What the documents look like
Cross-border material for fruit-store procurement centers on purchase orders, acceptance records, pesticide-residue test reports and loss records. Wholesale market prices and supplier quotes move quickly, and the brief gives two update rhythms: daily and per batch. Grade acceptance depends on on-site judgment, so the same batch of fruit can carry different wording across supplier documents in different languages. Pesticide-residue reports usually contain a report number and item names, and these precise fields suit the full-text retrieval path. The vector model and index step has to answer whether the same batch of material can be recalled correctly in a multilingual setting, and whether the indexed content preserves subject, object and validity scope.
Constraints at the vector indexes step
Knowledge bases linked for retrieval must use the same vector model. Check the actual model identifier and the input type first. One source data block can be associated with multiple indexes, and the index content and the original content are checked separately. Build index content from complete and focused information; retrieval coverage and index length are weighed against sample material. Image retrieval also depends on whether ingestion generated an image description or an image vector. Uploading an image on the query side is not sufficient on its own. Semantic retrieval relies on vector distance, and cross-language understanding is one documented advantage; accuracy is affected by model training quality and sentence completeness. Hybrid retrieval fuses semantic and full-text results through RRF, and RRF is a fusion process rather than a separate switch. The minimum relevance value takes effect only in semantic retrieval or when result re-ranking is used. It is a filter threshold and does not perform permission or entity isolation. The reference limit is measured in tokens, which differs from top k.
Hypothetical paper exercise
The following is a paper exercise used to explain the checking method. It does not represent any real record or executed operation.
Supplier document text (hypothetical): product name "Red Fuji apple", grade "Grade 1", origin "Shandong", batch number "SD-2401", test report number "JC-2401-087", report date "2024-01-18", test item "chlorpyrifos", result "not detected". Purchase order date "2024-01-19", quantity "200 cartons".
Draft sentence to be judged (hypothetical): "This batch of Red Fuji apples has passed pesticide-residue testing and can be scheduled for shelf sale."
Checking points: the source text provides the test report number and the item result. It does not provide shelf authorization, an acceptance conclusion or a sales permit. The phrase "can be scheduled for shelf sale" in the draft sentence is a business authorization action, and the source text does not state it. The retrieval step can only confirm whether material is recalled; it does not replace an authorization decision.
Configuration and implementation checks
| Configuration or check item | How it is set | Relation to this page's sample |
|---|---|---|
| Vector model consistency | Check the actual vector model identifier of the linked knowledge bases | Multilingual supplier documents must be indexed under the same model |
| Index content | Build multiple indexes for the same source data block | Batch number and test item can each have an index |
| Image index | Check whether ingestion generated an image description or image vector | The hypothetical sample has no image input, so this item remains to be verified |
| Search mode | Semantic retrieval, full-text retrieval or hybrid retrieval | Batch number and report number suit the full-text path |
| Minimum relevance | A 0-1 value, effective only in semantic retrieval or re-ranking | This hypothetical starting value is 0.3, chosen to retain number-type hits, compared with 0.5 to observe recall changes |
| Reference limit | A reference-content limit measured in tokens | This hypothetical starting value is 2000 tokens, compared with 4000 to observe reference length |
Three common errors
- Treating retrieval recall as authorization execution. In the hypothetical sample the draft sentence says "can be scheduled for shelf sale", while the source text gives only the test result and no shelf permit. The input is the draft sentence and the source text; the comparison is whether an authorization field exists; the observable result is that the source text lacks an authorization statement.
- Linking knowledge bases across different vector models. The input is the model identifiers of the two knowledge bases; the comparison is whether the identifiers match; the observable result is that different identifiers cannot be linked together, and the actual observation is recorded without inferring run-time behavior.
- Treating minimum relevance as a permission filter. The input is the minimum relevance value and the query subject; the comparison is whether that setting involves a subject field; the observable result is that the setting only filters low-relevance results and does not distinguish business entities.
How to verify the setup
- Input the batch number "SD-2401" from the hypothetical sample, compare whether the full-text retrieval path recalls the corresponding source data block, and observe whether the reference content contains that batch number from the source text.
- Input the draft sentence "This batch of Red Fuji apples has passed pesticide-residue testing and can be scheduled for shelf sale" from the hypothetical sample, compare whether the reference content contains a shelf-authorization statement, and observe that the source text does not state authorization, so the sentence is not supported.
- Input a query with no corresponding material, compare the length of the reference-content array, and observe that a length of 0 is allowed and the empty-result path completes. Do not claim that a fictional rule necessarily returns an empty array.