsupported.
- For the shift schedule in the hypothetical sample, input host Person A, floor controller Person B and the session date 10 March 2026. Compare the responsible parties and date appearing in the draft sentence or handover request against the schedule. If a party or the date does not match, the item is recorded as pending verification.
- For the retrieval configuration, input the list of library identifiers taking part in retrieval and compare the vector model identifier used by each library. If any identifier differs, exclude that library and record the observation. If the input is an empty list, check downstream design against an empty result; do not infer that retrieval necessarily returns empty.