What the data for this category looks like
Typical data for this category comes from color printing, label, carton, flexible packaging, and pharmaceutical packaging processing entities. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first article sign-off samples, barcode inspection, die-cutting and lamination records. Data sources cover customer manuscripts, color management systems, printing press operation data, ink and substrate batch ledgers, spectrophotometer test results, and barcode detector readings. Data updates occur per order, per batch, or each time a plate is changed. Individual documents include structured and unstructured fields such as plate version, color parameters, batch numbers, and inspection thresholds.
What constraints these characteristics impose on knowledge base retrieval and recall
This category has high data update frequency, with real-time updates per order, batch, or plate change. This requires knowledge base retrieval to support fast incremental synchronization and version matching. Multiple types of documents are stored together, covering pre-press files, process parameters, inspection data and other content in different formats. Targeted recall must be configured for exclusive fields such as plate version, color parameters, and barcode verification rules. Individual data includes structured numerical values and unstructured image text. The recall accuracy must balance semantic retrieval and precise field matching. In addition, data mismatches will directly lead to full batch scrap. This requires retrieval and recall results to have high confidence, to avoid batch losses caused by mismatched versions or parameters.
How to set configurations
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category requires precise matching of core audit fields such as plate version and color parameters. A threshold that is too low will introduce irrelevant results, while a threshold that is too high may miss valid content |
limit | Top 8–12 entries | The per-batch data volume for this category is moderate. Too many results will increase audit workload, while too few may fail to cover all relevant documents |
searchMode | mixedRecall | This category includes both semantic content such as pre-press files and structured parameters such as ink batches. Hybrid retrieval balances semantic and precise matching |
usingReRank | Enabled | This category has diverse document types. Reranking filters low-correlation results and improves the recall priority of core audit fields |
datasetSearchUsingExtensionQuery | Enabled | This category requires association with related documents such as customer manuscripts and process sheets. Extended queries cover cross-document related information |
collectionIds | Bind to the exclusive knowledge base collection for the corresponding category | This category isolates data by order or batch. Exclusive collections avoid interference from cross-category data |
Three common errors
- Symptom: Retrieval results are empty during conversations, but normal recall works in knowledge base backend tests. Cause:
datasetSearchUsingExtensionQueryis not configured, or thesimilaritythreshold is set too high. This leads to insufficient query expansion during real-time conversations, filtering out valid results. - Symptom: The query is unrelated to knowledge base content, but partial results are still returned. Cause:
searchModeis only configured as vector mode, and thesimilaritythreshold is set too low. This introduces semantically similar but business-unrelated content. - Symptom: The knowledge base cannot correctly read mathematical or physical formulas in documents. Cause: Exclusive text chunking rules are not configured for color formulas and barcode verification formulas in printing processes, leading to formula splitting and structural loss.
How to confirm configurations are properly set
- Upload one test document with plate version numbers and color parameters, bind it to the corresponding
collectionIds, initiate a query that includes the version number, and check if the recalled results include this document. - Adjust the
similaritythreshold to the range that meets business requirements, initiate a query with a vague description such as "red ink batch", and check if the relevance of the recalled results meets expectations. - Enable
usingReRankanddatasetSearchUsingExtensionQuery, initiate a cross-document associated query such as "October 2024 carton imposition sheet", and check if related process sheets and inspection records are recalled. - Upload a barcode verification document that includes mathematical formulas, and check if the formulas are fully displayed in the retrieval results.
The values provided are common starting points and should be measured against the reader's own samples.