What the documents look like
Douyin storefront operations centre on product cards and the store experience score. The materials handled day to day include product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements. These materials update daily and with each order. Contract and transaction material comparison has to work across subjects, objects, states, and validity scopes that are spread across separate documents. When a condition and the content it constrains are split apart during chunking, the comparison loses its basis.
Frequent material updates mean every comparison must be anchored to a specific version. A successful upload does not mean the searchable content has been updated. The experience score breakdown and shipping violation appeal materials carry numeric scopes and dates that must stay attached to the order or period they describe. Store announcements carry effective dates and validity windows. Product qualification pages carry subject identity and qualification scope. Splitting any of these from the condition that qualifies them produces fragments that cannot support a same-subject, same-period, same-field comparison.
Constraints at the document chunking step
Chunking must preserve the document identity and version that allow a fragment to be traced back to its source. A condition, its explanation, and the content it constrains should remain in the same chunk. Raw data and retrieval indexes are two layers: one group of raw data can correspond to multiple indexes, and a change to an index does not change the raw data. Retrieval operates on the library as a whole; collections serve only to classify and manage data and do not affect search results.
Semantic retrieval depends on vector distance. Full-text retrieval suits numbers, models, and proper nouns. Hybrid retrieval combines both and merges results through RRF. The minimum relevance score takes effect only under semantic retrieval or result reranking. It is a filtering threshold and does not perform permission or entity isolation. When the same group of raw data is hit by multiple retrieval paths, the results are merged and the highest score is taken.
A hypothetical paper exercise sample: the source material reads, "Store announcement: from 1 March 2025 this store applies a 48-hour shipping time limit to order A-2031, and the appeal window for this order closes on 20 March 2025." The draft sentence to be assessed reads, "The shipping time limit for order A-2031 is 48 hours, and the appeal window is closed." The comparison uses the same subject, the same period, and the same field. The shipping time limit field matches. The appeal window field in the draft states it is closed, while the source gives only a closing date and does not state whether it is currently closed. That part is unsupported.
Configuration and implementation checks
The table below lists only the check items relevant to document parsing and chunking on this page. The values are hypothetical starting points for this exercise, used for comparison against the source material.
| Configuration or check item | Hypothetical starting point | Reason for selection and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | Materials contain both numbers and natural language, so semantic and full-text hits need to be observed together |
| Reference limit | Hypothetical starting point 2000 tokens | Compared against material paragraph length to observe whether conditional sentences are truncated |
| Minimum relevance | Hypothetical starting point 0.3 | Compared against semantic retrieval or rerank scores to observe whether low-scoring fragments enter |
| Question optimisation | Hypothetically enabled | Compared against multi-turn follow-up scenarios to observe whether references are completed |
| Chunking check | Section-by-section against material structure | Compared against source conditions, explanations, and constraint scope |
| Training and index status | Reviewed after upload | Compared against actual retrieval returns to confirm whether the index has updated |
Three common errors
- Treating a successful upload as an updated index. The input is a newly uploaded announcement; the comparison object is the retrieval return. If the return still shows the old paragraph, the index has not updated and training and index status need review.
- Treating minimum relevance as a permission filter. The input is an unrelated business query; the comparison object is the returned records. If the return contains unauthorised material, the setting does not perform isolation and a separate permission implementation is required.
- Treating a closing date as already closed. The input is the draft sentence "the appeal window is closed"; the comparison object is the source text "closes on 20 March 2025". The source does not state the current status, so the judgement is unsupported.
How to verify the setup
- Input the hypothetical source text and the draft sentence. Compare the shipping time limit field; if the two match, the condition is met. Compare the appeal window field; the source gives only a closing date and the draft states it is closed, so the condition is not met. Record the evidence gap.
- Input the number "A-2031" through full-text retrieval and a natural-language question through semantic retrieval. Observe whether the returned paragraph contains the subject, object, state, and validity scope. If a conditional sentence is split away, adjust the chunking and recheck.
- Input an explicit paper empty result [] and check whether the downstream design handles it as an empty reference. Do not claim that a fictional rule retrieval necessarily returns []. After material changes, check the actual training and index status and record the observed return content. Do not treat a successful upload as an updated index.