What the documents look like
Douyin storefront operations does not depend on the rhythm of a single livestream. Daily materials concentrate on product qualification pages, experience score details, shipping violation appeals, and store announcements. Product qualification pages and experience score details are produced daily or per order, and store announcements appear as rules are adjusted. Product material and selection Q&A answers which qualification belongs to a given product and which assessment item a given experience score record belongs to; the answer depends on the subject, object, and valid scope stated in the source text.
These materials share two traits: many versions and dense fields. A qualification page carries a qualification name, a number, a validity period, and the applicable product. Experience score details carry an assessment item, a score change, and a statistical period. Appeal materials carry an order number, a violation type, and a handling status. Parsing and chunking need to preserve the binding between these fields and their context so that a retrieved fragment can point back to its position in the source.
Constraints at the document chunking step
A FastGPT knowledge base is composed of library, collection, and data. The smallest search unit is the library. Vector source data is stored in the MongoDB dataset.datas table, where the indexes field records the corresponding vector IDs as an array; one group of data can correspond to multiple vectors. The vectors themselves are stored in the PostgresSQL vector field, with an HNSW index. Retrieval first recalls vectors, then uses the vector IDs to fetch the source data from MongoDB. When the same group of source data is hit through multiple routes, the results are merged and the vector score takes the highest value.
Multi-vector mapping reduces the tension between content length and semantic richness. A long qualification description can be split into multiple indexes, and recall of any one index brings out the whole group of data. When chunking, conditions, explanations, and the objects they constrain should stay within the same data group, so that a validity period and an applicable product are not separated. Semantic retrieval relies on vector distance, while full-text retrieval relies on keyword hits; numbers, models, and proper nouns suit full-text or hybrid retrieval. RRF is a result fusion process after hybrid retrieval, not a standalone switch. The minimum relevance score takes effect only under semantic retrieval or when result reranking is used; it is a filtering threshold and does not perform permission or entity isolation.
Hypothetical paper exercise sample: the source text of a product qualification page reads, "Subject: Example Merchant; Object: a certain insulated cup; Qualification name: food contact material test report; Number: JC-2024-0187; Valid until December 31, 2024; Applicable product: all specifications of this insulated cup." A draft sentence to be judged reads, "The test report for this insulated cup remains valid in March 2025." The source text only states a validity end of December 31, 2024, so the draft sentence exceeds the valid scope and is unsupported. The retrieval step can only check whether the source text was correctly chunked and recalled; whether to delist or update the product card on that basis is a separate product management action that needs its own confirmation.
Configuration and implementation checks
| Configuration or check item | Relevant setting on this page | Implementation check method |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, or hybrid retrieval | Run a full-text query with a qualification number and a semantic query with a natural-language question; compare the two hit sets |
| Reference limit | Maximum tokens referenced per search | For this hypothetical exercise, set a starting value equal to the length needed to cover one full qualification page, and compare it with the length of a single chunk after splitting |
| Minimum relevance | A 0-1 value effective only under semantic retrieval or result reranking | Observe whether recalled items increase or decrease as the threshold changes; do not treat it as a permission filter |
| Result reranking | Requires a ReRank model enabled per settings | Confirm whether reranking is enabled; compare the rerank score with the vector score |
| Question optimization | Adds one model call before retrieval | In multi-turn follow-up scenarios observe the coreference resolution result; in short-question scenarios compare elapsed time |
| Index status | The indexes field of dataset.datas | After a material change, check the actual training and index status; a successful upload does not mean retrieval has been updated |
Three common errors
- Treating a successful upload as an updated index. After a material change, the actual training and index status must be checked. The
indexesfield ofdataset.datasreflects the vector IDs actually generated; while training is incomplete, retrieval results still point to the old content. - Treating the minimum relevance score as a permission or entity filter. That value takes effect only under semantic retrieval or result reranking and is used to filter low-relevance results; it does not distinguish merchant, product, or record ownership.
- Reading RRF fusion after hybrid retrieval as a standalone switch that can be turned on. RRF is a merge method that combines the retrieval effects of multiple channels; hybrid retrieval itself is selected through the search mode.
How to verify the setup
- Enter the hypothetical source text "Qualification name: food contact material test report; Number: JC-2024-0187; Valid until December 31, 2024; Applicable product: all specifications of this insulated cup." Run a full-text query with the number JC-2024-0187 and compare whether the returned fragment contains all three items: the number, the validity period, and the applicable product. If all three appear in the same chunk and can point back to the source, the condition is met; if only the number is hit and the validity period is missing, record it as incomplete chunking.
- Enter the draft sentence "The test report for this insulated cup remains valid in March 2025" and compare it with the source validity period. The source valid range ends on December 31, 2024, so the draft sentence exceeds that range and is judged unsupported. The retrieval result itself does not change that judgment; the evidence gap must be preserved.
- Use a paper input of an empty reference list to check the downstream design. With an explicit empty reference list as input, check whether the downstream Q&A handles the no-reference case as designed. Do not assume that a fictional rule must return an empty result; record only the return state actually observed.
This page addresses only Douyin storefront operations / product material and selection Q&A / document parsing and chunking. During operational handover, which materials, operations, or requests sit at which authorization state cannot be treated as authorized execution merely because retrieval or a form was involved.