What Data for This Category Looks Like
Declaration materials and case data for non-ferrous metal smelting come primarily from four sources: raw material quality inspection reports, environmental assessment declaration documents, work safety case files, and monthly smelting logs. Data update frequencies fall into three categories: real-time (raw material composition test data), monthly (declaration material submission), and quarterly (case file archiving). Documents include fixed header fields: smelting batch number, raw material metal proportion, energy consumption parameters, approval document number, and rectification records. Proportion fields use percentage units. Batch numbers and document numbers use string formats. The text length of a single case file mostly falls between 2000 and 5000 characters.
What Constraints These Characteristics Impose on Vector Models and Indexing
Different update frequency data sources require differentiated indexing strategies. Real-time test data needs incremental indexing. Batch declaration materials need full refresh. Documents contain both structured numerical fields and unstructured approval text. Vector encoding must balance feature extraction for both content types, to avoid losing numerical association information with a single encoding logic. Fixed field formats and units require retaining field metadata during indexing, to prevent matching errors caused by unit confusion across smelting data batches. Long-text case file chunking must avoid destroying the integrity of approval logic, which increases the difficulty of adjusting chunking parameters.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of single raw material test reports and approval opinions |
| Overlap Length | 100–150 characters | Prevents truncation of cross-block smelting batch association logic |
| Similarity Threshold | 0.75–0.85 | Distinguishes matching accuracy for strongly associated fields such as smelting batch numbers and composition proportions |
| Single Search Limit | Top 10 entries | Adapts to the candidate quantity requirement for single calls in case review |
maxToken | 4096 | Adapts to the context encoding length of long-text case files |
defaultToken | 1024 | Controls the encoding token limit for a single vector to avoid GPU memory overflow |
Three Common Mistakes
- Phenomenon: GPU memory overflow error occurs when starting the vector encoding service. Cause: No reasonable
maxTokenparameter set for long-text smelting case files. The memory occupied by a single encoding exceeds the available capacity of a single 3090 GPU. - Phenomenon: Calling the Baidu embedding-v1 interface returns a 404 status code. Cause: The exclusive interface path and permission key for embedding-v1 were not correctly configured in OneAPI, leading to incorrect request routing.
- Phenomenon: The locally deployed vector model can only call a single 3090 GPU. Cause: No multi-GPU scheduling configuration added to Docker startup parameters. The service defaults to allocating only the first GPU resource.
How to Confirm the Configuration Is Correct
- Check the vector service logs of FastGPT v4.17.0 to confirm that the
maxTokenanddefaultTokenconfiguration parameters match the preset values. - Upload a typical non-ferrous metal smelting declaration material, and check that the chunked text length falls within the preset chunk and overlap length ranges.
- Initiate a case review retrieval request, and confirm that the number of returned candidate results matches the single search limit configuration.
- Check the resource usage of the Docker container, and confirm that multiple GPUs are being called normally by the vector service.
The values provided are common starting points and should be measured 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.