What the documents look like
Channel diversion review work concerns regional and price order. The material set named in the brief is diversion complaint forms, anti-counterfeit code records, penalty notices and inspection photographs. The procurement and supplier task inside this category adds purchase documents, supplier files and shipment records, and the work is to keep the correspondence between those records visible at the chunking step.
Three industry facts shape this page. First, the data sources are the anti-counterfeit code system, e-commerce platform prices, distributor shipment records and market inspections, so a single chunk may need to carry a batch number, a code range, a region and a supplier subject at the same time. Second, the update frequency is weekly and irregular, which means a change to source material is followed by a re-check of the actual training and index state rather than an assumption that retrieval has already moved. Third, the most time-consuming steps are tracing goods source codes, comparing low-price stores and executing penalties; parsing and chunking sit upstream of all three, and a chunk that separates a condition from the content it constrains will not support them.
Documents in this task carry document identity and version information that allows a result to be traced back to the source text. That identity is separate from the retrieval index.
Constraints at the document chunking step
Segmentation follows the structure of the specific material. A condition, its explanation and the content the condition constrains stay in the same segment. Original data and retrieval indexes are distinct; one group of original data can map to multiple indexes, which is the multi-vector mapping described in the official knowledge base documentation. Retrieval is a recall behaviour. It does not perform authorization judgement and does not by itself constitute a basis for executing a penalty.
Hypothetical paper exercise sample. Source material reads: "Complaint form CX-2024-017 states that Distributor A sold goods of batch B2401 in the East China region, with anti-counterfeit code range 3100 to 3200. Supplier file SP-009 records that the supply recipient of this batch was Distributor B, with a supply date of March 2024." The draft sentence to be assessed is: "Distributor A purchased batch B2401 from Supplier B and sold it across regions."
The comparison checks whether the selling subject recorded on the complaint form and the supply recipient recorded in the supplier file are the same subject, and whether the batch number and code range correspond to the same period. If the two subjects differ, the draft sentence is not supported. If the material does not state a purchase relationship, the point remains to be verified. No penalty, approval or refund is claimed here; this is a paper exercise only.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Batch numbers and code ranges are exact lookups while natural-language descriptions are semantic; compare recall differences for the same question across modes |
| Reference limit | Measured in tokens; starting point set to cover one complaint form plus one supplier file | The documentation gives no specific figure; the starting point follows sample material length and is compared against truncation behaviour |
| Minimum relevance | Starting point set to a mid-range value | The documentation describes this as a 0-1 filter threshold only; the starting point is hypothetical and is compared against the relevance level of recalled content |
| Question optimisation | Enabled for short questions and multi-turn follow-ups | The documentation states this adds one model call; compare retrieval effect against elapsed time |
| Result reranking | Enabled when candidates are numerous | The documentation states rerank scores can be used for filtering; compare against ordering without reranking |
| Image retrieval | Available only when inspection photographs are indexed and the corresponding model is configured | The documentation states image retrieval depends on indexing at ingestion and model capability; compare against text-only query results |
The documentation records that FastGPT uses PostgresSQL with the PG Vector extension as the vector retriever, with an HNSW index, while MongoDB stores other data, and that the dataset.datas table holds an indexes field recording vector IDs. These are documented storage facts, not settings to be reproduced on every page. RRF is a fusion method for combining multi-path results, not a switch.
Three common errors
- Treating the selling subject on the complaint form as the purchasing subject. Input is the complaint form and the supplier file; compare the subject fields recorded in each. If the subjects differ, the draft sentence is not supported.
- Treating a successful upload as retrieval already updated. Input is the changed material and a retrieval request; compare the recalled content against the changed content. If the recalled content is still the pre-change version, the index state remains to be verified and the actual training state needs checking.
- Treating a similarity score as an authorization or entity filter. Input is a retrieval request and its results; compare whether results include records outside the target scope. If they do, this shows retrieval does not perform permission judgement. A relevance score is not a category, entity or permission filter.
How to verify the setup
- Input the complaint form text from the hypothetical sample above together with the draft sentence. Compare the selling subject field against the supply recipient field. If the subjects differ, the draft sentence is not supported; if the material does not state a purchase relationship, record the point as to be verified.
- Input the changed supplier file and a retrieval request. Compare recalled content against the changed content. If recall still returns the pre-change version, the index state remains to be verified and the actual training state must be checked. A pending request is not approval.
- Input the batch number and code range as a retrieval query. Compare whether returned results include the corresponding source text. If the return is empty, record the gap; do not infer that a retrieval rule necessarily returns an empty result. Where an empty result must be carried forward, use an explicit paper input of an empty list to check downstream design, and do not claim that a hypothetical rule returns an empty list.
Cross-model association is not possible. Check identifiers first and exclude those that do not match; do not run cross-model retrieval to compare missed recall. Optional paths in the overall flow diagram do not mean every request executes them: image retrieval requires image input and model capability, and reranking requires a model and must be enabled. Where material is not provided, the field remains unknown.