What the Data for This Category Looks Like
Data for this category comes from hot metal and scrap data ledgers, L2 process control systems, laboratory composition reports, MES production management systems, energy medium monitoring systems, and equipment condition monitoring terminals. Daily documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process parameter records, and slab quality judgment sheets. Data is generated in real time per heat. Key process parameters are updated synchronously with production progress, and records are summarized and archived per shift. Document fields include carbon content, tapping temperature, cast sequence number, slab thickness, and other metrics, with corresponding testing units and process parameter units attached.
Constraints Imposed on Vector Models and Indexing
Real-time data updates and per-heat, per-cast-sequence generation require indexing to support incremental synchronization, avoid full reconstruction, and reduce time usage during production gaps. Documents contain numerous industrial technical terms and process parameters with units. Vector models must adapt to the semantics of industrial process text, avoiding semantic bias present in general-purpose models. Strong associations between data fields and clear production timelines require indexing to support filtered retrieval by metadata such as heat number and cast sequence time, to narrow the recall scope. A single bid involves historical data for multiple heats and cast sequences. Index recall efficiency must align with the time requirements of bid preparation.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of single-heat process records, avoids including irrelevant cross-heat data in a single chunk |
| Overlap Length | 50–100 characters | Retains associated semantics of adjacent heat records, prevents key process parameters from being truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-match historical data, avoids mixing irrelevant heat records into bid documents |
| Single Search Limit | Top 10 results | Controls the volume of recalled data, adapts to the rapid retrieval needs of bid preparation |
maxToken | 4096 token | Adapts to the embedding length requirements of industrial professional text, avoids semantic truncation |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
This page is compatible with FastGPT v4.17.0.
Three Common Configuration Errors
- Symptom: After configuring the Qianwen text-embedding-v3 model, the interface displays a prompt that no available channel is found, and logs show API call failure under the default group. Cause: Channel permissions for the corresponding model are not configured in OneAPI, or the default group is not bound to the API key for this model.
- Symptom: Retrieval takes an excessively long time during knowledge base question answering, with delayed return of reranked results. Cause: Excessively large chunk length leads to overly long single-block text, or unreasonable
maxTokenconfiguration, which increases the computational load of vector retrieval and reranking. - Symptom: Retrieval results include slab judgment sheet data from non-continuous casting processes. Cause: No metadata filtering rules configured by process type and heat number, leading to an overly large recall scope.
How to Verify Successful Configuration
- Upload a single typical converter smelting record, run a vector embedding test, and check that the embedding log contains no text truncation errors. Confirm that the embedding configuration adapts to the current text length.
- Initiate a retrieval request containing a specific heat number, and check whether the recalled results only include associated documents for the corresponding heat. Confirm that the metadata filtering rules are configured correctly.
- Configure a test vector model, run a batch embedding test, and check whether synchronization time aligns with the time window of production gaps. Confirm that the incremental synchronization configuration is valid.
- Access the system embedding model management interface, confirm that the target model is bound to the currently used group, and there are no channel abnormality prompts.
The values provided are common starting points and should be measured against the reader's own samples.