What the documents look like
This page covers cross-border materials and multilingual retrieval in franchise filing management, at the document parsing and chunking step. The category works with the commercial franchise information management system, enterprise licences, store lists and contract ledgers. The documents that appear most often are franchise contracts, operations manuals, filing materials and information disclosure documents.
In cross-border work, one set of materials may exist in Chinese and in another language at the same time. Chunking therefore has to preserve a document identity and version that can be traced back to the source text. Without that, retrieval cannot tell which language version or which revision was matched. Materials are updated irregularly. A successful upload and an actual refresh of the retrieval index are two separate events and are checked separately.
Two or three facts shape this task directly. First, the same source data can map to more than one index, so one chunk may be reachable through several vectors. Second, semantic retrieval carries cross-language understanding, but that ability depends on how the vector model was trained, is not stable in precision, and is affected by keywords and sentence completeness. Third, full-text retrieval suits exact lookups such as numbers, models and proper nouns. Retrieval itself performs no authorisation judgement. A record being retrievable does not mean it is in a state that can be submitted externally or signed.
Constraints at the document chunking step
Multilingual materials require language markers and structural boundaries to survive chunking. A condition, its explanation and the content it governs should not be split apart. Source data and retrieval index are two layers. Vector retrieval recalls vectors first, then uses the vector ID to fetch the source content from the dataset.datas table in MongoDB. When several vectors resolve to the same source record, the results are merged and the highest score is kept.
The overall retrieval path is not a single step from user question to vector store to result. Depending on input and search parameters, FastGPT combines text, images, semantic recall, full-text recall, question optimisation and reranking, then fuses the multiple result streams into cited content. RRF is the merge method for those streams. Rerank performs a second ordering of text results. Image retrieval is an added input path on the existing search chain and requires a suitable image input and model capability. These optional paths are not executed on every request.
Hypothetical drill sample. A franchisor headquarters intends to check the parsing result of one cross-border filing document. The source text contains two sentences: "Party A grants Party B the franchise right within the designated territory, for a term of three years from the date of signing. Party B shall complete operations manual training before opening." One draft sentence is submitted for judgement: "Party B obtains exclusive rights over all territories immediately after signing." The source states only a designated territory and a three-year term. It does not state any exclusivity scope. The phrase "all territories exclusive" in the draft is not supported by the source and is recorded as pending verification.
Configuration and implementation checks
The table below is a hypothetical starting point for this drill. It illustrates a comparison method and is not an official default value.
| Configuration item | Hypothetical starting point for this drill | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains a proper noun such as "franchise right" and natural sentences; compare semantic and full-text recall paths |
| Minimum relevance | 0.5 | This value takes effect only in semantic retrieval or with reranking; compare against sample hit results before adjusting |
| Citation limit | 2000 tokens | Controlled by tokens; sample passages are short, compare against the length of matched content |
| Question optimisation | Enabled | The sample contains a reference; compare the retrieval terms before and after enabling |
| Result reranking | Enabled | A ReRank model is required; compare ordering before and after reranking |
| Chunking method | By clause boundary | The two sample sentences are separate clauses; compare retrieval differences between splitting and preserving |
Three common errors
- Treating a successful upload as an updated index. The input is the upload record and training status of the sample material. Compare the timestamps and status fields of the two. The observable result is an entry that has been uploaded but not trained.
- Treating a semantic recall result as an authorisation basis. The input is the sample draft sentence and the source text. Compare the subject, the object and the effective scope. The observable result is whether "all territories exclusive" appears in the source text.
- Treating minimum relevance as an entity or permission filter. The input is a cross-subject query. Compare the subject identifier of the returned records. The observable result is whether records belonging to a non-target subject appear. This value only filters low-relevance results.
How to verify the setup
- Input the two source sentences and the draft sentence from the hypothetical drill. Compare whether the draft contains "all territories exclusive", which the source does not state. If the source does not contain that wording, the judgement is not supported and the evidence gap is retained.
- Input the term "franchise right" and a natural-language question separately. Compare whether the matched passages still preserve clause boundaries. If a condition and the content it governs are split apart, record it as a chunking issue.
- Input one cross-subject query. Compare the subject identifier of the returned records. If content belonging to a non-target subject appears, record it as a retrieval-scope observation. Do not draw a permission conclusion from it.
For an empty-reference case, the downstream design can be checked against a paper input of an empty list. This does not assert that a fictional rule returns an empty list. Where libraries use different vector models, they cannot be associated with each other. Verify identifiers first and exclude non-matching ones rather than running a cross-model retrieval to compare missed recalls. Where the total document flow chart lists optional paths, those paths are not all executed on every request.