What the documents look like
Short-video affiliate sales rely on long-term content exposure and product-link conversion rather than live explanation. Inventory procurement and supplier materials therefore arrive as short-video scripts, product lists attached to videos, commission plans and violation feedback, updated daily or per order. When parsing misaligns, promotion can fail, commission can be wasted and videos can be taken down.
Three characteristics shape document parsing and chunking for this task. First, one set of supplier data can map to several vector indexes, so a single original record may be recalled through multiple paths; chunking must preserve the document identity and version that point back to the source. Second, the smallest search unit is the library, while a collection only classifies data and does not affect search results. Third, full-text search suits numbers, models and proper nouns, semantic search suits natural language, and hybrid search merges results through RRF before ReRank performs a second ordering. Procurement materials carry many model numbers and supplier identifiers, so the exact-match path must be preserved alongside the semantic path.
Constraints at the document chunking step
Chunking must follow the structure of each specific material and avoid separating a condition, an explanation and the content it constrains. Supplier quotations, stock quantities and authorization status often appear in one continuous clause; once split, a single chunk cannot determine the subject, the object and the effective scope.
Original data and search indexes must be distinguished. In MongoDB, the dataset.datas table stores vector original data, and an indexes field records the corresponding vector IDs, so one data group can correspond to multiple vectors. In PostgresSQL, a vector field stores vectors only; retrieval recalls vectors first, then looks up original data in MongoDB, merges records belonging to the same data group and keeps the highest score. Chunk quality therefore determines whether recalled fragments can be reassembled into complete meaning.
After a material changes, the actual training and index state must be checked; a successful upload does not mean retrieval has updated. Semantic recall and relevance scores do not handle permission or entity isolation, and retrieval is not authorization. Libraries built on different vector models cannot be associated together; identifiers must be checked first and non-matching libraries excluded, without running cross-model retrieval to compare missed recalls.
Hypothetical exercise sample
Material text for checking (hypothetical exercise sample, not a real record):
"Supplier A submitted the Spring Inventory Procurement Quotation on 1 March 2024, with a quotation validity period ending 31 March 2024. The quotation states that product A attached to short videos can supply 500 units, and that the authorization scope is limited to short-video affiliate distribution on this platform, excluding live-stream scenarios."
A draft sentence to be assessed: "Supplier A has confirmed long-term supply to this platform at this quotation."
Assessment point: the source text states only that the quotation validity period ends 31 March 2024 and does not state a long-term supply commitment; a quotation validity period does not indicate a supply term. The draft sentence is unsupported for the same subject, the same period and the same field, and must be marked as pending verification rather than confirmed.
Configuration and implementation checks
| Configuration item | Related setting or check on this page | Explanation and hypothetical starting point |
|---|---|---|
| Search mode | Semantic search, full-text search or hybrid search | Questions about model numbers and supplier identifiers prefer full-text or hybrid search |
| Reference limit | Maximum tokens referenced per search | Hypothetical trial starting point of 3000 tokens, chosen because chunk lengths differ greatly in a mixed library; compare with 1500 tokens to observe reference completeness |
| Minimum relevance | A 0-1 value effective only in semantic search or with result reranking | Hypothetical trial starting point of 0.4; compare with 0.6 to observe the proportion of irrelevant fragments |
| Question optimization | Coreference removal and question expansion before retrieval | Can be enabled for short questions and multi-turn follow-ups; adds a model call and latency |
| Result reranking | ReRank performs a second ordering of candidates, score 0-1 | Requires a reranking model enabled by configuration; not part of every request |
| Post-update review | Check the actual training and index state | A successful upload does not mean retrieval has updated; manual checking is required |
Three common errors
- Treating a quotation validity period as a supply term. The input is the sample source text above and the draft sentence. Compare whether the same field, supply term, is stated. The source text gives only a quotation validity period ending 31 March 2024, while the draft sentence says "long-term supply". The two are unsupported for the same subject, the same period and the same field, so the correct result is to mark the draft as pending verification.
- Treating a retrieval hit as authorization confirmation. The input is the supplier A quotation text and a handover request stating "authorized to list". Compare the stated authorization scope, limited to short-video affiliate distribution on this platform and excluding live-stream scenarios. A request involving a live-stream scenario falls outside the stated scope, so the correct result is to record the authorization boundary gap and not execute the listing.
- Treating a successful upload as an updated index. The input is an upload operation record after a material change. Compare the actual training and index state. If the training completion state has not been checked, the correct result is to keep the status as pending verification and not treat retrieval as updated.
How to verify the setup
- Check one: input the hypothetical sample source text and the draft sentence "Supplier A has confirmed long-term supply to this platform at this quotation". Compare whether the source text states a long-term supply field. The source text gives only a quotation validity period and no long-term supply statement. The condition is met when the draft sentence is judged unsupported and marked pending verification.
- Check two: input the hypothetical sample source text and a short-video request involving a live-stream scenario. Compare whether the stated authorization scope covers live-stream scenarios. The source text explicitly excludes live-stream scenarios. The condition is met when the out-of-scope request is recorded, the gap is retained and the request is not executed.
- Check three: input a paper-based empty result
[]and a downstream reference design. Compare whether the empty result is handled as designed, without claiming that a fictional-rule search necessarily returns[]. The condition is met when the downstream explicitly handles an empty reference and records the actual observation. Image retrieval requires a corresponding image input and model capability, and reranking requires an available model enabled by configuration; neither is part of every request.