What the Data for This Category Looks Like
Data for this category comes from molten iron and scrap steel collection systems, L2 process control terminals, laboratory reports, manufacturing execution systems (MES), energy medium monitoring platforms, and equipment status monitoring nodes. Daily generated documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Core smelting and continuous casting records are generated per heat. Key process parameters are synchronized in real time. Shift-level summary reports are updated per shift. Data fields include smelt batch identifiers, raw material ratio parameters, real-time temperature values, casting speeds, composition test results, and defect judgment results. There is no unified fixed format, and minor variations exist based on production line adjustments.
What Constraints Do These Characteristics Impose on Vector Models and Indexing?
High-frequency updated business features impose multiple constraints on the indexing process. Independent records generated per heat and real-time synchronized process parameters require indexing to support incremental writes and partial refreshes. This avoids resource consumption from full index rebuilding. Unstructured documents with varied fields require chunking logic to adapt to different document structures. Fixed-length one-size-fits-all chunking cannot be used. Mixed updates of shift-level summary reports and real-time data require flexible configuration of index refresh strategies. This balances real-time performance and storage overhead. The strong coupling between smelt batches and casting runs requires indexes to retain association identifiers between batches. This avoids breaking process links during retrieval.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | Adapt based on document type: 300–500 characters for heat records, 800–1200 characters for report documents | Differences in information density across documents avoid semantic fragmentation or overly large chunks, and adapt to the varied document formats of iron and steel smelting and continuous casting |
| Overlap Length | 50–80 characters | Retains cross-chunk process association information, avoiding context breaks in smelting workflows |
| Similarity Threshold | Calibrated based on actual testing | The fluctuation range of iron and steel process parameters is narrow, requiring adaptation to the matching accuracy requirements of the business scenario |
| Single Index Limit | Top 20 entries | Balances retrieval efficiency and recall coverage, adapting to the record volume per heat |
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. |
defaultToken | 200 token | Adapts to the basic retrieval length of single records, avoiding invalid token resource consumption |
Three Common Configuration Mistakes
- Symptom: Index construction stalls at the final dataset, with no progress updates in the interface. Logs show excessive resource usage peaks. Cause: No incremental indexing strategy is configured. Full index rebuilding triggers system resource exhaustion due to the large volume of frequently updated smelting and continuous casting data.
- Symptom: After adding a custom embedding model, indexes can be generated normally, but search tests return errors or no matching results. Cause: The vector encoding logic is not adjusted for the numerical process parameters of iron and steel smelting. This causes generated vectors to fail to match business features.
- Symptom: After batch uploading documents, only the final set of files completes indexing. The status of all other files remains pending for indexing. Cause: The single index limit is configured too low. Batch tasks are truncated, failing to adapt to the batch upload requirements of iron and steel smelting documents.
How to Confirm Proper Configuration
- Upload a single converter smelting record. Verify that index generation progress completes normally, with no stalls or errors.
- Enter a search term containing specific process parameters. Check that the returned results include associated batch identifiers consistent with business logic. Adjust matching rules to meet requirements.
- View the system index monitoring panel. Confirm that incremental update tasks trigger at the preset frequency, with no abnormal full index rebuilding operations.
- Check the loading logs of the custom embedding model. Confirm that the encoding rules adapted to iron and steel smelting data are in effect.
The values provided are common starting points and should be measured against the reader's own samples.