What Data for This Category Looks Like
This category of data originates from product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES systems, and measurement and inspection tool data. Daily generated documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data updates trigger with each work order, each machined workpiece, or during random inspections and tool changes. Data fields include process number, tool model, dimensional deviation value, surface roughness parameters, machine tool operating parameters, and other related items.
Constraints Imposed on Vector Models and Indexes
The multi-source dispersion, high-frequency updates, and diverse document types of this category of data create multiple constraints for the vector model and index link. Multi-source heterogeneous data requires models to support mixed encoding of structured parameters and unstructured text, ensuring consistent vector space representation for operation cards, CNC programs, and machine tool collection data. High-frequency update scenarios require indexes to support incremental synchronization, avoiding performance losses caused by full reconstruction. Associated data across different processes must retain sequence features, while filtering duplicate patrol inspection records and calibration data to maintain the accuracy of traceability links.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of operation cards and CNC programs, avoiding splitting of single-block information |
| Overlap Length | 100–150 characters | Retains associated context between processes, preventing cross-block semantic loss |
| Similarity Threshold | 0.75–0.85 | Filters duplicate patrol inspection records and calibration data, matching the precision requirements of metal cutting parameters |
| Single Search Limit | Top 10 entries | Adapts to the number of processes per work order, controlling redundancy of recall results |
defaultToken | 2000–3000 token | Adapts to the long-text encoding requirements of CNC programs, ensuring complete extraction of machining parameters |
maxToken | 4096 token | Matches the default vector model context limit of FastGPT v4.17.0, avoiding encoding overflow |
Three Common Configuration Errors
- Phenomenon: A 401 status code is returned when calling the vector model, with an authentication failure prompt. Cause: The vector model API key is not configured correctly, or the key permissions do not cover vector encoding requests.
- Phenomenon: Duplicate process records in the knowledge base are not filtered, resulting in a large number of redundant entries in recall results. Cause: Index deduplication configuration is not enabled, or the deduplication threshold is set outside a reasonable range, failing to match duplicate patrol inspection data for the same process.
- Phenomenon: The order of custom-split document chunks does not match the original document, making it impossible to restore the association between machining processes. Cause: The chunk overlap length is not retained, or vector data is not stored in the original document order during index merging.
How to Verify Proper Configuration
- Import a single operation card document, confirm that the number of vector-encoded chunks matches the expected count, with no excessive splitting or merging.
- Submit a query that includes tool wear parameters, verify that recall results include associated records for the same process, and that the order follows the machining flow.
- Simulate high-frequency data updates following a tool change, check that the index automatically synchronizes new records without prompts for full reconstruction.
- Check the system logs for FastGPT v4.17.0 to confirm there are no abnormal errors during vector model calls.
The values provided are common starting points and should be evaluated against the reader's own samples.