What the documents look like
This page covers one task inside platform advertising and on-site traffic operations: cross-border reference material and multilingual retrieval, handled at the document parsing and chunking stage. The material surface for this category is made up of campaign plans, keyword reports, audience package descriptions, and conversion attribution tables. These come from platforms such as Juliang Qianchuan, JD Express, and Alimama, together with store transaction data, and the update frequency is daily or hourly.
For cross-border advertising, one campaign plan may exist in both a Chinese version and a target-market language version at the same time. Keyword report fields such as identifiers, bid values, and audience package labels are exact-query objects. Natural-language descriptions are semantic-query objects. This split directly shapes parsing and chunking: a segment has to keep the bid value, the keyword identifier, and the attribution definition together with their surrounding context, and a conditional clause cannot be separated from the campaign object it constrains. The correspondence between a Chinese version and its translated version also depends on each segment retaining identity information that can point back to the source.
Constraints at the document chunking step
In the FastGPT vector storage structure, the MongoDB dataset.datas table stores the original vector data, and an indexes field records the corresponding vector IDs. One group of data can map to multiple vectors. This means one passage of campaign plan text can be mapped to several index groups, and if any one group is retrieved, that data is recalled. When chunking, building separate indexes for a keyword identifier, a bid condition, and an attribution definition can improve the hit conditions for exact queries.
On the retrieval side, semantic retrieval suits natural-language phrasing, and cross-multilingual understanding is a documented capability of it. Full-text retrieval suits exact queries such as numbers, model numbers, proper nouns, and error codes. Hybrid retrieval merges the two result paths through RRF. The reference limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or result reranking. These settings belong to the retrieval stage, which is a different stage from the chunking and index state created when material is ingested.
Hypothetical paper exercise sample: the source material text is three sentences. "March 2025 campaign plan version A, target market Germany region, daily budget 800 yuan. Keyword identifier KW-0417, bid ceiling 2.3 yuan, audience package label AUD-DE-02. Conversion attribution definition is seven-day click attribution." One draft sentence to be judged is: "This plan has been approved by the brand marketing department and can directly execute Germany-region delivery on March 5." The approval and execution states in this draft sentence are not stated anywhere in the source material text.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this page | Reason for the choice and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | Keyword identifiers and natural-language phrasing both exist; compare the separate results of semantic retrieval and full-text retrieval |
| Reference limit | Hypothetical starting point 3000 tokens | This value is the paper exercise starting point, used to compare reference completeness under different chunk lengths |
| Minimum relevance | Hypothetical starting point 0.4 | Exercise starting point, observed against the 0-1 rerank score range for filtering effect |
| Question optimization | Enabled for multi-turn follow-up scenarios | The document states it adds one model call; compare against a single-turn, already-clear question scenario |
| Chunk indexes | One index group each for keyword identifier and bid condition | Corresponds to the documented structure where the indexes array can record multiple vector IDs |
| Post-update check | Manual check of training and index state | A successful upload and an updated retrieval state are two different facts |
Three common errors
- Treating a successful upload as an updated retrieval state. After material changes, the actual training and index state needs to be checked; the upload action itself does not indicate the index has been rebuilt.
- Mixing a keyword identifier and a natural-language description into the same chunk. Identifier queries depend on full-text retrieval and natural-language phrasing depends on semantic retrieval; mixing them in one chunk weakens the comparison basis for both query types at once.
- Treating the approval and execution states in the draft sentence as already stated in the source material. The sample source text states only the budget, bid, audience package label, and attribution definition; approval and execution are unsupported fields.
How to verify the setup
- Input "KW-0417" from the sample source text, compare the returned chunks from full-text retrieval and semantic retrieval, and observe whether the chunk containing the identifier keeps the complete context of the bid ceiling 2.3 yuan and the audience package label AUD-DE-02.
- Input the draft sentence "This plan has been approved by the brand marketing department and can directly execute Germany-region delivery on March 5," compare the retrieved content against the sample source text, and observe whether the source states an approving party and an execution date. When it does not, record this as pending verification, without judging it as a negation or a conflict.
- Input the paper empty result
[], and check the downstream design's handling condition for an empty reference; this check does not mean a fictional-rule retrieval is guaranteed to return an empty result. Libraries across different vector models cannot be associated together; first check the identifiers and exclude those that do not match, then record the actual observation.
This page addresses only platform advertising and on-site traffic / cross-border reference material and multilingual retrieval / document parsing and chunking. During operational handover, the authorization state of materials, operations, and requests must be kept distinct; retrieval or a form is not an authorization to execute.