What This Category's Data Looks Like
Data for this category originates from slab or billet batches, heating furnaces, mill automatic control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems. Daily documents include rolling plans, heating schedules, rolling regulations, plate shape and thickness records, surface quality reports, and quality certificates. Fields cover coil number, batch number, heating temperature, reduction rate, thickness deviation, surface defect level, and other related items. Units include degrees Celsius, millimeters, micrometers, and others. Data is updated in real time per coil or per piece, and the size of a single document varies based on the scale of the rolling batch.
Constraints on Vector Models and Indexes
Real-time per-coil data requires indexes to support incremental synchronization, avoiding performance losses from full reconstruction. Industrial technical terms and specialized units across multiple fields require vector models to adapt to rolling-related professional semantics, ensuring embedding accuracy. Bid documents need to associate multiple document types such as rolling plans and quality certificates, so indexes must support cross-document associated retrieval logic. The size of a single document varies with the batch, so the chunking strategy must adapt to rolling-related documents of different lengths, avoiding overly fragmented or overly long chunks that impair retrieval effectiveness.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the document length of single-coil rolling records, avoiding overly fragmented chunks that lose professional semantic associations |
| Overlap Length | 100–150 characters | Retains rolling batch context across adjacent chunks, preventing breaks in professional logic across chunks |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Filters low-match non-rolling-related documents, ensuring retrieval results align with bid requirements |
| Single Search Limit | Top 10 results | Controls the volume of retrieval results, adapting to the multi-document associated retrieval scenario for bid documents |
defaultToken | 2048 | Adapts to the embedding length of long documents such as rolling regulations, meeting the complete semantics of professional terms |
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. |
Three Common Misconfigurations
- 503 status code is returned, indicating no available channels for text-embedding-v3 in the current group. The cause is incorrect configuration of the embedding model's channel resources, or exhausted channel quotas.
- Vector model calls fail after startup, with an API key missing prompt. The cause is that the CHATAPIKEY environment variable was not configured via docker-compose. Restarting the container will take effect without rebuilding the image.
- Retrieval results cannot be associated with the quality certificate of a specific rolling batch. The cause is that an appropriate overlap length was not set, resulting in lost batch association information across chunks.
How to Verify Proper Configuration
- Access the vector index management page in FastGPT v4.17.0, and check whether the index's chunk length and overlap length match the configured values.
- Submit a test query containing the rolling temperature keyword, and check whether the similarity distribution of returned results falls within the set threshold range.
- Import a single-coil rolling record document, and verify that the index supports incremental synchronization without full reconstruction.
- Check the container environment variable configuration, confirm that CHATAPIKEY has been correctly loaded, and eliminate 503-level model channel errors.
The values provided are common starting points and should be evaluated against the reader's own samples.