What the documents look like
Closure settlement moves in the opposite direction from site selection and store opening. It handles exit and asset recovery, so the material set is different in kind: closure approvals, inventory clearance forms, lease termination agreements, and fixed-asset count sheets. The underlying data arrives from POS inventory, the fixed-asset system, lease contracts, and financial ledgers. Operations staff, finance, legal, and the landlord contact all work from these records.
Cross-border and multilingual conditions are common. For one store, the lease termination agreement may be in a foreign language, the inventory clearance form in Chinese, and the asset count sheet may carry part numbers and model designations. Materials update irregularly, per store. Document parsing and chunking must preserve the document identity and version that allow a retrieved passage to be traced back to its source, so that material in different languages and formats can still be located at the level of a specific clause or quantity.
Constraints at the document chunking step
Chunk completeness directly affects retrieval quality. If a condition in a lease termination agreement is separated from the clause it constrains, retrieval may return only half of the sentence. FastGPT maps one group of data to multiple vectors; a single group of original data can correspond to multiple indexes, and a hit on any one index recalls that group. Semantic retrieval supports cross-language understanding, for example a Chinese question matching an English knowledge point, which suits multilingual material. Full-text retrieval suits exact queries such as numbers, models, and proper nouns. Hybrid retrieval merges multiple result paths through RRF, and a ReRank model re-sorts candidate text and produces a score from 0 to 1. The citation limit is counted in tokens, and the minimum relevance threshold takes effect only under semantic retrieval or when result reranking is used. Retrieval is a recall operation. It does not perform authorization decisions and does not isolate entities or permissions.
Hypothetical drill sample: the source text for one store's closure settlement reads, "The lease termination agreement (No. L-2024-017) provides that the deposit is refunded within 30 days after handover is completed; the inventory clearance form states 12 shelving units and 3 cash registers; the asset count sheet lists the above assets with status pending review." The draft sentence to be assessed is, "The deposit refund obligation took effect upon completion of handover." That sentence involves a subject, an object, a status, and a scope of validity, and must be compared against the source text.
Configuration and implementation checks
The table below lists, for the sample above, documented settings or implementation check items related to document parsing and chunking. The values are starting points for this hypothetical trial, not official defaults.
| Configuration item | Hypothetical starting point | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains number L-2024-017 and a natural-language question; compare the semantic and full-text recall paths |
| Citation limit | Set to 2000 tokens for this trial | The sample passages are short; compare whether clauses enter the citation intact at different values |
| Minimum relevance | Set to 0.3 for this trial | Effective only under semantic retrieval or reranking; compare candidate counts before and after filtering |
| Question optimization | Enabled | The sample question contains a reference; compare the retrieval terms before and after completion |
| Result reranking | Enabled | When candidates are numerous, compare the ordering change produced by rerank scores |
| Chunk review | Manual review along clause boundaries | Check whether a condition and the content it constrains remain in the same group |
Three common errors
- Treating a successful upload as an updated index. Input is a revised lease termination agreement; compare the upload record against the actual training and index state, and observe whether retrieval still returns the old clause. A return of old content indicates the index has not been updated.
- Treating a retrieval hit as authorization. Input is a deposit refund question; compare the retrieval result against the authorization record, and observe whether recalled content is used directly to execute a refund. With no authorization record, execution must not proceed.
- Treating minimum relevance as a permission or entity filter. Input is a cross-store query; compare results before and after filtering, and observe whether the threshold is assumed to exclude unrelated stores. The threshold affects relevance ordering only.
How to verify the setup
- Verification one: input the sample source text and the draft sentence "The deposit refund obligation took effect upon completion of handover." Compare the source wording "the deposit is refunded within 30 days after handover is completed" against the draft sentence, and observe whether the draft conflates the refund period with the effective condition. The condition is met when the reviewer can state that the source specifies only a refund period and does not state when the obligation takes effect.
- Verification two: input number L-2024-017 and run full-text retrieval and semantic retrieval separately. Compare the two recall paths and observe whether the number is matched accurately. The condition is met when full-text retrieval matches the number; semantic retrieval results require manual review.
- Verification three: input an empty-result handover scenario, using the explicit paper input [] to check the downstream design. Compare the empty array against the downstream handling logic, and observe whether an empty result is misread as absence of permission. The condition is met when the empty result is recorded and the evidence gap is preserved, without inferring authorization status.
Libraries built on different vector models cannot be associated together; verify identifiers first, exclude those that do not match, and record the actual observation. Image retrieval requires a corresponding image input and model capability, and reranking requires a model enabled under the settings. A pending review does not demonstrate absence of operating permission; signing, reaching agreement, and contract effectiveness are three distinct facts.