What the data for this category looks like
This category of data covers the mine, raw material, calcination, grinding, quality control, and energy departments of cement enterprises. Daily documents include raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data sources include online composition analysis systems, DCS control systems, laboratory equipment, kiln and grinding operation devices, raw and fuel ledgers, and energy metering devices. Update frequencies are divided into three categories: real-time, per shift, and daily. Data fields include raw material mixing ratio, calcination temperature, grinding fineness, energy consumption values, and more. Corresponding units include degrees Celsius, kilograms, kilowatt-hours, and more.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Multi-source data with varying update frequencies requires retrieval to adapt to different timeliness priorities. Real-time collected kiln and grinding operation data must be recalled first. Daily aggregated energy reports can have their indexes updated on a fixed schedule. Multi-department, multi-type documents need separate recall weights. Core production documents such as raw material batching plans and kiln operation logs must have higher matching priority. Specificity of fields and units requires binding corresponding business tags during retrieval to avoid mismatching cross-category data. Batch data updates must support incremental recall configuration to avoid excessive resource usage from full index rebuilding. The continuous calcination and grinding business characteristics of this category require recall results to cover associated documents such as thermal control systems and batching adjustments, to support logical completeness of ledger summary.
How to configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Adapt to the field specificity of production data in this category, filter low-match non-production documents |
limit | Top 8 entries | Cover core documents from multiple departments including raw material, calcination, and grinding, avoid context overflow caused by too many single-round recall results |
searchMode | embedding | Prioritize matching semantic features of production data, adapt to retrieval needs of unstructured documents such as batching and thermal control systems |
usingReRank | Enabled | Perform secondary sorting on recall results, improve display priority of core production documents, adapt to business logic of ledger summary |
datasetSearchUsingExtensionQuery | Enabled | Expand retrieval terms related to production scenarios based on original questions, cover business scenarios such as raw and fuel fluctuations and kiln crust treatment |
collectionIds | Bind corresponding collection IDs by department | Separate exclusive data for departments including mine, raw material, calcination, and grinding, avoid mixing cross-department documents |
Three common errors
- Symptom: The retrieval interface returns empty JSON, or the interface displays no matching results. Cause: The
collectionIdsconfiguration is not correctly bound to the knowledge base collection of the corresponding department, resulting in the retrieval scope not covering cement clinker and grinding ledger data. - Symptom: Using raw HTTP response data as a knowledge base reference for upload results in no matching retrieval effects. Cause: The response data is not converted to the structured format required by FastGPT, and no business tags or corresponding field descriptions are added, so the system cannot identify valid retrieval content.
- Symptom: Retrieval results do not cover documents related to adjusting batching due to raw and fuel fluctuations. Cause: The
datasetSearchUsingExtensionQueryconfiguration is not enabled, so business-related retrieval terms cannot be expanded, resulting in limited matching scope.
How to confirm the configuration is complete
- Enter the knowledge base management interface of FastGPT v4.17.0, check whether the collection corresponding to
collectionIdsincludes core documents such as raw material batching plans and kiln operation logs. - Submit test questions including "adjust raw material ratio" and "rotary kiln thermal control system", verify whether the
similarityscore of the recall results falls within the preset range. - After enabling the
usingReRankconfiguration, confirm that core production documents are ranked in the top 3 of the recall results. - After uploading structured equipment inspection data, confirm that the data can be normally recalled through retrieval testing.
The values provided here are common starting points, and should be evaluated against the reader's own samples.