What the documents look like
Seller ratings and experience score maintenance works on consumer feedback that arrives after fulfilment and after-sales handling. It does not directly set promotion prices. The daily material set for this category includes negative-review handling records, experience score detail statements, service diagnostic reports and rectification ledgers. Sources named in the brief are Douyin Store experience score, Tmall service indicators, JD after-sales reviews and the customer service system. Update cadence is daily and weekly.
For the document comparison task, chunking has to cope with the same store being reported under different platform conventions and different date ranges. Material commonly contains order numbers, handling status values, indicator names and time ranges. When a chunking step separates a conditional clause from the conclusion it constrains, negative-review attribution loses its premise, and the rectification ledger stops matching the diagnostic report.
Constraints at the document chunking step
Chunking must preserve a document identity and version that can be traced back to the source text. Segmentation follows the structure of the material, so conditions, explanations and the content they constrain stay together. Original data and retrieval indexes are distinct; one group of original data can map to multiple indexes. Sample retrieval can use terminology, identifiers and natural-language questions separately. After material changes, the actual training and index state must be checked; a successful upload does not mean retrieval has already updated. Semantic recall and relevance scoring affect ordering only and do not perform permission or entity isolation. Libraries built on different vector models cannot be associated together; identifiers are checked and non-matching items are excluded.
Hypothetical drill sample: the material text reads, "The experience score detail statement for Store A covers 1 March 2025 to 31 March 2025. It shows that on 12 March one order's review handling status was 'awaiting supporting documents'. On 15 March the customer service supervisor submitted a rectification ledger recording that this negative review was attributed to logistics timeliness." The draft sentence to be assessed is, "This negative review for Store A has completed rectification and been closed." The sentence lacks a closure time and a closure operation record, so it is unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this trial | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid search | Material mixes natural language and order numbers; compare semantic and full-text recall paths |
| Reference cap | 3000 tokens | Hypothetical starting point for this trial; compare against other values and adjust by material length |
| Minimum relevance | 0.4 | Hypothetical starting point for this trial; compare filtered counts at 0.3 and 0.5 |
| Question optimization | Enabled | Many follow-up turns; compare recalled content with it on and off |
| Result reranking | Enabled | Many candidates; compare ordering changes from rerank scores |
| Segmentation basis | By material headings and paragraphs | Compare with whole-document ingestion and check conditional-clause integrity |
These are implementation checks and hypothetical trial starting points, not documented defaults. The documented engine facts are that FastGPT stores original vector data in the MongoDB dataset.datas table with an indexes field holding vector IDs, that PostgresSQL with the PG Vector plugin and an HNSW index handles vector retrieval, and that RRF merges multiple recall paths while ReRank produces a 0-1 score.
Three common errors
- Treating "awaiting supporting documents" as completed rectification. Input is the material text above; compare the handling status field; observe whether a closure time and a closure operation record appear. When they are absent, the item can only be recorded as pending verification.
- Treating a successful upload as an updated index. Input is the updated rectification ledger file; compare the training state against the actual retrieval result; observe whether retrieval returns the new content. A miss is recorded as pending verification.
- Treating a semantic recall score as a permission filter. Input is a cross-store query; compare the store identifier on returned content; observe whether records from a non-target store appear. Relevance does not exclude unauthorized records.
How to verify the setup
- Input the hypothetical sample text and the draft sentence; compare whether the draft sentence contains a closure time and an operation record; observe whether the draft sentence is marked unsupported.
- Input an order number and a natural-language question separately; compare whether both recall paths return the same material paragraph; observe whether the chunk retains the conditional clause.
- Input the updated rectification ledger; compare the training state against the actual retrieval result; observe whether the new content can be retrieved. A miss is recorded as pending verification.
A retrieval check states what to observe and the failure branch. It does not guarantee a first-position hit, full recall, or an empty result for an unrelated query. A paper input of an empty array can be used to check downstream design, without claiming that a fictional rule returns an empty array. Where a document field is not provided, the value is unknown. A pending request is not approval, an offered option is not execution, and an unsigned termination is not a completed termination.