What the documents look like
A brand-run livestream carries its own scripts, inventory, and after-sales work. Product material therefore has to serve the host's spoken lines, the floor controller's product sequencing, and the ad buyer's selection questions at the same time. The four document types named for this category are the livestream script, the product explanation card, the shift schedule, and the livestream review. These update per session or hourly. When the explanation card and the review disagree on a price or a gift bundle, selection questions can repeat an old price or an old gift to viewers, which produces false-advertising complaints and oversold inventory.
Product material and selection questions answer things like which item is the main push this session, which item fits a given price band, and which explanation card belongs to which sequencing slot. These questions mix natural-language description with exact fields such as model numbers, specification codes, and gift combinations. The vector model converts material and questions into vectors and computes distance. The index determines how an original data block is recalled. Selection questions for a brand-run livestream therefore cannot rely on semantic closeness alone; they also need to hit code-type fields.
Constraints at the vector indexes step
Knowledge bases linked for retrieval must use the same vector model. Check the actual model and the input type first. One original data block can be associated with multiple indexes, and index content is checked separately from the original text. Index content is built from complete and focused information; retrieval coverage and index length are weighed against samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, so the query side alone is not enough. After a model or index change, compare cited original text using the same set of questions, and do not promise an outcome from a vector score alone.
Retrieval is a recall process and does not perform permission judgment. Semantic recall and relevance scores do not carry permission or entity isolation. The minimum relevance value is a filtering threshold, not a final ranking rule. Knowledge bases with different vector models cannot be linked together; check identifiers, exclude non-matching bases, and record what is observed.
Hypothetical drill sample: a brand livestream team's product explanation card states in its original text, "Model A air fryer, session sequencing number 03, final price follows the livestream overlay, gift is one silicone mat." The shift schedule states, "Position 03 is presented by host A; floor controller B is responsible for price changes." A draft sentence to be judged reads, "The Model A air fryer has a fixed final price this session, and the gift is one silicone mat." The original text only says the final price follows the overlay; the draft turns the price into a fixed value, which goes beyond the original text. The gift item matches the explanation card. This sample is for checking only and does not represent any real session.
Configuration and implementation checks
| Configuration or check item | Value or action relevant to this page | Explanation and comparison method |
|---|---|---|
| Vector model of linked knowledge bases | For this hypothetical trial, the starting point is one shared vector model | Check each base identifier first, exclude bases with a different model, and do not run cross-model retrieval |
| Original data and indexes | One original data block can be associated with multiple indexes | Check indexes and the explanation card original text separately; do not treat index content as the original text |
| Index content length | For this hypothetical trial, the starting point is a focused fragment | This starting point is chosen because explanation card fields are short; compare recall coverage against a full-paragraph sample |
| Search mode | Semantic search, full-text search, or hybrid search | Use full-text or hybrid for model-number and sequence-number questions; use semantic search for natural-language phrasing |
| Minimum relevance | For this hypothetical trial, the starting point is a medium threshold | This starting point is chosen because the sample question is clear; compare filtered results against a higher and a lower threshold |
| Citation limit | Control cited content volume by tokens | Compare against returned original text item by item to confirm no key field is truncated |
Result reranking requires a model and must be enabled in settings. Hybrid search merges multiple result paths through RRF; RRF is a merging process and is not a standalone switch. Question optimization adds one model call before retrieval.
Three common errors
- Treating index content as the original text. If the explanation card index says "fixed final price" while the original text says "final price follows the overlay," checking only the index turns an out-of-scope statement into a supported one.
- Treating a retrieval result as permission or entity isolation. Semantic recall and relevance scores do not carry permission; a retrieved record does not mean the current account is entitled to use that record.
- Treating a pending-review state as granted authorization. The shift schedule line "floor controller B is responsible for price changes" is a division-of-labor record. Whether a price change has been authorized requires separate authorization material. Pending review does not demonstrate a lack of operating permission, and it does not demonstrate approval.
How to verify the setup
- Input the explanation card original text and the draft sentence "The Model A air fryer has a fixed final price this session, and the gift is one silicone mat." Compare the price field value in the draft against the original. The original only states that the final price follows the overlay, while the draft writes a fixed value. That sentence is not supported by the original and should be recorded as pending verification.
- Input a model-number or sequence-number question and compare the cited original text returned by semantic search against that returned by full-text search. If semantic search misses the sequence-number field while full-text search hits it, record that difference and do not conclude that one mode necessarily fails.
- Input a paper citation result explicitly set to empty, and check whether the downstream chain continues with an array of length 0. An empty result can be used as a paper input to check downstream design; this does not claim that a real retrieval will return an empty array.