What the documents look like
Livestream after-sales material concentrates after a single session of traffic. Refund requests, reshipment orders, and script templates appear together, and agents need to find prior handling positions across languages. The retrieval result directly affects whether explanations given to buyers stay consistent. Material updates after each session and daily, and the retrieval step reads order lists, refund requests, reshipment orders, and script templates that are already stored.
The category difference from ordinary platform after-sales work is that volume arrives in bursts tied to a single session and to what the host promised. The main data sources named in the brief are the shop after-sales system, customer service IM, warehouse WMS, and logistics tracking. Typical roles are livestream customer service, after-sales supervisors, and warehouse exception staff. Errors surface as concentrated negative reviews, platform intervention, and damaged creator cooperation. This page addresses cross-border material and multilingual retrieval at the vector model and index step, and does not construct business functions without a documented basis.
Constraints at the vector indexes step
FastGPT stores vector source data in the dataset.datas table in MongoDB, and an indexes field records the corresponding vector IDs as an array. One group of source data can map to multiple vectors. Apart from the default text index, image content can also produce an image description index or an image vector index when model capability supports it. The PostgreSQL PG Vector plugin acts as the vector retriever with HNSW as the index, and PostgreSQL serves vector retrieval only while MongoDB handles other data access. During retrieval, vectors are recalled first, then the vector ID is used to find source content in MongoDB; entries corresponding to the same source data are merged and the highest vector score is kept.
A dataset search node can select one or more knowledge bases that use the same vector model. Verifying the actual model and input type comes before association. Whether an image can be retrieved depends on whether an image description or image vector index was generated at ingestion time, not only on what is uploaded at query time. A vector score reflects similarity and does not carry permission or entity isolation.
Hypothetical paper exercise sample: an after-sales team prepares the source text of a "Cross-border refund script template" containing three sentences: "This template applies to refund explanations for livestream orders on the Southeast Asia site. The template status is pending review. The applicable period is seven days starting the day after a given livestream session ends." A separate draft sentence reads: "This template is already effective and can be used directly for reshipment order explanations." The task is to determine whether the draft sentence agrees with the source text.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | How to check |
|---|---|---|
| Vector model | One model that supports multilingual text is selected | Compare the model identifier against the knowledge base registration |
| Associated knowledge bases | Only knowledge bases with the same vector model are associated | Check each model identifier and exclude those that do not match |
| Index content | Indexes are built from complete, focused template sentences | Compare against the source text sentence by sentence and confirm subject and status |
| Image index | This sample contains no images | Record as not involved; do not infer image retrieval results |
| Reference limit | Set to a starting value for this exercise | Adjust after comparing with the actual token volume of the sample |
| Minimum relevance | Set to a starting value for this exercise | Takes effect only under semantic retrieval or reranking |
The reference limit is measured in tokens, and the documented reason for using tokens rather than top k is that chunk lengths vary widely in mixed knowledge bases. The minimum relevance value is a filter threshold and not a final ordering rule. When question optimization, hybrid retrieval, image retrieval, or reranking is enabled, final results may pass through multi-path recall fusion and may not be ordered strictly by a single vector similarity. RRF is a fusion step for combining multiple retrieval paths, and the documents describe it as a merge method rather than an on/off switch. The dataset search node outputs references as an array whose length can be zero, so the downstream path continues even when nothing is found.
Three common errors
- Treating "already effective" in the draft sentence as a fact from the source text. The source text states "pending review," and the two values are mutually exclusive in the same field, which makes them contradictory.
- Treating a retrieval score as an authorization basis. The score does not determine whether the template may be used for reshipment order explanations, and authorization status requires separate verification.
- Associating knowledge bases that use different vector models. When model identifiers do not match, they are excluded and cross-model retrieval is not performed.
How to verify the setup
- Input the three source sentences and the draft sentence above, then compare the template status field. The source text gives pending review and the draft gives already effective; a mutually exclusive value in the same field is judged contradictory.
- Input the source text and the question "what is the applicable period of this template," then compare whether the returned reference contains "seven days starting the day after a given livestream session ends." A reference containing that period satisfies the condition; a missing period is recorded as unsupported.
- Input an explicit paper input of an empty array and check whether the downstream design carries a reference path of length zero. Normal continuation downstream satisfies the condition, and no claim is made that a fictional rule retrieval necessarily returns empty.
Each check states what is entered, what is compared, and what observable result satisfies the condition. No test, deployment, approval, or business action is claimed to have occurred. Where the documents do not provide a field, the value remains unknown.