What the documents look like
Pinduoduo low-price campaign store operations revolve around campaign resource slots, campaign inventory, and refund rate fluctuation. The working materials named in the brief are campaign registration forms, product cost calculation sheets, after-sales refund orders, and platform site messages. These materials are updated daily or per campaign. The product information and selection question-answering task needs to organize the product name, campaign price, and registered quantity from registration forms, the cost items and gross margin from cost calculation sheets, the refund reason and status from refund orders, and the notice content from site messages into a searchable knowledge base. The vector model and index decide whether these questions can be recalled.
The brief marks this category as roughly accurate, so the material list and update frequency remain conditional. A registration form carries a registration status; a cost sheet carries cost and margin; a refund order carries a refund state; a site message carries a notice. Each of these is a different field with a different evidence role, and none of them substitutes for another.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. One original data block can be associated with multiple indexes, and the index content and the original text content are checked separately. Index content needs to be complete and focused; retrieval coverage and index length are weighed together against samples. Image retrieval depends on whether an image description or image vector index was generated at ingestion time, and cannot be judged from the query side alone. After a model or index change, the same group of questions is compared against cited original text, and vector scores alone do not support a promise about effect.
A relevance score is a filtering threshold, not a category, entity, or permission filter. Similarity settings do not exclude unrelated businesses or unauthorized records. Retrieval is not authorization. Forms collect values; business verification, approval, database writes, and external synchronization need their stated downstream implementation. A pending request is not approval, and an offered option is not execution.
Hypothetical paper exercise sample: one store's campaign registration form reads "Product A, campaign price 9.9 yuan, registered quantity 500 units, registration status is pending review." One cost calculation sheet reads "Product A, cost 7.5 yuan, campaign price 9.9 yuan, gross margin per unit 2.4 yuan." A draft sentence under review reads "Product A has passed campaign review and can accept orders for 500 units at 9.9 yuan." The pending request under review reads "Please confirm whether Product A's campaign inventory can be locked at 500 units."
Configuration and implementation checks
| Configuration item | Setting or check on this page | Relation to the sample |
|---|---|---|
| Associated knowledge base | Select one or more knowledge bases with the same vector model | The registration form and the cost sheet must use the same vector model |
| Search mode | Semantic search, full-text search, or hybrid search | Product name and campaign price suit hybrid search |
| Citation cap | Each search cites at most n tokens of content | The hypothetical trial starting point is 2000, compared against the documented value before adjustment |
| Minimum relevance | A 0-1 value, effective only in semantic search or with result reranking | The hypothetical trial starting point is 0.3, compared against recalled original text |
| Question optimization | Coreference resolution and question expansion before retrieval | Can be enabled for follow-up questions about campaign review status |
| Result reranking | Uses a ReRank model for secondary ordering of candidates | Enabled when candidates are numerous, filtered by rerank score |
The citation cap hypothetical trial starting point of 2000 tokens is chosen because a single registration form or cost sheet entry is short, and the coverage range is confirmed by comparison against the documented value. The minimum relevance hypothetical trial starting point of 0.3 is chosen because it must be compared against recalled original text to confirm that a pending-review status is not filtered out. These are trial starting points for this paper exercise, not official defaults.
The FastGPT workflow knowledge base search node can be added repeatedly, has external input, has static configuration, triggers execution, and is a core module. A knowledge base search node can select one or more knowledge bases with the same vector model. Its cited content output is an array whose length can be 0, so the downstream path runs even when nothing is retrieved. RRF is a fusion process for combining multiple retrieval channels; it is not described as a toggle. Question optimization adds one model call before formal retrieval, which usually improves multi-turn retrieval but also adds latency. Image retrieval paths require a corresponding image input and model capability, and reranking requires a model and must be enabled by configuration; the optional paths in the overall flow do not all run on every request.
Three common errors
- Treating pending review as passed. The sample registration status is "pending review," and the draft sentence says "has passed campaign review." The input is the registration form original text and the draft sentence; the comparison is the registration status field; the observable result is that the two values differ, and the draft sentence is not supported.
- Treating the cost sheet's gross margin as the basis for campaign inventory. The input is the cost sheet original text and the inventory lock request; the comparison is the cost items against the registered quantity; the observable result is that the cost sheet does not state a lockable inventory quantity, so the request lacks evidence.
- Associating knowledge bases across different vector models. The input is the vector model identifiers of the two knowledge bases; the comparison is whether the identifiers are the same; the observable result is that different identifiers exclude joint association, and the actual observation is recorded without inferring a runtime result.
How to verify the setup
- Check the draft sentence against the sample original text. Input the registration form original text "registration status is pending review" together with the draft sentence "has passed campaign review." Compare the values of the same field. Pending review and passed are mutually exclusive values for that field; an inconsistency means the draft sentence is not supported.
- Check the evidence gap against the sample request. Input "Please confirm whether Product A's campaign inventory can be locked at 500 units" together with the cost sheet original text. Compare the fields the request requires against the fields the original text states. The original text does not state a lockable inventory quantity, so this is recorded as pending verification.
- Check the downstream design against an empty result. Input an explicit paper input of an empty array, and check whether the downstream path runs with an array length of 0. This does not claim that a fictional rule retrieval necessarily returns an empty array. A pending review status does not demonstrate a lack of operating authority; signing, reaching cooperation, and contract effectiveness are three different facts; a quote validity period does not indicate a supply period. Missing evidence remains missing.