What the documents look like
Group-buy leader operations organize records around community nodes. The backend holds leader agreements, commission tables, pickup lists, and community announcements, while order details and after-sales records are produced per day and per group. Procurement and supplier materials sit upstream of this chain: supplier quotations, supply scope, purchase quantities, and arrival records need to correspond with the pickup lists that leaders receive. The same supplier's quotation may change by batch, so index content needs to retain date and batch scope. Without that scope, an old quotation retrieved during the current procurement stage can be mistaken for the active one.
Leader agreements, commission tables, pickup lists, and community announcements are different document types with clearly different subjects and fields. An agreement states signing parties and validity period, a commission table states rates and settlement cycles, a pickup list states items and quantities, and an announcement states notice items. Vector models and indexes handle retrieval positioning of these materials. They do not perform signing, approval, or settlement.
Constraints at the vector indexes step
A knowledge base consists of library, collection, and data. The smallest search unit is the library; a collection only classifies data and does not affect search results. One library should therefore use one vector model, and the knowledge base search node can only associate knowledge bases that use the same vector model. Libraries with different vector models cannot be associated together. During review, check the model identifier first, exclude non-matching libraries, and record the actual observation. Do not infer runtime behavior for the excluded libraries.
One original data record can correspond to multiple indexes, and the indexes field records an array of vector IDs. More focused index content usually raises retrieval precision while narrowing retrieval range. For supplier materials, quotation sheets, supply scope, and procurement batches can each have separate indexes sharing the same original data block. Image retrieval depends on whether an image description or image vector index was generated at ingestion and on the query-side model capability. Both conditions require review.
Retrieval results pass through semantic recall, full-text recall, RRF fusion, reranking, and similarity filtering before reference content is returned. Reference length can be 0. Minimum relevance is a filtering threshold that applies only during semantic retrieval or result reranking. It does not isolate supplier qualifications or leader permissions. Retrieval itself does not constitute authorization. Forms collect values; business verification, approval, database writes, and external synchronization each require their own implementation.
Hypothetical paper exercise sample, not a real record: Material A is a supplier quotation whose text reads "This quotation applies from March 1, 2025 to March 31, 2025, item category is leafy vegetables, tax-inclusive unit prices are in the attached table." Material B is a procurement list for the same period whose text reads "On March 5, 200 units of leafy vegetables were ordered from this supplier; 180 units arrived." The draft sentence to be assessed is "The supplier supplied leafy vegetables at the quotation price for the full month of March, and the purchase quantity follows the list." The fields required for assessment are quotation validity period, item category, purchase date, and quantity, all within the same period.
Configuration and implementation checks
| Configuration item | Value or check for this page | Basis and explanation |
|---|---|---|
| Associated knowledge bases | Associate only the procurement and supplier materials library that uses the same vector model | The node requires the same vector model |
| Search mode | Hypothetical starting value for this exercise: hybrid retrieval | Item numbers, item names, and natural-language questions coexist; compare against pure semantic retrieval after choosing this starting point |
| Minimum relevance | Hypothetical starting value for this exercise: 0.3 | This value applies only during semantic retrieval or reranking; compare with the original reference text before adjusting |
| Reference limit | Measured in tokens, set according to actual sample length | The documentation controls reference limit by tokens, not by top k |
| Index content | Separate indexes for quotation sheet, supply scope, and procurement batch, sharing original data | Multi-vector mapping; verify index and original text separately |
| Image index | Check whether an image description or image vector index was generated at ingestion | Image retrieval depends on ingestion index and model capability |
Three common errors
- Associating knowledge bases that use different vector models. Input is the model identifier of two libraries. Compare whether the two identifiers match. The observable result is that libraries with different model identifiers are excluded, and no cross-model retrieval is executed to compare recall differences.
- Using minimum relevance as a substitute for supplier or leader permission filtering. Input is a request from an unauthorized leader to view another leader's commission. Compare the returned reference content with the requesting subject. The observable result is that the relevance setting does not change the accessible range; permission requires a separate implementation.
- Treating a retrieval hit as signing or supply confirmation. Input is Material A and Material B. Compare quotation validity period, item category, and purchase date. The observable result is that retrieving the quotation sheet only indicates the text exists; it does not indicate the contract is effective or supply is complete.
How to verify the setup
- Input the original text of Material A and the draft sentence. Compare the quotation validity period "March 1 to March 31" with the draft sentence's "full month of March." Both refer to the same period and the same field with consistent values, so the condition is met. If the draft sentence said "April," the same field would hold mutually exclusive values and would be judged contradictory.
- Input the original text of Material B and the draft sentence. Compare the purchase quantity "200 units" with the draft sentence's "follows the list." The draft sentence does not give a specific quantity and is unsupported; the quantity field must be supplied before assessment.
- Input an empty-result query. Compare the returned reference content with an empty array. Observe whether the downstream design accepts a length-0 path. Do not infer from this that unrelated queries necessarily return an empty array.
- Input a query containing an image. Compare whether a vision model or an image-supporting vector model is available, and whether an image index was generated at ingestion. If any condition is unmet, the image path does not hold; record the actual observation.
This page addresses only group-buy leader operations, procurement and supplier materials, and vector models and indexes. During operational handover, which materials, operations, and requests sit at which authorization state cannot be treated as authorized by retrieval or by a form.