What the Data for This Category Looks Like
The main entities in this category are color printing, label, carton, flexible packaging, and pharmaceutical packaging processing enterprises. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first-piece sample approvals, barcode inspection records, die-cutting and lamination records. Data comes from customer submissions, color management systems, printing press operation data, ink and substrate batch ledgers, spectrometer and barcode detector outputs. Data is updated per order, per production batch, or each time a printing plate is changed. Document fields include order number, plate version, color parameters, substrate model, batch number, and other fields. Some documents include numeric parameters and verification identifiers.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
Data in this category has a high update frequency and is associated with production batches. Knowledge base retrieval must prioritize recalling the latest version of documents, and avoid using expired plate, color matching, or inspection data. Document types include process sheets with numeric parameters, inspection records, and typesetting files. The field structures of different documents vary significantly. Targeted retrieval rules must be configured by document type to ensure content matching the corresponding business scenario is retrieved. Data sources involve outputs from multiple types of equipment and systems. Index construction must support heterogeneous format field mapping. Incorrect printing plates can lead to full batch scrap, so the accuracy of retrieval results must be strictly guaranteed. Matching rules for recall must be adjusted based on business scenarios.
How to Configure the Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category has high matching precision requirements for plate versions and color matching parameters. Low-similarity irrelevant documents must be filtered to avoid mismatched process data |
limit | Top 8 entries | Bid document and quotation preparation needs to cover multi-batch historical process data, while controlling context length to avoid exceeding model input limits |
searchMode | mixedRecall | Documents include text descriptions and structured fields such as order numbers and batch numbers. Hybrid retrieval can match both semantic meaning and precise business parameters |
usingReRank | Enabled | After multiple documents are recalled, they must be reordered by business relevance, prioritizing results matching plate versions and order fields, to improve the accuracy of bid content |
datasetSearchUsingExtensionQuery | Enabled | Bid scenarios require expanding query intent, adding keywords related to processes and batches to cover more historical business documents |
collectionIds | Bind three collections: pre-press processes, imposition sheets, and quotation history | Only call business documents corresponding to this category, to avoid irrelevant data interfering with retrieval results |
Three Common Mistakes
- Symptom: After uploading a Markdown document containing local image paths, images fail to load normally in retrieval results. Cause: Image resources were not uploaded synchronously to the knowledge base storage directory, or paths were not replaced with public links accessible to the knowledge base.
- Symptom: The retrieval configuration page only displays a single model option, and other text understanding models cannot be added. Cause: Multi-model retrieval permission was not enabled in the dataset management page, and only the default retrieval model was enabled.
- Symptom: Imported documents are not grouped according to custom business rules, and retrieval returns process data from irrelevant batches. Cause: Exclusive collections were not created according to business scenarios, and non-bid-related documents were not filtered during index construction.
How to Confirm the Configuration Is Correct
- Upload one test document containing plate version and batch number fields, execute retrieval queries for the corresponding business, and check whether the returned results include the key fields of this document.
- Check the
similarityparameter value in the retrieval configuration, confirm that it matches the precision requirements of this category, and adjust the range through multiple rounds of testing. - View the sorting of retrieval results, confirm that after
usingReRankis enabled, results are displayed first according to business relevance. - Verify the incremental update function, upload a process sheet document for a new batch, and confirm that the latest version of the content can be recalled during retrieval.
The values provided are common starting points and should be measured against the reader's own samples.