What the documents look like
Channel receivables collection happens after credit sales, and the work centers on credit limits and cash recovery. The main data sources are the finance system, bank receipts, distributor orders, and sales contracts. Daily documents concentrate on four types: accounts receivable aging schedules, collection letters, credit agreements, and shipment freeze notices. Inventory procurement and supplier records form one category of business material that enters the knowledge base, covering purchase orders, supplier reconciliation records, goods receipt notes, and supplier qualification files. These materials update roughly weekly or monthly, and after a change the parsing and indexing status needs to be checked again.
Constraints at the document chunking step
A FastGPT knowledge base consists of the library, collections, and data. The smallest search unit is the library; collections only classify and manage data and do not affect search behavior. One group of data can map to multiple vector indexes. During retrieval, vectors are recalled first, then the vector ID is used to find the original data in the MongoDB dataset.datas table. When the same group of original data is hit through multiple paths, the results merge and the highest score is kept. This means chunking should keep each data block structurally and semantically complete and single. A condition, its explanation, and the content it constrains should not be split across different blocks.
Hypothetical paper exercise sample: an excerpt from a credit agreement signed by a distributor and a brand owner reads, "Party A grants Party B a credit limit of RMB 800,000, with a payment term of sixty days after shipment. Shipment above the credit limit by Party B requires Party A's written approval; a freeze notice issued without approval takes effect from the date of issuance." A draft sentence to be assessed reads, "The distributor has obtained Party A's approval for shipment above the credit limit, and the freeze notice has not yet taken effect." In this sample the parties are Party A and Party B, the objects are the credit limit and the freeze notice, the state is "without approval" and "not yet taken effect," and the effective scope is the payment term and shipment conditions set in the agreement. The draft sentence and the source text take opposite values on the field "whether approval has been obtained," which is a mutually exclusive value on the same subject, the same period, and the same field.
Configuration and implementation checks
The table below lists, around the sample above, officially documented settings or implementation check items relevant to this feature.
| Configuration item | Setting or check content | Relation to this page's sample |
|---|---|---|
| Search mode | Semantic search, full-text search, or hybrid search | The sample contains exact values such as "RMB 800,000" and "sixty days"; hybrid search covers both semantics and keywords |
| Minimum relevance | A 0-1 value, effective only in semantic search or with result reranking | The hypothetical trial starting value here is 0.3, because the sample question has a clear subject; compare with 0.5 to observe changes in recall scope |
| Reference limit | The maximum tokens referenced per search | The sample text is short; confirm the reference limit can hold the complete clause without truncating the conditional sentence |
| Question optimization | Coreference resolution and question expansion before retrieval | If the sample question contains references such as "this agreement," enabling it can complete the subject |
| Result reranking | A ReRank model performs a second ordering | When the sample has many candidate clauses, the rerank score can be used for filtering |
| Index status | Confirm whether actual training and indexing were updated after a material change | After a version change of the credit agreement, check whether the new clause has generated an index |
Three common errors
- Treating "without approval" as "approved." The sample source text states that shipment above the credit limit requires Party A's written approval, while the draft sentence states that approval has been obtained. The two take mutually exclusive values on the same field. Inferring approval status from the appearance of the words "freeze notice" treats unstated information as stated.
- Treating a successful upload as an updated index. After material is uploaded to the knowledge base, training and index generation need separate confirmation. If the credit agreement in the sample changes version, the old index may still point to the old clause, and retrieval returns outdated content.
- Treating a retrieval result as an authorization basis. Retrieving a credit agreement clause does not mean the distributor currently has approval or that the freeze notice has taken effect. Approval and effectiveness are business facts that require manual verification or confirmation in a downstream system; the retrieval step does not perform authorization determination.
How to verify the setup
- Input the sample source text and the draft sentence, and compare the field "whether approval has been obtained." If the retrieved clause contains "requires Party A's written approval" and no approval record appears, the draft sentence's statement of approval does not hold and should be marked as pending verification.
- Input the exact values "RMB 800,000" and "sixty days" from the sample, using full-text search or hybrid search, and observe whether the returned result contains the complete clause. If the returned content omits the payment term or the credit limit condition, chunking has separated the condition from its constraint, and segmentation needs adjustment.
- Input a paper query that clearly is not in the library, for example an empty reference list
[], and check how the downstream design handles an empty result. If the system returns an empty result, record it as an observed phenomenon; if it returns unrelated content, record it as pending investigation without inferring a specific cause.
All three verification items above are based on the hypothetical paper exercise sample. They were not actually executed and only describe the expected comparison method and observed phenomena.