What Data Looks Like for This Category
The main entities in this category are hot rolling, cold rolling, section steel, and steel pipe manufacturing enterprises. Daily data includes rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Data sources include slab or billet batch information, heating furnaces, mill automatic control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems. Updates occur in real time per coil or per steel product. Documents contain fields such as temperature parameters, reduction amounts, plate shape deviations, and defect levels. Units align with corresponding production indicators: degrees Celsius for temperature, millimeters for thickness, for example.
Constraints for Vector Models and Indexes
Real-time, per-coil or per-product updates require indexes to support incremental updates. This avoids performance loss from full index reconstruction. The coexistence of multiple document types — long specifications and short records — requires chunking strategies tailored to different text lengths. This prevents critical business information from being truncated. Data from multiple production line devices and MES systems has inconsistent field formats. Preprocessing steps are needed to align field standards. The coil-level traceability business requirement requires indexes to associate key identifiers such as batch IDs. This enables recall of related data by production batch. The real-time Q&A scenario for equipment inspection and maintenance requires vector recall response speed to match production line operations timeliness requirements.
Configuration Settings
The following configurations are designed for FastGPT v4.17.0, adapted to the equipment inspection and maintenance Q&A scenario for the steel rolling and finishing category.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
defaultToken | 8192 tokens | Adapts to encoding needs for long texts such as rolling specifications and quality inspection reports in this category, avoiding exceeding model input limits |
maxToken | 4096 tokens | Controls the encoding length of a single vector, balancing model performance and vector recall accuracy |
| Chunk Length | 800–1200 characters | Adapts to the average length of documents in this category. Avoids overly fragmented chunks that break context, or overly long chunks that cause encoding distortion |
| Overlap Length | 100–200 characters | Retains key contextual associations such as rolling batches and temperature parameters between chunks, preventing critical information from being truncated |
| Similarity Threshold | 0.72–0.80 | Matches equipment inspection and maintenance-related questions and documents, reducing irrelevant recall results |
| Single Search Limit | Top 8 results | Balances recall accuracy and response speed, meeting real-time Q&A requirements for production line operations |
Three Common Misconfigurations
- Symptom: A 503 status code error occurs when calling the embedding model, with a prompt stating no available nodes are available for the text-embedding model under the default group. Cause: No dedicated embedding model group is configured, or model nodes within the group are not online normally, causing call requests to fail to be correctly routed.
- Symptom: The Docker-deployed knowledge base remains in the indexing state with no progress updates. Cause: The network connection to the connected external embedding model is unstable, or the index chunk setting is too large, causing a single batch indexing task to take too long to complete.
- Symptom: Recalled equipment inspection results have low matching degree with the query, or a large number of irrelevant documents related to steelmaking processes appear. Cause: The similarity threshold is set incorrectly, or the chunking strategy is not adjusted for the document types of this category, causing vector encoding to fail to accurately match business keywords.
How to Verify Successful Configuration
- Review vector model call logs to confirm there are no 503 or timeout errors, verifying that the embedding model interface calls function normally.
- Manually upload a rolling specification or quality inspection report document, check whether the indexing progress completes within a reasonable time frame, confirming that the incremental indexing logic is active.
- Enter an equipment inspection-related query, verify the number and matching degree of recalled results, and adjust the similarity threshold to a range that meets business requirements.
- Review document association information after indexing, confirm that key fields such as coil numbers and batch IDs are correctly embedded in vectors, enabling subsequent traceability queries by production batch.
The values provided are common starting points and should be measured against the user's own samples.