What this category of data looks like
Data sources include online composition analysis equipment, DCS control systems, laboratory reports, kiln and mill operation logs, raw and fuel inbound and outbound records, and energy metering systems. Update frequencies are divided into three categories: real-time (equipment operating parameters), shift-based (equipment patrol records), and daily (quality control notices, energy consumption reports). Document formats are mostly structured tables, time-series operation logs, and checklist-style production plans. Fields include raw and fuel composition parameters, equipment operating temperature, clinker strength indicators, energy consumption values, and operation timestamps. Units include degrees Celsius, kilograms, kilowatt-hours, tons, and others.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Heterogeneous multi-source data includes structured reports, time-series logs, and checklist documents. Different retrieval matching logic must be adapted to these formats. Time-series data must be recalled sorted by timestamp to align with production timelines. Real-time updated equipment parameter data requires consistent knowledge base synchronization frequency to avoid recalling stale data. Shift-based and daily updated patrol and quality documents must trigger incremental synchronization per their respective cycles to prevent data lag. Retrieval of professional production indicators such as clinker strength and kiln temperature must match industry-specific terminology. High accuracy requirements apply to recalled terms.
How to set up the configuration
This configuration is formulated for FastGPT v4.17.0, and is adapted to the inspection and process record audit scenario for cement clinker and grinding:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match cement production professional indicators, filter low-relevance search results |
limit | Top 3–5 entries | Control context length, meet precise retrieval needs for production scenarios |
datasetSearchUsingExtensionQuery | Enabled | Expand professional terminology, resolve ambiguous references, such as expanding "current parameters" to production parameters for the corresponding position |
usingReRank | Enabled | Perform secondary sorting on initial retrieval results, improve accuracy of professional indicator matching |
searchMode | embedding | Adapt to semantic matching logic for time-series logs and professional terminology |
collectionIds | IDs of three dataset groups: raw material batching, kiln operation, laboratory reports | Divide retrieval scope by production link, reduce the scale of recalled data sources |
Three common mistakes
- Issue: After importing a raw material batching Excel file, multiple row contents are merged into a single knowledge base chunk. Cause: No custom delimiter is configured, and single records are not split by line breaks.
- Issue: Search results include equipment patrol records unrelated to clinker strength. Cause:
usingReRankis not enabled, or thesimilaritythreshold is set too low. - Issue: Ambiguous queries such as "current parameters" fail to recall corresponding batching plans. Cause:
datasetSearchUsingExtensionQueryis not enabled, and no query expansion or reference resolution is performed.
How to confirm the configuration is properly set
- A test laboratory report Excel file is uploaded. The imported chunks are checked to confirm they are split by row, with each record corresponding to an independent chunk.
- A test query such as "Reasons for unqualified clinker strength today" is entered. Search results are verified to cover the corresponding day’s laboratory reports and quality control notices.
- The retrieval configuration panel is viewed to confirm that both
datasetSearchUsingExtensionQueryandusingReRankare enabled. - The
similaritythreshold is adjusted. Changes in the relevance of search results are observed to confirm alignment with business matching requirements.
The values provided are common starting points, and should be evaluated against the operator’s own samples.