What this category of data looks like
Data sources for this category cover online composition analysis systems, DCS control systems, laboratory testing equipment, kiln and grinding operation equipment, raw fuel ledgers, and energy metering devices. Update frequencies are divided into three categories: real-time (such as kiln temperature, raw material composition), shift-based (such as inspection records, daily energy consumption reports), and daily (such as quality control notices, strength test reports). Daily documents include raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection forms, and energy reports. Fields cover raw material component parameters, thermal operation parameters, quality inspection indicators, and energy consumption data, using industry-standard industrial parameter units.
What constraints these characteristics impose on the knowledge base retrieval and recall link
This category has scattered data sources and significant differences in update frequencies. The retrieval link must support mixed recall of multi-source data, while distinguishing the synchronization frequency of real-time stream data and offline documents. There are various types of daily documents, including structured test reports, semi-structured inspection records, and unstructured operation logs. Retrieval logic must be adapted to chunking and matching for different document types. Industry parameters require precise matching of field names and units. Fuzzy recall can easily lead to deviations in process adjustments. Exception report drafting is tied to production stability, and the accuracy of recall results directly affects decision-making reliability. The recall scope and matching accuracy must be strictly constrained.
How to set the configuration
This configuration is adapted for FastGPT v4.17.0. Specific parameters are listed below:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the accuracy requirements for matching cement clinker industrial parameters, avoids mixing irrelevant process data with low matching scores into results |
limit | Top 8–12 results | Covers necessary reference data for core scenarios such as raw material batching and kiln operation, while controlling context length |
searchMode | mixedRecall | Adapts to mixed recall of structured parameters and unstructured text, meeting different retrieval needs for batching plans and operation records |
usingReRank | Enabled | Re-ranks initial recall results, corrects local deviations from vector retrieval, and improves the precision matching required for exception reports |
datasetSearchUsingExtensionQuery | Enabled | Expands industrial synonyms for retrieval terms, such as matching "grinding fineness" with "specific surface area" and other industry terms, adapting to the terminology habits of process documents |
collectionIds | Department-divided collection IDs | Distinguishes exclusive knowledge bases for departments such as mining, raw material preparation, calcination, and grinding, avoiding interference from cross-department irrelevant data in retrieval |
Three common misconfigurations
- Symptom: After uploading process drawing images or PDFs containing embedded images, retrieval results do not extract parameter annotations from the images. Cause: No image parsing model was bound in FastGPT v4.17.0, or image files were not uploaded to the knowledge base collection for the corresponding business department.
- Symptom: After exporting the knowledge base from an old server to an Excel file and importing it to a new server, some parameter fields of batching plans are missing. Cause: The chunking metadata of the original knowledge base was not retained during Excel import, or the field mapping rules of the corresponding dataset were not matched.
- Symptom: Searching for "kiln skin scaling treatment" returns a large number of irrelevant glass kiln operation records. Cause: The
collectionIdsparameter was not used to limit the knowledge base collection exclusive to cement clinker, resulting in cross-category data being mixed into retrieval results.
How to verify the configuration is correct
- Retrieve the core parameters of raw material batching plans, and check whether the document sources of the recall results are limited to the specified department knowledge base collections.
- Enter query terms containing industry-specific terminology, and confirm that the retrieval results cover the expanded synonym matching items.
- Upload a PDF file embedded with process parameters, and confirm that the parsed text can be normally retrieved and recalled.
- Check whether the data type of the global variable for knowledge base selection in the workflow meets the business configuration requirements, and matches the format rules of the dataset ID.
The values listed here are common starting points and should be evaluated against the reader's own samples.