What the documents look like
Sample inspection for livestream selling happens before a broadcast. The decision is whether a product can be listed and whether it suits on-camera presentation, not whether a script reads well. Four document types carry that decision: sample evaluation forms, quality inspection reports, supplier qualification records, and prohibited-item lists. Sources are separate. Suppliers submit their own materials, testing bodies issue reports, and platform negative catalogs and historical return rates arrive through other channels. Updates move by week or by product. Selection managers, supply chain business development staff, and quality inspection specialists work across the same materials, and a parsing error in any one of them can lead to returns, a false advertising finding, or a platform penalty.
When these materials enter a knowledge base, two things need to survive chunking. The first is document identity and version that can be traced back to the source text. The second is the relationship between a condition, its explanation, and the content it constrains. An applicable batch on a sample evaluation form, a test item and conclusion in an inspection report, an expiry date in a supplier qualification record, a category scope in a prohibited-item list: once any of these is split apart during chunking, the retrieved fragment loses the qualifier needed to judge it.
Constraints at the document chunking step
A FastGPT knowledge base consists of library, collection, and data. The smallest search unit is the library; collections only classify data and do not affect search results. The dataset.datas table in MongoDB stores vector source data, and its indexes field records the corresponding vector IDs as an array, so one data group can map to multiple vectors. A vector field in PostgresSQL stores vectors. During retrieval, vectors are recalled first, then the vector IDs are used to find source content in MongoDB. When records resolve to the same data group, they are merged and the highest vector score is kept.
This structure means chunking has to serve two goals at once: semantic completeness inside one data group, and coverage of that same source data by multiple indexes. The four document types differ in structure, so segmentation should follow each type's own layout and keep a conditional clause with the conclusion it constrains. Semantic search relies on vector distance, full-text search relies on keyword hits, hybrid search merges both paths through RRF, and ReRank performs a second ordering. Identifiers, model numbers, and proper nouns suit full-text or hybrid search; natural-language questions suit semantic or hybrid search.
Hypothetical drill sample: a sample evaluation form reads "Sample name: folding electric cooker; submitted batch: B2405; evaluation conclusion: appearance and function meet listing requirements, pending confirmation after the quality inspection report is filed." One draft sentence awaiting review reads "This batch is confirmed ready for broadcast." The supplier qualification text supplied for comparison reads "Qualification name: food-contact material test report; covered object: folding electric cooker series; valid until: December 31, 2025." The handover request reads "Please confirm this batch can be broadcast and sync it to the selection list." Here the subject is batch B2405, the object is the folding electric cooker, the status is pending confirmation after the quality inspection report is filed, and the authorization boundary is that the evaluation conclusion has not turned into a broadcast confirmation.
Configuration and implementation checks
| Configuration or check item | Value or practice for this page | Basis and explanation |
|---|---|---|
| Search mode | Choose semantic, full-text, or hybrid search by question type | Official documentation describes the three modes; hybrid search merges through RRF |
| Reference limit | Measured in tokens, not top k | Official documentation notes large chunk length differences in mixed knowledge bases |
| Minimum relevance | A 0-1 value effective only under semantic search or result reranking | Official documentation describes it as a filter threshold, not a final ordering rule |
| Segmentation check | Inspect paragraphs by the structure of each document type | Official documentation states complete, single-purpose segments improve precision |
| Index check | Confirm multiple vectors exist for one source data group | Official documentation describes indexes as an array that can map to several vectors |
| Post-update review | Check actual training and index state after material changes | A successful upload does not mean retrieval has updated; manual review is required |
Hypothetical trial starting point for this page: set minimum relevance to 0.3 because the sample question is a natural-language phrasing that needs some recall range. The comparison object is the returned content from the same batch of materials at 0.3 and at 0.5, observed for fragments where a conditional clause and its conclusion have been split apart.
Three common errors
- Treating an evaluation conclusion of "pending confirmation after the quality inspection report is filed" as a confirmed broadcast approval. The input is the sample source text and the draft sentence; the comparison is whether the draft sentence exceeds the source status; the observable result is that the draft sentence turns a pending state into a confirmed one.
- Treating a retrieval hit as authorized execution. The input is the handover request "Please confirm this batch can be broadcast and sync it to the selection list"; the comparison is whether the request carries a completed confirmation action; the observable result is that the request itself does not constitute a confirmation or a sync authorization.
- Treating a successful upload as an updated index. The input is the material change record and the retrieval result; the comparison is whether retrieval after the change returns new content; the observable result is that an upload record exists while retrieval still returns old fragments.
How to verify the setup
- Input the sample source text and the draft sentence; compare whether the draft sentence preserves the "pending confirmation after the quality inspection report is filed" status. The condition is met only when the draft sentence does not exceed the source status.
- Input the handover request; compare whether the request states a confirmation action and a sync authorization. The condition is met only when the request is explicitly marked as pending review and no sync has been executed.
- Input the material change record; compare the content returned by retrieval after the change with the content before the change. The condition is met only when the returned content matches the changed material.
An empty-result handoff can use the explicit paper input [] to check downstream design; it cannot be claimed that a fictional rule retrieval will return []. Libraries across different vector models cannot be associated together; verify identifiers first, exclude non-matching ones, and record the actual observation. Image retrieval requires a corresponding image input and model capability, and reranking requires a model enabled according to settings. Retrieval and similarity scores do not handle permission or entity isolation; business confirmation, approval, database writes, and external synchronization need separately connected system implementations.