What the data for this industry looks like
Business data sources for pulp and paper manufacturing include pulp and waste paper batch archives, DCS/QCS systems, on-paper machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Daily generated documents cover pulp blending sheets, beating records, paper machine operation logs, online quality curves, laboratory test reports, and sheet break and product change records. Data update frequencies include real-time (online quality data), per shift (operation logs), and per roll (sheet break and product change records). Documents include professional fields such as pulp ratio, beating degree, grammage, moisture content, and thickness. Units include g/㎡, %, mm, tons, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
A high proportion of real-time data requires the knowledge base to use an incremental update frequency that matches the per-shift and per-roll business rhythm. This avoids recalling outdated batch data. Multiple types of structured and semi-structured documents coexist. Retrieval must support filtering by fields such as batch number and roll number to narrow the scope of invalid results. Quality issues in high-speed continuous web production involve linked links including pulp blending, beating, and paper machine operation. Recall results must cover full-chain documents to avoid missing key related items. Many professional terms are used, such as beating degree and refining degree. Semantic extended query support is required to cover synonyms and industry-specific expressions.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
similarity | 0.75–0.85 | Quality parameters in pulp and paper manufacturing are strongly correlated. A threshold that is too low will introduce irrelevant water treatment or energy records, while a threshold that is too high will miss key pulp blending and beating matching items |
limit | Top 8–12 entries | Quality issues in high-speed production involve multi-link related data. A sufficient number of recalled entries can cover full-chain records of pulp blending, beating, and paper machine operation |
searchMode | mixedRecall | The query examples combine domain terms with natural-language context; compare with representative data, not as a product default. |
usingReRank | Enabled | Semantic similarity of multiple types of documents such as online quality curves and laboratory reports requires secondary calibration to improve the matching accuracy of defect analysis |
datasetSearchUsingExtensionQuery | Enabled | There are many professional terms in pulp blending sheets and beating records. Extended queries can cover synonyms such as beating degree and refining degree, expanding the scope of retrieval and recall |
collectionIds | Bind three exclusive knowledge base collections for pulping, papermaking, and quality inspection | Documents are clearly categorized by different departments. Binding by business department can narrow the retrieval scope and reduce the proportion of irrelevant results |
Three common mistakes
- Phenomenon: Retrieval results only return a small number of single documents, and cannot cover full-chain related data of pulp blending and paper machine operation. Cause: The
limitparameter is set too low, failing to cover the demand for multi-link related records. - Phenomenon: Knowledge base entries cited in responses are not associated by batch, and invalid reference content across batches appears. Cause:
searchModeis not configured as batch-related retrieval mode, andcollectionIdsis not bound to the corresponding batch collection. - Phenomenon: The large model does not cite knowledge base content and directly generates generic responses. Cause:
datasetSearchUsingExtensionQueryis not enabled, leading to failure of professional term matching, or thesimilaritythreshold is set too high, filtering out all valid recall results.
How to verify the configuration is properly set
- Upload a single specified batch of pulp blending sheet and sheet break record, execute the query "A certain roll of paper has grammage deviation", and check that the recall results include the pulp blending parameters and sheet break time records of the corresponding batch.
- Adjust the
similarityparameter to 0.75, check the semantic matching degree of the retrieval results, and confirm that no obvious irrelevant water treatment records are mixed in. - After enabling
usingReRank, compare the sorting of retrieval results before and after enabling, and confirm that highly relevant laboratory reports are displayed upfront. - Check the FastGPT v4.17.0 knowledge base configuration panel, and confirm that
collectionIdshas been bound to the knowledge base collection of the corresponding business department.
The values provided are common starting points and should be measured against the reader's own samples.