What the documents look like
Retail plastic bag and single-use item reporting centers on disposable plastics reporting and environmental supervision. It draws on the Ministry of Commerce reporting system, POS plastic bag sales, procurement systems, and store ledgers. The everyday documents are plastic bag procurement ledgers, sales vouchers, semi-annual reports, and self-discipline commitments. These materials share fixed fields, a clear reporting entity, and a defined period. Amounts and quantities must correspond to vouchers. The same batch of material frequently appears in Chinese, English, or other languages at the same time. In cross-border material and multilingual retrieval, parsing and chunking decide whether later retrieval can locate the correct entity, the correct period, and the correct field.
The semi-annual submission and daily update rhythm means material versions keep changing. A successful upload and a retrieval index that has actually been refreshed are two different states. After material changes, the actual training and index status needs to be checked.
Constraints at the document chunking step
A FastGPT knowledge base consists of libraries, collections, and data. The smallest search unit is the library. The MongoDB dataset.datas table stores vector source data, and its indexes field records the corresponding vector IDs, so one set of data can map to multiple vectors. The PostgresSQL vector field stores vectors. During retrieval, vectors are recalled first, then the source data is found in MongoDB by vector ID. When several vectors correspond to the same source data, the entries merge and the vector score takes the highest value.
Multi-vector mapping balances content length against semantic richness. A longer text can carry several vectors, and if any one of them is recalled, that data is returned. Segmentation should keep structure and semantics complete and content single-purpose. A condition, an explanation, and the object it constrains should not be split apart.
Retrieval includes semantic search, full-text search, and hybrid search. Hybrid search merges multiple result paths through RRF. ReRank reorders candidate text and produces a 0-1 score. Minimum relevance takes effect only under semantic search or when result reranking is used. The citation limit is controlled by tokens. Query optimization adds one model call before retrieval for coreference resolution and query expansion. Image retrieval requires the corresponding image input and model capability. Reranking requires a model and is enabled according to settings. Minimum relevance is a filtering threshold and does not perform permission or entity isolation.
Configuration and implementation checks
Hypothetical paper exercise sample: a supermarket headquarters compliance role checks a Plastic Bag Procurement Ledger (Second Half of 2025) in March 2026. The source text reads: "Supplier: Huadong Packaging Products Co., Ltd.; Contract No.: HDBZ-2025-071; Procurement quantity: 120,000 units; Unit price: 0.18 yuan; Delivery period: July 2025 to December 2025." The draft sentence to be judged reads: "Huadong Packaging Products Co., Ltd. supplied 120,000 units in the second half of 2025 at a unit price of 0.18 yuan." This sample only illustrates how parsing and chunking are checked and does not represent any real customer or completed business action.
| Configuration or check item | Value or action on this page | Basis and explanation |
|---|---|---|
| Search mode | Hybrid search | The ledger contains numbers and proper nouns alongside natural-language questions |
| Citation limit | Hypothetical trial starting point of 3000 tokens | Ledger segments are short; this value allows comparison of citation coverage against 1500 tokens |
| Minimum relevance | Hypothetical trial starting point of 0.4 | Used to observe candidate count changes after filtering, compared against 0.2 |
| Result reranking | ReRank enabled according to settings | The rerank score is used for secondary ordering and filtering |
| Segmentation check | Split along ledger field boundaries; keep contract number and delivery period in the same segment | Prevents a condition and its subject from being separated |
| Post-update review | Check actual training and index status after changes | A successful upload does not equal a refreshed retrieval index |
Three common errors
- Treating retrieval as authorization. Retrieval returning ledger content does not mean the supplier passed review, the contract took effect, or payment was executed. In the sample, the contract number and delivery period only describe what the ledger records. Approval status, signing status, and payment status are not stated.
- Treating minimum relevance as an entity or permission filter. A similarity threshold only affects candidate ordering and filtering. It does not exclude unrelated business entities and does not verify access permission.
- Treating a successful upload as a refreshed index. After material changes, the actual training and index status needs to be checked. Before that check, whether a new segment can be retrieved remains unknown.
How to verify the setup
- Verification one: Enter the sample source text and the draft sentence, and compare whether the supplier name, quantity, and unit price in the draft sentence match the source text on the same entity, the same period, and the same field. The source text does not state approval or signing status, and the draft sentence does not claim it. The condition is met when the draft sentence stays within the source text scope. If the draft sentence adds content such as "passed review" that the source text does not state, the condition is not met.
- Verification two: Use contract number HDBZ-2025-071 as a full-text search input and "second-half plastic bag procurement unit price" as a semantic search input, then compare whether the segments returned by both query types contain the delivery period and unit price fields. The condition is met when segments preserve field boundaries and the cited content can be traced back to the source text. If segmentation splits the delivery period and unit price into different chunks, segmentation needs to be rechecked.
- Verification three: Use the paper input of an empty citation list [] and check how the downstream design handles the absence of cited content. This check confirms the empty-result branch and does not assert that a fictional rule search necessarily returns an empty list. Libraries across different vector models cannot be jointly associated. The library identifier should be checked first and non-matching ones excluded, with actual observations recorded. A cross-model retrieval is not executed to compare missed recalls.