What the documents look like
Brand after-sales repair work centers on warranty responsibility and physical service outlets. The records handled at this stage are warranty cards, repair work orders, inspection reports, and replacement approvals. Data reaches the team from the after-sales system, store orders, repair outlet feedback, and the national three-guarantees catalog. In cross-border and multilingual settings, one repair work order may be entered in a different language from the warranty card it belongs to, and the inspection report carries a number, a model, and a fault description that must line up with the valid scope stated on the warranty card. Document parsing and chunking has to keep the conditions, the conclusion, and the object those conditions constrain inside the same data segment, so retrieval can locate them together.
Three facts from this category shape the chunking step. First, the unit of work is a warranty obligation tied to an outlet, not a platform refund, so the material that matters is the card, the work order, the report, and the approval. Second, the same repair case can arrive as several documents in several languages, which means a chunk boundary that separates a model number from its validity period removes the evidence retrieval needs. Third, data updates per order and per day, so re-checking after a material change is part of normal operation rather than an occasional audit.
Constraints at the document chunking step
A parsed result must be able to point back to the document identity and version of the source text. Chunking follows the structure of the specific material, and a conditional clause cannot be separated from the conclusion it governs. One group of original data can map to multiple vector indexes, so the original data and the index entries are checked separately. Semantic retrieval carries cross-language understanding and suits natural-language questions. Full-text retrieval suits numbers, model codes, and proper nouns. Hybrid retrieval merges the result paths through RRF, and reranking requires a model and must be enabled by configuration. Similarity filtering and the reference cap act only on result trimming; they do not perform permission or entity isolation.
Hypothetical paper exercise sample. A warranty card reads: "This warranty card applies to the main unit of model XR-200. The warranty period runs twelve months from 1 March 2025. Repair is limited to authorized outlets in mainland China." A repair work order reads: "Date sent for repair: 10 June 2025. Fault description: will not power on. Inspection conclusion: mainboard affected by moisture." A draft sentence awaiting review reads: "The moisture damage to the mainboard falls within the warranty scope and qualifies for a free replacement." The review compares the valid scope stated on the warranty card against the inspection conclusion on the work order. The sample does not state whether moisture damage falls under warranty responsibility, so the draft sentence is not supported by the supplied text.
Configuration and implementation checks
| Configuration item | Setting or check relevant to this page | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, hybrid retrieval | Model code XR-200 is checked with full-text retrieval for the number hit |
| Result reranking | Enabled by configuration when a ReRank model is available | Secondary ordering when candidates are many; the score is used for filtering |
| Minimum relevance | A 0-1 value, effective only under semantic retrieval or reranking | Hypothetical trial starting value 0.3, compared against 0.5 to observe trimming differences |
| Reference cap | Each search references at most n tokens of content | Controls the length of reference content returned to the model |
| Question optimization | Coreference removal and question expansion before retrieval | Completes the subject in multi-turn follow-ups; a single clear question can leave it off |
| Chunk check | Original data and the indexes field are checked separately | Warranty card conditions and conclusion must stay in the same data segment |
The minimum relevance starting value of 0.3 is a choice for this hypothetical trial. The reason is that the sample question is short and its subject is explicit, and the value is compared against 0.5 to observe how the recall range changes. This value is not an official default.
Three common errors
- Treating a successful upload as an updated retrieval index. Input: the hypothetical sample warranty card. Comparison: the upload record against the actual training and index state. Observable result: whether retrieval can hit that segment. A completed upload does not mean the index has been built.
- Treating a similarity score as a permission filter. Input: a query for model XR-200. Comparison: the returned content against the authorized outlet scope. Observable result: whether records outside that scope appear. Minimum relevance only trims low-scoring results and does not exclude unauthorized records.
- Treating a draft sentence as a confirmed conclusion. Input: "moisture damage to the mainboard falls within the warranty scope." Comparison: the valid scope stated on the warranty card against the inspection conclusion on the work order. Observable result: whether the source text states where responsibility for moisture damage lies. The sample does not state it, so the sentence remains unverified.
How to verify the setup
- Input the hypothetical sample warranty card text and the question "When does the XR-200 warranty expire?" Compare whether the returned content contains the twelve months from 1 March 2025, and observe whether the conditional clause and the conclusion return in the same segment. If the returned content omits the period or splits the condition away, the chunking needs adjustment.
- Input the code "XR-200" and the natural-language question "can moisture damage to the mainboard be repaired under warranty," using full-text retrieval and semantic retrieval respectively. Compare the hit differences between the two paths, and observe whether the code query hits the warranty card and whether the natural-language query recalls the work order. If either path misses, record the actual observation without inferring a cause.
- Input an empty-result continuation scenario, using the explicit paper input
[]to check the downstream design. Compare whether the downstream handles it as an empty reference, and observe whether unsupported completion appears. This check does not claim that a fictional rule search returns[].
Libraries built on different vector models cannot be associated together. Check the identifiers first, exclude those that do not match, and record the actual observation. Image retrieval requires an 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.