What the documents look like
A fresh-food concession counter in a hypermarket is often run by a supplier who stations staff on site and shares sales revenue with the store. The materials that need day-by-day and batch-by-batch comparison are the concession contract, the daily sales report, the loss report, and the food purchase inspection record. Disputes over weighed sales and loss allocation frequently come from mismatched subjects, periods, or field definitions across these materials.
Document parsing and chunking must cut the contract, reports, loss sheets, and inspection records into fragments that can point back to the original text, so that a comparison can locate a specific clause or a specific batch. For example, a loss-allocation ratio in the contract must stay attached to the counter it applies to and the period it covers. If a chunk separates the ratio from its applicable counter or period, the comparison loses the condition that gives the number its meaning.
The contract and the transaction materials differ in structure. The contract carries clauses with conditions and scope. The daily sales report carries figures by date. The loss report carries batch identifiers. The inspection record carries receiving details per batch. Chunking that treats all four as one uniform text type will either split a clause from its condition or merge unrelated batches into one fragment. The practical requirement is to preserve the internal structure of each material type while keeping every fragment traceable to its source.
Constraints at the document chunking step
Several documented conditions shape how chunking and retrieval behave for this comparison task.
A single group of original data can map to multiple indexes. When retrieval hits any one of those indexes, the whole group of original data is recalled, and the vector score takes the highest score among them. This matters for contract clauses: a long clause can carry several indexes, and hitting any one of them brings back the full clause rather than only the matched sentence.
Retrieval operates at the library level. A collection is used only for classification and management and does not affect search results. This means the comparison scope is the whole library, not a single collection.
Semantic retrieval relies on vector distance. Full-text retrieval suits numbers, models, and proper nouns. Hybrid retrieval combines semantic and full-text recall through RRF. After reranking, a 0–1 score represents relevance and can be used for filtering. The minimum relevance setting takes effect only under semantic retrieval or when result reranking is used. It is a filtering threshold and does not perform permission or entity isolation.
A library requires the same vector model throughout. Libraries built on different vector models cannot be associated together. This is a condition on the retrieval library itself and does not extend to any external system.
The following hypothetical paper sample is provided for this page only. It is not a real customer, company, or legal identity.
Hypothetical paper sample: A concession contract original text states: "Party A is the fresh-food department of XX hypermarket; Party B is XX aquatic products supplier; the loss-allocation ratio is executed at three percent of the counter's monthly sales; the applicable period is March 1, 2025 to May 31, 2025." A draft sentence awaiting judgment states: "Party B bears all losses of the counter from April 1, 2025."
The draft sentence and the contract original text must be compared field by field on subject, object, period, and ratio.
Configuration and implementation checks
The table lists only documented settings or implementation checks relevant to document chunking for this page. The hypothetical trial starting values are for this paper check only and are not official defaults.
| Configuration or check item | Hypothetical trial starting value for this check | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | Contract clauses contain natural language; loss sheets contain batch numbers; both query types need coverage |
| Citation limit | Hypothetical starting value 3000 tokens | Estimated from the sample contract and loss-sheet fragment lengths; compared against the total fragment volume of a single material |
| Minimum relevance | Hypothetical starting value 0.3 | Compared against the 0–1 rerank score range; observe whether contract clause fragments remain after filtering |
| Question optimization | Off for this check | The sample question already contains subject and period; enabling it adds one model call |
| Chunk completeness check | Check clause conditions against constrained content item by item | Compared against the original clause structure; confirm conditions and constrained content are not split apart |
| Training and index status | Check actual training status after upload | Compared against actual retrieval hits; upload success is not treated as retrieval already updated |
Three common errors
- Treating "all losses" in the draft sentence as an already agreed contract term. Input: the draft sentence and the contract original text. Comparison object: subject, object, period, and ratio. The contract original text states three percent and limits the period to March 1 to May 31, 2025; the draft sentence expands to all losses from April 1. The two definitions do not match, and the draft sentence is not supported by the contract original text.
- Treating minimum relevance as a permission filter. Input: one semantic retrieval request. Comparison object: returned fragments and the material scope the requester is authorized to view. The relevance score reflects semantic similarity only and does not exclude unauthorized records. Permission requires a separate system implementation check.
- Treating upload success as retrieval already updated. Input: the upload action of a new loss sheet. Comparison object: upload status and actual retrieval hit results. Upload success does not ensure training and indexing are complete. The actual training status must be checked before retrieval.
How to verify the setup
- Input the hypothetical sample contract original text and the draft sentence. Compare the four fields of subject, object, period, and ratio. The contract original text states three percent and limits the period to March 1 to May 31, 2025; the draft sentence states all losses from April 1. The result is recorded as unsupported, with the evidence gap noted.
- Input a loss-sheet fragment containing a batch number and run full-text retrieval. Compare whether the returned fragment points back to that batch's original text. If the returned fragment lacks the batch number or points to another batch, record it as the chunk failing to preserve the number, then adjust chunking and recheck.
- Input a paper query that clearly has no corresponding material. Compare the returned result against the empty-citation design. If the returned result contains unrelated fragments, record it as a filtering condition needing review. The empty-result handoff can be checked against a clearly stated paper input [] for the downstream design. This does not assert that a search for a fictional rule always returns [].
Operational handoff: which materials, operations, and requests are in what authorization state cannot be resolved by retrieval or by a form. Retrieval is not authorization. Form collection, business verification, approval, database writes, and external synchronization each require their stated downstream implementation.