What data for this category looks like
Data for this category comes from online composition analysis systems, DCS distributed control systems, laboratory terminals, kiln and grinding equipment sensors, raw fuel ledgers, and energy metering devices. Update frequencies fall into three categories: real-time, per shift, and daily. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Fields cover raw fuel composition parameters, equipment operation parameters, laboratory test values, and energy consumption metering data. Units include kilograms, cubic meters, degrees Celsius, hours, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
Data for this category is multi-source, heterogeneous, and has widely varying update frequencies. Knowledge base retrieval must support incremental synchronization across multiple data sources to avoid delays from full data pulls. Daily documents include both structured ledgers and unstructured records, so retrieval must adapt to mixed-format content matching. Field units cover multiple measurement standards, so recall processes must link parameter units during matching to avoid invalid results. Tender quotations must link batching plans under raw fuel fluctuations and kiln thermal system parameters. Retrieval must recall multi-dimensional associated data at the same time; single-field matching cannot cover business requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | This category has high parameter accuracy requirements. Filter low-match irrelevant data to avoid parameter deviations in tender plans |
limit | Top 8–12 entries | Tender documents need to cover multi-dimensional data such as batching, kiln temperature, and energy consumption. An appropriate number of recall results provides sufficient reference without exceeding context limits |
searchMode | mixedRecall | This category includes both structured ledger data and unstructured operation records. The hybrid mode balances precise matching and semantic association |
usingReRank | Enabled | Multi-dimensional recall results must be reordered by business relevance. This ensures plans matching current raw fuel conditions are prioritized during tendering |
datasetSearchUsingExtensionQuery | Enabled | Tender scenarios require extended retrieval of associated kiln operation records and laboratory reports. This supplements adjustment basis under raw fuel fluctuations |
collectionIds | Specify corresponding knowledge base collection IDs by business module | Data for this category is divided by departments such as mining, raw material preparation, and firing. Specifying collections narrows the retrieval scope and improves recall efficiency |
Three common mistakes
- Issue: Real-time kiln temperature data returned by external APIs cannot be used as knowledge base content. Cause: API data source synchronization rules are not configured, and returned results are not imported into the specified knowledge base collection when the interface is called directly.
- Issue: Knowledge base results returned when calling the chat interface are empty. Cause: The
collectionIdsparameter is not correctly specified, or the data corresponding to the collection ID has not completed synchronization updates. - Issue: Retrieval results include web document content that cannot be accessed normally. Cause: Login-restricted web links are not processed in advance, and direct import of links leads to failure to capture valid document content.
How to confirm the configuration is correct
- Enter the current batch's raw fuel composition parameters for retrieval testing, and verify that recall results include the corresponding raw material batching plans and kiln operation records.
- View knowledge base synchronization logs to confirm that multi-source data completes updates according to the preset real-time, per shift, and daily frequencies.
- Call the FastGPT v4.17.0 chat interface, specify the target knowledge base collection ID, and confirm that returned results only include documents within this collection.
- Adjust the
similarityparameter range, observe changes in retrieval result matching degrees, and confirm that parameter adjustments meet business accuracy requirements.
The values provided here are common starting points and should be measured against the reader's own samples.