What the Data for This Category Looks Like
Core users include material preparation, pulping, paper making, quality control and energy departments of pulp and paper enterprises. Daily generated documents include pulp mixing sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, sheet break and product change records. Data sources include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratory testing instruments, energy and water treatment systems. Data update frequencies include real-time, per shift, and per roll. Fields include batch identifiers, process parameters, quality indicators, equipment operation data, and professional metrological markings such as process parameter units and quality testing units.
Constraints on Knowledge Base Retrieval and Recall
Multi-source heterogeneous data sources and differentiated update frequencies require retrieval systems to support cross-data-source associated recall, while adapting to low-latency response needs for real-time data. Structural differences across document types require retrieval logic to balance matching rules for text records and curve data. Exclusive process data and quality indicators for each paper roll require retrieval results to strictly match the scope of the current audit batch, preventing cross-batch data from interfering with audit judgments. The high-speed continuous production characteristics require retrieval result return speed and sorting logic to adapt to the real-time requirements of on-site audits.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Adapt to the precision requirements of pulp and paper process parameters, and avoid recalling irrelevant batch data |
limit | Top 8 entries | Match the volume of single-batch process data, and avoid context overload affecting audit judgments |
searchMode | mixedRecall | Balance keyword matching and semantic recall, and adapt to mixed query scenarios of process parameters and quality curves |
usingReRank | Enabled | Rerank recall results by working condition relevance, and prioritize matching the real-time operating status of the current paper machine |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand process-related query terms, and cover different expressions of professional terms such as pulp mixing and beating |
collectionIds | Bind corresponding knowledge base collections by department | Distinguish exclusive data of material preparation, pulping and quality control departments, and avoid cross-department data interfering with current audit links |
Three Common Configuration Mistakes
- Phenomenon: The import option for CSV-format knowledge bases cannot be found in the retrieval interface. Cause: Support configuration for the corresponding file type is not enabled in dataset management.
- Phenomenon: Batch number variables cannot be correctly referenced in retrieval cards. Cause: The required variable placeholder format is not configured in the query template.
- Phenomenon: Beating records returned by retrieval do not match the current paper machine working condition. Cause:
collectionIdsis not set correctly, resulting in recall of cross-department historical batch data.
How to Verify Correct Configuration
- Upload test beating records and corresponding online quality curves for a single paper roll, initiate a query containing "current batch beating degree", and verify that the returned results include the test data.
- View retrieval logs, confirm that
usingReRankanddatasetSearchUsingExtensionQueryare active, and that result sorting meets expectations. - Check dataset binding relationships, confirm that
collectionIdsonly includes exclusive document collections of the current audit department, with no cross-department data mixed in. - Trigger an extended query test, enter the query "adjust pulp mixing ratio", and verify that the system automatically supplements relevant process terms.
The values provided are common starting points and should be measured against the reader's own samples.