What the documents look like
For multilingual work in Marketplace advertising, this page examines FastGPT vector indexes. Preserve planning and execution stages in ad-material indexes without deriving outcomes from creative suggestions. The examples are hypothetical.
Constraints at the vector indexes step
A hypothetical creative note proposes footage of a cleaning procedure while leaving landing-page wording for review. Its English counterpart preserves the plan and pending review. A historical material list records only an older clip upload; it establishes neither this campaign’s launch nor an outcome. An English entry that changes planned creative into a running ad has introduced execution at the search layer. Uploading a clip, launching an ad and observing a business result need separate records. An index may locate the note without merging these events. Cleaning procedure is only an invented material label here, without product efficacy testing or support for an added performance claim.
Configuration and implementation checks
| Ad-language check | Proposed material | Inspection action |
|---|---|---|
| Plan status | Proposed cleaning-procedure footage | Add no running-ad state in either language. |
| Event distinction | Upload, launch and outcome | Find a record for each current event. |
| Version identity | Older upload versus current creative | Check material and campaign correspondence. |
| Wording review | Landing-page copy still pending | Locate an actual review decision separately. |
| Creative purpose | Footage theme is not efficacy testing | Supply no outcome or certification from retrieval. |
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
- If proposed footage becomes a running ad in an English entry, compare stage wording and the targeted original to locate the added launch status.
- If an older upload is used for this campaign’s outcome, inspect material version, campaign identity and event type. An upload is not performance data.
- If landing-page wording pending review becomes approved in the answer, compare the original, citation and response to identify the absent review record.
How to verify the setup
Ask in both languages what is proposed, which stage has a record and whether wording review is complete. Trace each response separately. When launch or review material is supplied, inspect its subject and event time; later receipt does not imply later execution. After adding language entries for the same original, compare their actual targets. Matching both queries establishes neither efficacy nor advertising performance. Inspect embedding models within the associating search node without treating consistency as proof of reliable rewriting. This exercise publishes no ad, approves no claim and supplies no outcome figures.
| Material context | Scope |
|---|---|
| Document types | Advertising Plan, Keyword Report, Audience Package Description and Conversion Attribution Table |
| Candidate sources | Juliang Qianchuan, JD Kuaiche advertising, Alimama and Store Transaction Data |
| Technical baseline | FastGPT v4.17.2 |