What Data Looks Like for This Category
The typical entities for this category are cement enterprise mining, raw material, calcination, grinding, quality control, and energy departments. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports.
Data sources cover online composition analysis systems, DCS control systems, laboratory data, kiln and mill equipment operation data, raw and fuel ledgers, and energy metering instruments. Update frequencies include real-time, shift-based, and daily.
Document fields include raw material ratio parameters, equipment operation values, test item results, inspection times, and others. There is no unified standardized format, and some fields are dedicated industrial production parameters.
Constraints Imposed on Vector Models and Indexes
Multi-source heterogeneous data sources require indexes to support mixed-format document imports. This prevents production data loss caused by format incompatibility.
Different update frequencies require layered index synchronization rules. These rules adapt to the update rates of real-time process data, shift-based inspection records, and daily reports.
Professional industrial production fields need vector models adapted to cement clinker production terminology. General-purpose vector models may fail to accurately encode core parameters such as batching ratios and kiln thermal regimes.
The rapid retrieval requirement for tender scenarios requires index recall efficiency to match preparation timelines. This avoids delaying document delivery due to retrieval latency.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section content length of raw material batching plans and kiln operation records, avoids splitting key parameters across document chunks |
| Overlap Length | 100–150 characters | Retains key cross-chunk associated information such as batching ratios and temperature curves, prevents key parameters from being split and lost |
defaultToken | 4096 | Matches the maximum effective content length of a single block of industrial documents, adapts to the basic configuration of FastGPT v4.17.0 |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes similar process parameters for cement clinker production from irrelevant data, avoids recalling redundant non-production documents |
| Single Search Limit | Top 8 entries | Matches the rapid retrieval requirement of tender preparation, balances recall accuracy and response speed |
Three Common Misconfigurations
- Symptom: Core business fields appear empty during index creation. Cause: Required parameters for raw and fuel ledgers and laboratory reports were not configured as index-associated fields. This leads to missing key business information during vector encoding.
- Symptom: A large number of irrelevant daily equipment inspection records are included in retrieval results. Cause: The similarity threshold was set incorrectly, failing to filter out non-core documents unrelated to tender quotation preparation.
- Symptom: Custom RAG cannot associate the process relationship between raw material batching and kiln thermal regimes. Cause: Flat entity indexing was used improperly, without retaining association rules between fields. This loses the context logic of the production process.
How to Confirm Proper Configuration
- Upload a single raw material batching plan document. Verify that index chunks fully retain core content such as batching ratios and raw material parameters, with no obvious truncation.
- Enter search terms including "clinker strength adjustment" and "batching plan optimization". Check the relevance of recall results, and adjust the similarity threshold to a range that meets business requirements.
- Test splicing historical quotation documents and current raw material data. Confirm that the
maxTokenconfiguration can accommodate the complete context without truncation errors. - View the index synchronization log. Verify that real-time updated kiln operation records are synchronized to the index database on time, with no delays or data loss.
The values provided here are common starting points, and should be evaluated against the reader's own samples.