What the documents look like
A Shopify standalone store lets the merchant configure payment, shipping, and fulfillment services, and the platform does not run a single automatic return process the way TikTok Shop US does. The working materials for this category are order details, shipping rate templates, shipping labels, and refund records. They sit across Shopify Admin, payment gateways, carrier systems, and inventory location data, and they update per order and daily. Language and field formats shift with the channel and the carrier.
Document parsing and chunking turns this material into searchable segments while keeping the document identity and version that let a segment point back to its source. In cross-border work one source file can carry fields in several languages. Semantic retrieval has cross-language understanding, so a Chinese question can match an English knowledge point, but accuracy depends on model training and on how complete the sentences are.
A shipping rate template may state a condition, the service it applies to, and the destination range it covers. If chunking separates the condition from the content it constrains, a retrieved segment can lack the subject, object, or validity range needed to judge it.
Constraints at the document chunking step
Segment completeness takes priority over segment count. A condition, its explanation, and the content it constrains belong in the same segment. One group of source data can map to several indexes, and the source data and the retrieval index are recorded separately. After a material changes, a successful upload does not mean retrieval has updated; the actual training and index state must be checked. Semantic recall and relevance scores do not carry permission or entity isolation, and the search scope is set by the knowledge base itself. Libraries built on different vector models cannot be associated together, so identifiers are checked first and non-matching libraries are excluded.
Hypothetical paper exercise sample: a standalone store archives one order detail whose source text reads, "Order A-1042, order date 2025-03-11, delivery address Germany, payment status paid, carrier DHL, shipping rate template EU-Standard." A second item is a draft sentence awaiting judgment: "The refund record for order A-1042 shows a refund has been issued." The source text carries no refund record, so the draft sentence is unsupported and the evidence gap is recorded.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid search | Order numbers and carrier names are exact queries, while natural-language questions need semantic recall; compare hit differences against semantic search alone |
| Reference limit | 2000 tokens as a hypothetical starting point | The documents give no default value; estimate from sample segment length and compare reference completeness against 1000 and 4000 |
| Minimum relevance | 0.4 as a hypothetical starting point | The documents give no default value; compare remaining segments after filtering against 0.2 and 0.6 |
| Question optimization | Enabled | Completes unclear references in follow-up turns; compare retrieval hits against the disabled state |
| Result reranking | Enabled | Second-pass ordering when candidates are numerous; compare ranking positions against no reranking |
| Chunking check | Manual review | Check whether a condition and the content it constrains stay in one segment, comparing segment by segment against the source |
Three common errors
- Treating a successful upload as updated retrieval. Input is a changed order detail; compare the upload time with the actual training and index state, and observe whether retrieval still returns the old segment.
- Treating semantic recall as permission filtering. Input is a cross-library query; compare the knowledge base identifier each returned segment belongs to, and observe whether content from an unauthorized library is mixed in.
- Treating a draft sentence as a verified fact. Input is "the refund record shows a refund has been issued"; compare whether the source text carries a refund field, and observe that the judgment is unsupported when the source has no such field.
How to verify the setup
- Segment completeness check. Input the sample source text and the chunking result; compare whether the condition, its explanation, and the content it constrains stay in the same segment. A separation requires re-chunking.
- Sample retrieval check. Search with "A-1042", "DHL", and "Germany shipping" separately; compare whether returned segments carry the subject and range needed for judgment, and record a gap when they do not. A first-position hit, full recall, or an empty result for an unrelated query is not guaranteed.
- Draft sentence judgment check. Input the draft sentence and the sample source text; compare the same subject, the same period, and the same field. When the source carries no refund field, the judgment is unsupported.
An empty result can be checked against a paper input of an empty list to confirm the downstream design, without claiming that a fictional rule always returns an empty list. Cross-model libraries are not associated; identifiers are checked and non-matching libraries excluded, without running a cross-model search to compare missed recall. The optional paths in the overall retrieval flow are not all executed on every request: image retrieval needs an image input and matching model capability, and reranking needs a model and must be enabled in settings. A pending review does not show that an operator lacks permission, and signing, reaching an agreement, and a contract taking effect are three separate facts. Where the source does not state an authorization or effective clause, the evidence gap stays open.