What the documents look like
Seller ratings and experience-score maintenance handles consumer feedback that arrives after fulfillment. The working materials are negative-review handling records, experience-score detail statements, service diagnostic reports, and remediation ledgers. These materials update daily or weekly. In cross-border operations, one batch of materials commonly contains fields in more than one language, and retrieval must cover both Chinese-language questions and foreign-language source text.
Document parsing and chunking sit at the front of that retrieval path. A negative-review record typically carries an order identifier, a consumer complaint, a contact action, a status such as awaiting consumer reply, and a date range. An experience-score detail statement carries metric values over a stated period. A service diagnostic report carries findings and their scope. A remediation ledger carries tracked items and their current stage. When chunking separates a condition clause from the value it constrains, or separates a status from the order it belongs to, semantic retrieval returns fragments that lack the basis for a judgment. A customer service supervisor reading such a fragment can reply in a way that departs from the actual detail statement.
Two features of this material shape the chunking step. First, the same source data can map to multiple vectors: the indexes field records vector IDs, and one data item can correspond to several vectors. Second, retrieval mode matters by question type. Full-text retrieval suits identifiers, model numbers, and proper nouns. Semantic retrieval suits natural-language and cross-language questions. Hybrid retrieval merges both through RRF and is usually followed by ReRank secondary ordering, which produces a 0–1 score. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used; it is a filtering threshold and does not perform permission or entity isolation. A retrieval library and a vector model must correspond; libraries built on different vector models cannot be associated together.
Constraints at the document chunking step
Chunk boundaries must preserve semantic singleness and structural completeness so each data group can point back to its source text. A chunk that keeps the subject, the period, and the status together supports a defensible reading. A chunk that splits them does not.
The following hypothetical paper sample is used throughout this page for verification. It is an invented test case, not a real record.
Hypothetical sample source text: "March 1, 2025 to March 31, 2025, Store A negative-review handling record item 12: order number H-2031, consumer feedback reports logistics delay, customer service contacted the consumer on March 5 to request supplementary evidence, status is awaiting consumer reply."
Hypothetical draft sentence to be assessed: "This negative review has completed remediation and been closed."
The source text states only that contact was made to request supplementary evidence and that the status is awaiting consumer reply. It does not state that remediation was completed or that the item was closed. The draft sentence is unsupported by the source text.
A second boundary condition concerns index state. A successful upload is not evidence that retrieval has been updated. Actual training and index status are checked separately, and when evidence is missing it is recorded as a gap. The indexes field and training state are the objects of that check.
Configuration and implementation checks
| Configuration item | Relevant value or check for this page | Explanation |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, hybrid retrieval | Chosen by question form; identifier-type questions use full-text or hybrid |
| Minimum relevance | Hypothetical trial starting value 0.4 | Compared against 0.3 and 0.5 to observe hit range; not an official default |
| Reference limit | Hypothetical trial starting value 2000 tokens | Compared against 1500 and 3000 to observe reference completeness |
| Question optimization | Enabled or not according to multi-turn follow-up scenarios | Adds one model call; improves retrieval across consecutive dialogue |
| Result reranking | Enabled when a model is available and the setting is on | Rerank score is 0–1 and can be used for filtering |
| Index check | Check indexes field and training status | A successful upload does not mean retrieval is already updated |
The two numeric starting values above are hypothetical trial starting points for this page's sample only. The 0.4 value was chosen to sit between 0.3 and 0.5 so that the hit range can be compared across the three. The 2000-token value was chosen to sit between 1500 and 3000 so that reference completeness can be compared. Neither is an official default, and neither has been tested.
Question optimization performs coreference resolution and question expansion before retrieval. It adds a model call and typically increases retrieval effectiveness in consecutive dialogue while adding latency. RRF is a fusion process applied to hybrid retrieval results, not a switch. Image retrieval requires image input and a model capable of handling it; reranking requires a rerank model and its setting. These optional paths are not executed on every request.
Three common errors
- Treating "awaiting consumer reply" as completed remediation. Input: the hypothetical sample source text and the draft sentence above. Comparison: whether the status field in the source text matches the status asserted in the draft sentence. The source text contains no remediation-completion or closure record, so the draft sentence is unsupported. Observable result: the draft sentence is recorded as unsupported, and the gap remains open.
- Using minimum relevance to exclude unrelated business records. Input: the minimum relevance setting. Comparison: the documented definition of that value. The value filters low-relevance results only and does not perform permission or entity isolation. Observable result: a setting change that is expected to exclude unauthorized records produces no such exclusion, and the expectation is recorded as incorrect.
- Treating a successful upload as an updated index. Input: the upload action. Comparison: actual training status and indexes field state. Observable result: where training or index state cannot be confirmed, the condition is recorded as a gap rather than as completed.
How to verify the setup
- Input the hypothetical sample source text and the hypothetical draft sentence. Compare the status field in each. The condition is satisfied only when the source text contains wording that remediation was completed or that the item was closed. Otherwise the draft sentence is recorded as unsupported. This is a paper assessment; no test has been run.
- Input order number H-2031 as a full-text retrieval query. Compare whether the returned fragment contains that identifier together with the corresponding period, March 1 to March 31, 2025. A hit that includes the identifier and a matching period satisfies the condition. A miss is recorded as pending verification.
- Input the Chinese-language question "March negative-review handling status" (rendered here in English for readers). Compare whether the returned fragment points back to the hypothetical sample source text. A fragment that contains the subject, the period, and the status satisfies the condition. A fragment missing any of the three is recorded as a gap.
Across all three checks, the retrieval library and vector model identifiers are examined first, and items that do not correspond are excluded. Cross-model association is not performed for comparison. Semantic recall and relevance scores do not carry permission or entity isolation, and retrieval is not authorization. Where a check produces no result, the empty outcome is recorded against the downstream design rather than treated as a rule that an empty result must occur. Materials needed for verification must sit within an accessible target retrieval scope or a clearly defined manual input; when they are missing, the gap is recorded as missing.