What the Data for This Category Looks Like
Steel smelting ledger data originates from real-time collection systems and production management systems across each process. It covers single-furnace production records, team summary reports, and monthly statistical documents for steelmaking, ironmaking, steel rolling, and other links.
Data update rhythms fall into three categories:
- Real-time: Parameter records are generated every 30 seconds during single-furnace smelting
- Shift close: Team summary ledgers are generated after each shift ends
- Daily close: Full workshop production ledgers are generated each day
Each document includes fields such as furnace number, smelting duration, temperature at each stage, alloy composition ratio, energy consumption values, and quality inspection results. Field units include seconds, degrees Celsius, percentage, ton, kilowatt-hour, and others.
Constraints for Vector Models and Indexes
Real-time high-frequency updated single-furnace data has a large volume. Fixed large chunks will cause embedding semantic dilution, and full index construction takes too long. This makes it impossible to adapt to shift close and daily close update rhythms.
The multi-field mixed document structure requires retaining field identifiers and context during chunking. Otherwise, similar parameter entries will be easily confused during retrieval.
Update requirements of different granularities require a hybrid strategy of incremental and full indexes. This avoids repeated processing of historical data.
The strong association between field units and semantics requires prioritizing the association of field names with corresponding values during indexing. This improves retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Selection |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content length of single ledger entries for steel smelting, avoiding semantic dilution or context loss |
| Overlap Length | 150–200 characters | Retains the association logic between processes, such as transition information between heating and holding stages |
maxToken | 8192 token | Processes aggregated long ledger documents, avoiding truncation of critical production parameters and quality inspection results |
defaultToken | 1024 token | Serves as the default length for basic context, adapting to the basic semantics of single ledger entries |
| Single Search Limit | Top 10 entries | Covers associated data across multiple processes while controlling context length to avoid overflow |
| Similarity Threshold | 0.72–0.78, calibrated based on actual testing | Distinguishes ledger entries with similar process parameters, preventing irrelevant results from being included |
| Reranking | Enabled | Optimizes the sorting logic of retrieval results to match the multi-dimensional association requirements of ledger summary |
Three Common Misconfigurations
- Phenomenon: Index construction task queues pile up for extended periods, and the interface displays wait times exceeding preset thresholds. Cause: Incremental index mode is not enabled, and every index construction scans all historical ledger data.
- Phenomenon: Entries with empty fields or confused parameters appear in retrieval results. For example, "holding duration" is mistakenly matched to "smelting duration". Cause: Field identifiers are not retained during chunking, and semantic context is lost from split fragments.
- Phenomenon: Retrieval of file collections created via the OpenAPI interface frequently times out, returning status code 504. Cause: Chunk length and index shard parameters are not adjusted, and excessive single shard data volume leads to overly long processing times.
How to Confirm Proper Configuration
- The index management page of FastGPT v4.17.0 can be reviewed to confirm the matching relationship between shard size and chunk length, with no obvious data accumulation.
- A set of ledger test data containing multi-process parameters can be submitted, and the matching degree of associated entries in retrieval results can be verified to fall within the set similarity threshold range.
- The OpenAPI can be called to batch upload 10 historical ledger documents, and the time taken for index construction to complete can be confirmed to meet expectations.
- The embedding model call logs can be checked to confirm that there are no error messages related to
maxTokenoverrun.
The values listed above are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.