What the documents look like
Pinduoduo low-price campaign operations revolve around activity resource slots, campaign inventory, and refund-rate fluctuation. The daily material set for this category includes campaign registration forms, product cost calculation sheets, after-sales refund orders, and platform internal messages. In cross-border material and multilingual retrieval work, the same batch of material may exist in a Chinese original and a foreign-language version at the same time. Retrieval requests fall into three input types: terminology, campaign identifiers, and natural-language questions. Material updates follow a daily and per-campaign rhythm.
The practical consequence for document parsing and chunking is that every campaign cycle needs chunks that can point back to the original material and its version. Campaign price, inventory quantity, and refund status are different values of the same fields. If a conditional clause is split away from the value it constrains, a retrieved chunk loses the basis for judgment. A chunk that carries "campaign price 9.9 yuan" without the campaign period it belongs to cannot support a later comparison.
Constraints at the document chunking step
In FastGPT, a knowledge base consists of library, collection, and data. The smallest search unit is the library; collections only classify and manage data and do not affect search results. One set of original data can map to multiple vector indexes. During retrieval, vectors are recalled first, then the vector ID is used to look up the original content in the dataset.datas table in MongoDB. When several vectors correspond to the same original data, the results are merged and the highest score is taken.
Chunking should keep structure and semantics complete. Conditions, explanations, and the content they constrain stay in the same data block. Semantic retrieval has cross-language understanding, so a Chinese question can match a foreign-language knowledge point. Full-text retrieval depends on keyword hits and suits campaign identifiers, error codes, and proper nouns. Hybrid retrieval merges multiple result paths through RRF. The ReRank model produces a 0-1 score used for filtering. Minimum relevance takes effect only under semantic retrieval or result reranking. The citation limit controls content by tokens.
Retrieval does not perform permission or entity isolation. Libraries built on different vector models cannot be associated together. Identifiers must be checked first and non-matching libraries excluded. A relevance score is a filtering threshold, not a category, entity, or permission filter. Retrieval is not authorization.
Configuration and implementation checks
The following items concern this page's sample. They list documented settings or implementation checks. The numeric values are hypothetical starting points for this exercise, used to compare against other values of the same field. They are not official defaults.
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains campaign identifiers and natural-language questions, requiring both semantic and full-text recall |
| Minimum relevance | 0.4 | Used to filter low-relevance results; compare with 0.2 and 0.6 to observe recall range changes |
| Citation limit | 2000 tokens | Controls cited content length; compare with 1000 and 4000 to observe answer completeness |
| Question optimization | Enabled | The sample contains multi-turn references; compare with the disabled state for follow-up retrieval |
| Result reranking | Enabled | Second-pass ordering when candidates are numerous; compare with the disabled state for ordering stability |
| Chunk review | Manual review of original structure | Confirm conditions and values are not split apart; compare with reading parsed output alone |
Three common errors
- Treating multiple vector indexes of one set of original data as multiple independent data sets. The check input is the sample material original text and the parsed data block list. Compare the original data identifier with the records in the
indexesfield. The observable result is whether the same original data appears once and corresponds to multiple vector IDs. - Treating minimum relevance as a permission or entity filter. The check input is a retrieval term containing a campaign identifier and the target library scope. Compare whether returned results are determined only by similarity. The observable result is whether unrelated business records can still be recalled.
- Treating a successful upload as an updated retrieval index. The check input is changed material and a retrieval request. Compare the training and indexing status records. The observable result is whether retrieval still points to the old version content.
How to verify the setup
Hypothetical exercise sample: the material original text reads, "From August 1 to August 7, 2024, product A has a campaign price of 9.9 yuan, campaign inventory of 500 units, and registration status submitted." The draft sentence to be judged reads, "Product A has a campaign price of 9.9 yuan, with a supply period until August 31." The original text records only the campaign period and registration status. It does not record a supply period. The draft sentence is unsupported, and the evidence gap must be recorded. A separate handover request reads, "Please confirm whether product A's registration has been approved." The original text records only that registration status is submitted. Pending review does not prove approval. This request should be marked as pending verification.
- Verification one: input the material original text above and the draft sentence. Compare the campaign price and campaign period in the draft sentence with the same fields in the original text. The observable result is that the draft sentence's values match the original text, while the supply period field has no corresponding record in the original text. The judgment is partially supported and partially unsupported.
- Verification two: input the retrieval terms "product A campaign price" and "campaign inventory 500". Compare the data blocks returned by semantic retrieval and full-text retrieval. The observable result is whether data blocks containing the campaign identifier and the value are recalled, and whether the conditional clause and the value sit in the same data block.
- Verification three: input an empty-result control, using the explicit paper input [] to check the downstream design. The observable result is whether the downstream handling path for no cited content executes as designed. Do not claim that a fictional rule retrieval necessarily returns [].
Retrieval checks record observations and failure branches only. If the first item is not hit, check chunk completeness and the retrieval term subject. If returned content contains unrelated fragments, check the minimum relevance and citation limit settings. For libraries on different vector models, check identifiers first and exclude non-matching ones. Do not run cross-model retrieval to compare missed recalls. The optional paths listed in the overall document flow diagram do not mean every request executes all of them. Image retrieval requires corresponding image input and model capability. Reranking requires a model and must be enabled by setting.