What the documents look like
This page covers cross-border material and multilingual retrieval in customer-service knowledge-base maintenance, at the document parsing and chunking step. Source material comes from the customer-service system, product detail pages, after-sales question statistics, and quality-check sampling. Day-to-day documents include FAQ entries, reply templates, product parameter tables, and escalation tickets. Knowledge-base maintenance is preparatory question-and-answer material organization; it does not approve refunds directly.
In cross-border work, one product may have a Chinese FAQ entry, an English reply template, and a multilingual parameter table at the same time. Parsing and chunking need to preserve a document identity and version that can be traced back to the source text. Semantic retrieval provides cross-language understanding, so a Chinese question can match an English knowledge point. That capability depends on how well the vector model was trained; precision is unstable and is affected by keywords and sentence completeness. Numbers, model designations, and proper nouns suit full-text retrieval or hybrid retrieval. After source material changes, the actual training and indexing state needs to be checked. A successful upload does not mean retrieval has already been updated.
Constraints at the document chunking step
Chunking follows the structure of the specific material. A condition, its explanation, and the content it constrains should stay in the same passage. One set of source data can map to several indexes, and the vector score takes the highest score. During retrieval, vectors are recalled first, then the source data is fetched by vector ID and merged. Knowledge-base search uses the library as its smallest search unit; a collection is for classified management of data and is unrelated to search results. The reference limit is measured in tokens. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used, and it is a filter threshold. Question optimization adds one model call before retrieval and can perform reference resolution and question expansion; the benefit is clearer for short questions or multi-turn follow-ups, and total latency increases. Image retrieval requires corresponding image input and model capability. Reranking requires a model and must be enabled by configuration. The optional paths shown in the document flow diagram do not mean every request executes all of them.
Hypothetical drill sample: the source text reads "Model A charger input voltage is 100 to 240 volts, region of use is the European Union. Model B charger input voltage is 220 to 240 volts, region of use is the United Kingdom." The proposed sentence is "Model A charger and Model B charger have the same input voltage." The point to decide is whether the input-voltage range fields in the two source texts agree. The material does not state that the two models can be used interchangeably.
Configuration and implementation checks
The table lists only settings documented in the official material or implementation checks relevant to document parsing and chunking on this page. The values are starting points for this hypothetical trial, used to compare against sample retrieval results.
| Configuration item | Hypothetical starting point | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains model and region fields; semantic recall and full-text recall are used together and merged through RRF before candidates are compared |
| Minimum relevance | Hypothetical starting point 0.4 | Used to filter low-relevance results; compared item by item against what the sample retrieval returns, then adjusted |
| Reference limit | Hypothetical starting point 2000 tokens | The sample passages are short; compare whether the referenced content covers the complete conditional sentence |
| Question optimization | Not enabled for now | The sample question has a complete subject; observe basic retrieval first before deciding |
| Result reranking | Not enabled for now | The candidate count is limited; check whether candidates are sufficient before enabling |
| Image retrieval | Not enabled | The sample is plain text material with no image input, so the image path is not triggered |
Three common errors
- Treating a successful upload as retrieval already updated. After source material changes, the actual training and indexing state needs to be checked. When evidence of completed training is missing, only a pending-verification note can be recorded.
- Treating the minimum relevance value as a permission or entity-isolation mechanism. That value filters low-relevance results; it does not exclude unrelated businesses or unauthorized records, and retrieval does not carry authorization functions.
- Treating a handover request awaiting review as already approved and executed. While a request is in pending-review status, no conclusion can be drawn that operational authority exists or that an action has occurred.
How to verify the setup
- Enter the sample source text and the proposed sentence above, compare the input-voltage field values in the two source texts for Model A and Model B, and observe whether the proposed sentence agrees with the source text. If the field values are mutually exclusive, the proposed sentence is not supported. If the source text does not state interchangeability, record the evidence gap and do not judge it as a negation.
- Run retrieval with "Model A charger input voltage," compare whether the returned content contains the complete conditional sentence and the region qualifier, and observe whether chunking separated the condition from the content it constrains. For an empty result, check the downstream design against the paper input; do not claim that a fictional rule necessarily returns empty.
- For the sample handover request, check the authorization status field and compare the request's current status with approved and executed records. While the request is pending review, record it as unsupported. Check signing, cooperation reached, and contract effectiveness as three separate facts, and do not fabricate authorization clauses.
The public technical facts checked for this feature are used to identify related content in the document and are not required to be copied as a fixed five-row table: source-material checking (check the source structure and parsing result first, and preserve a document identity and version that can be traced back to the source); chunk completeness (check passages against the specific material structure and avoid separating a condition, its explanation, and the content it constrains); data and index (distinguish source data from retrieval indexes; one set of source data can map to several indexes); sample retrieval (retrieve with terms, numbers, and natural language separately, and check how chunking affects the actual question); post-update review (after source material changes, check the actual training and indexing state; a successful upload is not the same as retrieval already updated).
This page addresses only customer-service knowledge-base maintenance, cross-border material and multilingual retrieval, and document parsing and chunking. Which materials, operations, and requests are in which authorization state during an operational handover is a separate matter; retrieval and forms are not authorization execution.