What the data for this category looks like
The typical data scope for this category covers cement enterprise mining, raw meal, calcination, grinding, quality, and energy departments. Data sources include online composition analysis equipment, DCS systems, laboratory test results, kiln and mill equipment operation logs, raw fuel ledgers, and energy metering instruments. Update frequencies are divided into real-time (equipment operation parameters), shift-based (team patrol and operation records), and daily (energy reports and quality summaries). Daily generated documents include raw meal batching plans, kiln operation logs, laboratory test reports, quality control notices, equipment inspection sheets, and energy consumption reports. Fields include equipment operation parameter values, test indicator values, operation time, team information, and energy consumption values, with units including degrees Celsius, kilowatt-hours, tons, and others.
What constraints do these characteristics impose on the vector models and indexing link
The following configurations are adapted for FastGPT v4.17.0. The multi-frequency updates, multi-type documents, and multi-field characteristics of this category impose multiple constraints on the vector indexing process. The difference in update frequency between real-time equipment parameters and daily summary reports requires the index to support a combination of incremental and full updates, to avoid duplicate indexing or data lag. Multi-type documents include structured Excel ledgers and semi-structured logs, requiring adaptation to different text splicing rules to ensure complete transmission of cross-field information such as batching plans and inspection records. Multi-field data with specific units requires the vector model to retain the association between fields and units during encoding, to avoid semantic confusion. A single knowledge base can reach a data volume of ten thousand rows, so the chunking granularity must be reasonably controlled to avoid index failure caused by overloaded single chunks.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for ten-thousand-row Excel ledgers and multi-field documents, avoids overloaded single chunks |
| Overlap Length | 100–200 characters | Preserves semantic connections between adjacent chunks, prevents breakage of cross-chunk batching plan information in ledgers |
| maxToken | 8192 | Meets long-text splicing requirements for cement clinker-related documents, aligns with input limits of mainstream open-source embedding models |
| Similarity Threshold | Calibrated via actual testing | Adapts to semantic matching accuracy for equipment operation parameters and quality indicators, avoids false recalls or missed recalls |
| Single Search Limit | Top 10 results | Covers recall requirements for multi-type reference data such as team patrols and energy reports, balances retrieval efficiency and result richness |
Three common mistakes
- The number of associated ledgers returned by retrieval is lower than expected. Cause: The single search limit configuration value is too low, failing to cover the cross-department, cross-document associated data requirements in cement clinker production.
- The locally deployed embedding model configuration is overwritten by a model with the same name. Cause: Unique identifiers are not set for embedding models from different deployment sources. FastGPT uses the model name as the unique recognition basis by default.
- Encoding timeout occurs during knowledge base construction. Cause: The chunk length configuration exceeds the range allowed by
maxToken, causing the vector model to fail to complete encoding of a single chunk of text.
How to confirm the configuration is complete
- Go to the FastGPT knowledge base configuration page, check whether the deployment address and port of the embedding model match the locally docker-deployed M3E.
- Upload a test Excel file with approximately 1,000 rows of cement raw meal batching data, run chunk preview, and confirm that the chunk length and overlap length match the preset configuration.
- Initiate a retrieval request, check whether the number and sorting of returned results match the configuration of the similarity threshold and single search limit.
- Check whether the
maxTokenparameter matches the input limit of the currently used embedding model, to avoid encoding failures.
The values provided are common starting points and should be measured against the reader's own samples.