What the data for this category looks like
Typical entities in this category are color printing, label, carton, flexible packaging, and pharmaceutical packaging processing enterprises. Daily business 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 sources include customer manuscripts, color management systems, printing press operation data, ink and substrate batch ledgers, spectrometer test results, and barcode detector readings. Data is updated per order, per batch, or each time a plate is changed. Document fields contain core business identifiers such as plate version, batch number, color parameters, and barcode content.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Data in this category has high update frequency, scattered sources, and diverse document types. Unlike the paper manufacturing category which only supplies base materials, this category requires multi-dimensional verification of content versions, colors, and packaging structures. This creates multiple constraints for the retrieval and recall process. High-frequency updates require retrieval results to prioritize the latest order, batch, or post-plate-change documents, to avoid calling outdated process or color matching records. Multiple document types include different fields such as text parameters, test values, and batch ledgers, so retrieval must support limiting search scope by business identifiers such as plate version and barcode number. Data from different sources has varying formats, so the system must support matching logic for both unstructured text and structured parameters, while adapting to flexible term matching needs in business scenarios.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches precise business identifiers such as plate version and barcode number for this category, balances recall scope and result relevance, and avoids missing critical first-piece sample approval or color matching records |
limit | Top 8–12 entries | Covers multiple document types under a single order, such as imposition sheets, process sheets, and inspection records, preventing a single context from exceeding model processing limits |
searchMode | mixedRecall | Adapts to this category's document types that have both text terms and numerical parameters, supports both keyword matching and semantic recall, covering text content on imposition sheets and color values from spectrometers |
usingReRank | Enabled, with weights calibrated via actual testing | Re-ranks recalled multi-type documents, prioritizing first-piece sample approvals and color matching records strongly relevant to inspection and audit scenarios, improving verification efficiency |
datasetSearchUsingExtensionQuery | Enabled | Expands matching scope of business queries, adapts to specialized terms in customer manuscripts and printing industry vocabulary, and supports pinyin matching requirements |
collectionIds | Filter by current order tags | Limits retrieval scope to document collections associated with the current order, avoiding invalid recall across batches and ensuring audit results only apply to current production tasks |
Three common configuration errors
- Retrieval results are not limited to documents under the specified order, resulting in cross-batch imposition sheets and process sheets. The cause is incorrect configuration of the
collectionIdsparameter, without binding to the current business's document collection. - Inputting pinyin abbreviations such as "YS" for "ink" fails to recall corresponding color matching records. The cause is that
datasetSearchUsingExtensionQueryis not enabled, and the pinyin matching extension logic is not activated. - Plugin validation fails with a MongoServerError. This error alone does not establish that MongoDB 4.4.29 is incompatible with FastGPT; inspect the complete logs, the database request issued by the plugin, and account permissions. The v4.17.2 deployment config documents 4.4.29 as a fallback for CPUs without AVX (see the official deployment configuration).
How to verify correct configuration
- A query including the current order's plate version and batch number is executed, and returned results are checked to confirm they only include documents associated with the current order.
- Pinyin term queries are tested, and confirmation is made that corresponding business documents can be recalled, such as inputting "youmo" to match ink color matching records.
- The knowledge base is exported and imported, and verification is made that different business collections maintain independent file structures and are not merged into a single file.
- After plugin validation, inspect the full error and service logs, confirm the MongoDB image version, whether the CPU supports AVX, and the plugin request/account permissions. Verify compatibility boundaries against the actual deployment instead of assuming one MongoDB version requirement for all FastGPT installations.
The values provided are common starting points and should be evaluated against samples appropriate to the specific deployment scenario.