What the data for this category looks like
Coal mining production ledger data primarily comes from monitoring terminals, equipment inspection terminals, and dispatch command platforms of production systems including fully mechanized mining, tunneling, and ventilation. Data update cycles fall into four categories: real-time, hourly, shift-based, and monthly. Real-time data tracks equipment operating status. Hourly updates generate period summary ledgers. Shift-based (8-hour) ledgers are generated for routine inspections. Full mine production summary ledgers are created at the end of each month. Each ledger document has a fixed structure, including fields such as equipment number, operating duration, fault count, inspection personnel, and timestamp. Units are uniformly hours, counts, equipment units, and standard time formats.
Constraints on vector models and indexing
Real-time and high-frequency updated ledger data requires indexes to support incremental updates. This avoids resource consumption and delays caused by full index reconstruction. Fixed field structures and uniform units require vector models to adapt to structured text. Prioritize models that support short-text encoding. Massive ledger data generated by multiple devices requires index recall limits and chunking strategies to adapt to batch query scenarios. The system must also support metadata filtering by equipment number and timestamp, to narrow the recall scope.
Configuration settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Coal mining production ledger single records have concentrated fields. Excessively long chunks will mix in unrelated information. This range adapts to structured short text |
| Overlap Length | 50–100 characters | Prevents split device operating data across chunks, ensures context continuity |
maxToken | 4096 token | Adapts to the input limit of most open-source vector models, avoids truncation of critical timestamps and equipment parameters |
| Similarity Threshold | 0.72–0.85 | Differentiates vector matching scores between normal production ledgers and abnormal records, filters low-correlation historical data |
| Single Search Limit | Top 20 entries | Balances recall efficiency and coverage, adapts to multi-equipment batch query scenarios for coal mining ledgers |
defaultToken | 1024 token | Controls the basic context length for single-round report generation, avoids exceeding platform resource limits |
Three common misconfigurations
- Phenomenon: When deploying a vector model locally, logs show insufficient video memory, or a second 3090 graphics card cannot be detected. Cause: Failed to specify visible graphics cards in the configuration file of FastGPT v4.17.0 or later, or failed to enable multi-graphics card load balancing.
- Phenomenon: After adding an index model, a 404 error appears in the interface, or an interface request returns a 404 status code. Cause: Failed to correctly configure the model access address, or the model routing path changed after a version upgrade.
- Phenomenon: Attempting to add a vector model without a GPU environment fails. The m3e service runs inside the container but cannot be connected. Cause: Failed to disable GPU mandatory dependency in the configuration, or failed to correctly map container ports to local service addresses.
How to confirm proper configuration
- Upload a single coal mining production ledger sample. Check if the chunking results retain core fields such as equipment number and operating duration, with no obvious content splitting.
- Initiate a ledger summary query. Verify that the number of returned context entries matches the single search limit setting, with no overages or shortfalls.
- Check the vector model runtime logs. Confirm that the specified model parameters have been loaded, with no video memory overflow or connection timeout errors.
- Trigger a report generation task. Cross-check that the generated content includes the preset field range, with no critical information missing.
The values provided here 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.