What the documents look like
Group-buy presale order collection starts with orders and then organizes purchasing and fulfilment. The working documents are the presale product table, the order-collection result, the purchasing plan and the cut-off notice. These update daily or per session, and their fields commonly include product name, specification, supplier, cut-off time, group-formation quantity and sorting wave. In cross-border material and multilingual retrieval, one product may appear under Chinese, English or Southeast Asian names, and the supplier stock sheet and the purchasing plan may be submitted by different parties at different times. Retrieval has to turn this multilingual material into vectors and build indexes so that operations staff can check product information, supplier constraints and fulfilment requirements around the cut-off.
The same knowledge base must use the same vector model for the knowledge bases that are associated for retrieval, so the actual model identifier and input type are checked first. One source data block can be linked to several indexes, and the index content and the source content are checked separately. Index content is built from complete and focused information, and retrieval coverage against index length is weighed against samples. Image retrieval also depends on whether an image description or image vector was generated at ingestion, so the query side alone is not sufficient. After a model or index change, the same set of questions is compared against the cited source text, and vector scores alone do not support a claim about effect.
Constraints at the vector indexes step
Hypothetical exercise sample. A community group-buy platform is working on cross-border presale order collection. An operations staff member plans to place one English-language supplier submission and one internal Chinese purchasing plan into the same knowledge base for retrieval. The supplier source text reads: "Supplier A confirms frozen shrimp 500g packs, minimum order 200 cartons, delivery window 10–12 May." The platform purchasing plan source text reads: "Purchasing plan: frozen shrimp 500g packs, minimum order 200 cartons, delivery window 10 May to 12 May." The draft sentence to be judged reads: "The supplier has confirmed the full purchase quantity and agreed to delivery." Here the subject is Supplier A, the object is frozen shrimp 500g packs, the state is "confirmed minimum order quantity and delivery window", and the effective range is 10–12 May. The draft sentence widens "confirmed minimum order quantity and delivery window" into "confirmed the full purchase quantity and agreed to delivery", so the source text has to be checked for whether it supports that conclusion.
The knowledge bases associated for retrieval must use the same vector model, and the actual model identifier and input type are checked first. A source data block can be linked to multiple indexes, and index content is checked separately from source content. Index content is built from complete and focused information; retrieval coverage and index length are weighed against samples. For image material, whether an image description or image vector was generated at ingestion is confirmed, because image retrieval depends on the ingestion index rather than only on whether an image was uploaded at query time. Semantic retrieval supports cross-language understanding, and hybrid retrieval merges semantic and full-text results through RRF. Reference limits are set in tokens, and the minimum relevance value is a 0–1 number that applies only in semantic retrieval or with result reranking. A retrieval result is not authorization or approval; retrieval does not perform permission or entity isolation, and a pending review state does not show that an operation permission is absent.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Vector model | One model identifier used inside the same knowledge base | The documented requirement is that knowledge bases associated for retrieval use the same vector model; the actual model and input type are checked first |
| Index content | A complete and focused index built for each piece of material | Index content and source text are checked separately; retrieval coverage and index length are weighed against samples |
| Image index | If the material contains images, confirm whether an image description or image vector was generated at ingestion | Image retrieval depends on the ingestion index, not only on whether an image was uploaded at query time |
| Search mode | Semantic retrieval or hybrid retrieval for multilingual material | Semantic retrieval supports cross-language understanding; hybrid retrieval merges semantic and full-text results through RRF |
| Reference limit | A trial starting point set in tokens | The documented control is in tokens rather than top k, because chunk lengths differ widely |
| Minimum relevance | A trial starting point set as a 0–1 value | This value applies only in semantic retrieval or with result reranking, and filters low-relevance results |
Three common errors
- Associating knowledge bases that use different vector models. The input is the model identifiers of the two knowledge bases to be associated. The comparison is whether the two identifiers are the same. If the identifiers differ, the non-matching one is excluded, and no cross-model retrieval is run to compare missed recalls.
- Treating "confirmed the full purchase quantity and agreed to delivery" in the draft sentence as a fact already supported by the source text. The input is the supplier source text and the purchasing plan source text. The comparison is whether the draft sentence goes beyond the subject, object, state and effective range recorded in the source text. The source text records only the minimum order quantity and the delivery window, and does not record the full purchase quantity or agreement to delivery.
- Treating a retrieval result or a similarity score as a basis for authorization or approval. The input is the cited content returned by retrieval. The comparison is whether it contains an authorizing party, an authorization scope and an effective condition. Retrieval does not perform permission or entity isolation, and a pending review state does not show that an operation permission is absent.
How to verify the setup
- For the hypothetical exercise sample, input the English supplier source text and the Chinese platform purchasing plan, and compare the draft sentence "The supplier has confirmed the full purchase quantity and agreed to delivery" against the recorded "confirmed minimum order quantity and delivery window". If the draft sentence goes beyond the source range, it is judged unsupported, and the evidence gap is recorded.
- For the model identifier of the same knowledge base, input the vector model identifiers of the two knowledge bases to be associated, and compare whether the two are the same. If they are the same, the joint-association condition is met. If they differ, the non-matching one is excluded and the actual observation is recorded.
- For the image index, input the material containing images and the query-side image, and compare whether an image description or image vector was generated at ingestion. If none was generated, image retrieval is unavailable, and this is recorded as pending verification, without claiming that a fictional rule makes retrieval return an empty result.
A paper input of an empty array can be used to check the downstream design that receives zero references, since the reference output is an array whose length can be zero. Optional paths in the overall flow are not executed on every request: image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled by configuration. Knowledge bases that use different vector models cannot be associated together; the identifiers are checked, non-matching ones are excluded, and the actual observation is recorded without describing runtime outcomes. A judgement requires the same subject, the same period and the same field; only mutually exclusive values in the same field count as a contradiction. A field that is not recorded, or evidence that covers only a related stage, counts as unsupported or pending verification. A pending review does not show that an operation permission is absent; signing, reaching cooperation and contract effectiveness are three distinct facts. Missing evidence stays missing, and authorization or effectiveness clauses are not supplied to force a definite answer.