What the documents look like
For multilingual work in Shopify fulfillment, this page examines FastGPT vector indexes. Bilingual entries require filter, main-unit and housing subjects checked, preserving the distinction between an initial draft and the order. The examples are hypothetical.
Constraints at the vector indexes step
A proposed Shopify bilingual-index exercise pairs Chinese order wording, rendered here as one replacement filter only, excluding the filtration main unit, with an initial English draft saying replacement cartridge only; housing not included. Cartridge and filter need an applicable term relationship, while housing needs its component subject checked. Fluency cannot establish that translation review occurred. Search entries may connect to the same original data, but the order and initial draft retain different source identities. Negative wording in both does not establish that the excluded entities are equivalent, and no confirmed term mapping is supplied.
Configuration and implementation checks
| Bilingual identity check | Wording and source in this exercise | Evidence action |
|---|---|---|
| Order subject | One replacement filter; main unit excluded | Retain single-item and exclusion wording. |
| Draft terms | Cartridge and housing | Inspect term mapping and component subjects. |
| Added index content | Complete unit with replacement filter | Compare whether a main unit was invented. |
| Version mapping | Initial and reviewed drafts have different roles | Check targeted original and actual citation. |
| Same-question retest | Same purchase-contents question in both languages | Retain excluded subject and separately check purpose questions. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Embedding model | Knowledge bases associated with one search node must use the same embedding model. |
Original data and indexes | One original data block can have multiple indexes. |
Index content | Index wording should be complete and focused. |
Image indexes | Where image retrieval is used, inspect descriptions or image vectors. |
Version and result check | After a change, reuse questions to compare originals. |
Three common errors
The first error writes complete unit with replacement filter in an English entry, adding a main unit absent from the order. Compare the entry against the single-item and exclusion wording. The second treats draft housing as equivalent to the filtration main unit. Inspect a term mapping and the component in original data, retaining ambiguity until confirmed. The third targets the earlier draft while the answer claims to use a reviewed order. Inspect target identity and its version relationship, followed by actual citation content. A source label cannot establish that translation review was completed.
How to verify the setup
Acceptance should ask about main-unit inclusion in Chinese and a complete filter unit in English, inspecting which original each answer actually cites. Preserve the order exclusion and trace only and not included to their subjects; do not standardize housing without support. An accessory-purpose question also checks whether use was rewritten as purchased contents. After adding a term mapping or reviewed draft, inspect order applicability, processing and indexes before repeating questions. If several bases are associated with one retrieval node, apply the documented matching-embedding condition to those bases, without extending it to every base used throughout a workflow.
| Material context | Scope |
|---|---|
| Document types | Order Details, Shipping Rate Template, Shipping Label and Refund Records |
| Candidate sources | Shopify Admin, Payment Gateway, Carrier System and Inventory Location Data |
| Technical baseline | FastGPT v4.17.2 |