What the documents look like
Sample inspection for livestream selection happens before a broadcast. The working question is whether a product can be sold and whether it suits on-camera display. The materials behind that question come from supplier files, testing-agency reports, platform negative lists, and historical return rates. On a daily basis the team handles sample evaluation forms, quality inspection reports, supplier qualifications, and prohibited-sale lists. Operating reports and anomaly reviews pull the conclusions of those documents together by week and by product, and trace a given anomaly back to a specific material, a specific sample, and a specific stage. Retrieval at this step turns scattered documents into quoted content that the report or review can cite. It does not decide authorization.
Two features of this category shape the retrieval step. First, the documents carry different states rather than a single pass or fail, so a retrieved sample evaluation form marked for re-check is not the same as an approved product. Second, the same sample appears across several materials, so a review has to compare the same subject, the same period, and the same field before calling anything contradictory. A missing report number is an evidence gap, and absence from a prohibited-sale list is not an approval to broadcast.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Search modes are semantic retrieval, full-text retrieval, and hybrid retrieval; hybrid retrieval merges paths through RRF, and ReRank re-orders candidate text. The citation limit is measured in tokens. The minimum relevance value applies only under semantic retrieval or when result re-ranking is used. Question optimization handles coreference resolution and question expansion and adds one model call. Quoted content is output as an array whose length can be zero, meaning the output path still runs when nothing is found. Retrieved content represents recall only; it does not represent authorization, approval, or a database write.
Hypothetical paper exercise: a selection manager records in a paper ledger "Sample A-0715, Supplier A, submitted for testing on 15 July 2026, testing-agency report number to be supplied, sample evaluation form conclusion pending re-check, not listed on the prohibited-sale list." The draft sentence to be judged is "Sample A-0715 has completed quality inspection and is approved for broadcast." Checking that draft sentence requires comparing the same subject, the same period, and the same field across the sample evaluation form, the quality inspection report, and the prohibited-sale list. The evaluation form conclusion is pending re-check, which is unsupported; the missing testing-agency report number is an evidence gap; absence from the prohibited-sale list does not mean approval for broadcast.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | One sample-inspection document base | It shares a vector model with the sample evaluation form, quality inspection report, supplier qualifications, and prohibited-sale list; verify the identifier before associating |
| Search mode | Run semantic retrieval and full-text retrieval separately | Compare what each mode recalls against the test question; do not assume one mode suits every material |
| Citation limit | Set after comparing actual material lengths | Unit is tokens; test with materials of different lengths to see whether content stays complete, then adjust |
| Minimum relevance | Label relevant and irrelevant samples first, then tune the threshold | This value applies only under semantic retrieval or when result re-ranking is used |
| Question optimization | Enable once for a question containing a reference | Compare the original question with the optimized question and check whether the subject was altered |
| Result re-ranking | Enable per setting when candidates are numerous | Requires a re-ranking model; the re-ranking score is used for filtering |
Three common errors
- Treating retrieval recall as an authorization act. The sample evaluation form conclusion is pending re-check; retrieving that form does not mean the product is approved for broadcast, and it does not mean inspection is complete.
- Associating knowledge bases that use different vector models together. Bases with different vector models cannot be associated; verify the identifier first, exclude those that do not match, and record the actual observation without inferring run results.
- Treating minimum relevance as a permission or entity filter. That value is a filtering threshold; it does not exclude unrelated businesses or unauthorized records, and retrieval does not carry authorization judgment.
How to verify the setup
- Input the hypothetical sample text "Sample A-0715, Supplier A, submitted for testing on 15 July 2026, testing-agency report number to be supplied, sample evaluation form conclusion pending re-check, not listed on the prohibited-sale list," and compare it with the draft sentence "Sample A-0715 has completed quality inspection and is approved for broadcast." The expected observation is that the draft sentence conflicts with the conclusion field of the sample evaluation form, so it is judged unsupported, and the missing report number is recorded as an evidence gap.
- Input the paper empty-result array [] and check whether the downstream design accepts an empty citation. The expected observation is that the output path runs through; do not claim that a fictional rule search necessarily returns [].
- Input two knowledge base identifiers that use different vector models and compare whether the identifiers match. The expected observation is that the non-matching one is excluded and only the actual observation is recorded; do not run a cross-model search to compare missed recalls.