What the documents look like
Pinduoduo low-price campaign operations revolve around campaign resource slots, campaign inventory, and refund-rate fluctuation. The daily and per-campaign documents are campaign registration forms, product cost calculation sheets, after-sales refund records, and platform in-site messages. In cross-border material and multilingual retrieval, the same batch of material may exist as a Chinese original, a translated version, and keyword indexes in different languages. Retrieval has to distinguish whether a hit is the original, a translation, or an image description.
The update frequency is daily and per campaign. That frequency affects the pace of material version checking. It does not justify any batch synchronization or real-time trigger mechanism.
Within this category, knowledge base retrieval and recall converts those documents into reference content that a question can retrieve, so that operations handover can check material status.
Constraints at the retrieval step
A knowledge base consists of libraries, collections, and data. The smallest search unit is the library. Collections only classify and manage data and are unrelated to search results. A dataset search node can associate one or more knowledge bases that use the same vector model. Libraries using different vector models cannot be associated together. Check the vector model identifier first and exclude those that do not match, then record the actual observation. Do not run a cross-model retrieval to compare missed recalls.
One retrieval combines question optimization, semantic retrieval, full-text retrieval or hybrid retrieval, image handling, RRF fusion, and ReRank secondary sorting according to the input content and search parameters, then applies similarity filtering and reference limit trimming. The flow diagram lists optional paths. Image retrieval requires an image input and model capability. ReRank requires a model and must be enabled in settings. This does not mean every request executes all paths.
Semantic retrieval depends on vector distance. Full-text retrieval depends on keyword hits. Hybrid retrieval fuses the two paths through RRF. RRF is a fusion process, not a switch. The minimum relevance score takes effect only in semantic retrieval or when result re-ranking is used. It is a filtering threshold, not the final sorting rule, and it does not perform permission or entity isolation. Retrieval itself does not constitute authorization.
The reference limit is measured in tokens and controls the maximum amount of content referenced per search. Question optimization adds one model call before formal retrieval for coreference resolution and question expansion, and it increases overall latency.
Configuration and implementation checks
The table below lists settings or implementation checks already described in the official documents, tied to this page's documents and primary task. The values are hypothetical starting points for this paper exercise, used only to explain the reason for choosing them and the comparison object. They are not official defaults.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Associated knowledge bases | One Chinese library and one English library; check the vector model identifier first | Association is allowed only when identifiers match; exclude those that do not match |
| Search mode | Semantic retrieval first, then compare with hybrid retrieval results | Compare the retrieved content difference for the same question under the two modes |
| Reference limit | Tentatively 3000 tokens | Compare with the longest campaign registration form fragment to observe whether truncation occurs |
| Minimum relevance score | Tentatively 0.4 | Label relevant and irrelevant samples first, then compare results on both sides of the threshold |
| Question optimization | Off first, then on for comparison | Compare the original question with the optimized question and check whether the subject was changed incorrectly |
| Result re-ranking | Not enabled first, then enabled for comparison | Compare candidate order before and after re-ranking and observe relevance changes |
Three common errors
- Treating a retrieval hit as an authorization state. Hypothetical exercise sample: a campaign registration form original states "This form is submitted to the platform recruitment entry; the review result is subject to the in-site message," while a draft sentence states "Registration has passed review." Retrieving that form and hitting it only means the material was recalled. The review result still awaits the in-site message, and the draft sentence is not supported.
- Forcing libraries with different vector models to be associated together. Hypothetical exercise sample: the Chinese library is identified as model A and the English library as model B, and a check request states "retrieve both libraries together." The correct action is to check the identifiers, exclude those that do not match, and record the observation. Do not run a cross-model retrieval to compare missed recalls.
- Mixing the reference limit with the thinking budget. Hypothetical exercise sample: one configuration places the reference limit and the model thinking budget in the same field and fills the same value. The reference limit is measured in tokens and trims reference content. The thinking budget is a model-side parameter. The two cannot substitute for each other.
How to verify the setup
The verification below uses the hypothetical exercise sample in this section. All of it is paper checking and was not actually executed.
Hypothetical exercise sample: the material originals are "This form is submitted to the platform recruitment entry; the review result is subject to the in-site message" and "The campaign price follows column B of the cost calculation sheet, valid until June 30." The draft sentence to be judged is "Registration has passed review, and the campaign price is valid long term."
- Input: the two original sentences above and the draft sentence, with the retrieval scope being one Chinese library. Comparison: "has passed review" in the draft sentence against the original "the review result is subject to the in-site message." Condition to satisfy: the original does not state a review conclusion, so the draft sentence is judged unsupported and the evidence gap is recorded.
- Input: the same originals and draft sentence. Comparison: "valid long term" against the original "valid until June 30." Condition to satisfy: the same field has mutually exclusive values, so the draft sentence contradicts the original and is judged conflicting.
- Input: the vector model identifiers of the two knowledge bases to be associated and one association request. Comparison: whether the two identifiers match. Condition to satisfy: association is allowed only when identifiers match; when they do not match, exclude those that do not match and record the actual observation without inferring runtime results. An empty-result handoff can use the paper input
[]to check the downstream design. Do not claim that a fictional rule retrieval necessarily returns[].