What Data for This Category Looks Like
Core data for this category uses Vehicle Identification Number (VIN) as the unique primary identifier. Data sources include ERP systems, MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, and quality traceability systems. Daily documents cover production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, rework records, and consistency release documents. Data is updated in real time for each vehicle, with batched summaries completed per shift. Each complete vehicle’s associated documents include hundreds of configuration and inspection fields. Some fields have clear units: torque values use newton meters, inspection thresholds use percentages.
Constraints on Vector Models and Indexing
Single-vehicle core data tied to VIN requires indexes to support fast filtering of associated documents by VIN. This avoids interference from cross-vehicle data. Real-time production and inspection data requires indexes to support incremental synchronization. This prevents performance loss from full index rebuilding. Using solid-state drives to store vector indexes further reduces real-time retrieval performance loss. Document structures with hundreds of configuration and inspection fields per vehicle require chunking logic aligned with workstations or process divisions. This avoids vector confusion across content. Additionally, errors in inspection and audit workflows have severe consequences. Vector retrieval recall rate and matching accuracy must meet business verification standards. Missing or mismatched retrieval results must be prevented.
How to Set Configurations
These configurations are based on the interface and parameter logic of FastGPT v4.17.0. maxToken and defaultToken control the length of retrieval context.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the average length of individual entries in assembly work instructions and tightening data, avoids single chunks spanning multiple processes |
| Overlap Length | 100–200 characters | Covers process-related information across workstations, ensures retrieval can associate operation records from preceding and following workstations |
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. |
defaultToken | 1500 token | Adapts to the typical content length of single-station tightening data and rework records, balances retrieval efficiency and information completeness |
| Similarity Threshold | 0.75–0.85 | Calibrated through actual testing, matches the accuracy requirements for configuration matching and defect identification in inspection and audit workflows |
| Single Search Limit | Top 20 results | Corresponds to the upper limit of configuration and inspection items associated with a single VIN, avoids excessive invalid results |
Three Common Misconfigurations
- Phenomenon: Mixed retrieval response time exceeds 10 seconds. Single retrieval latency is noticeably higher than pure vector retrieval. Cause: Index sharding is not optimized for VIN-focused single-vehicle core data. Vector indexes are not stored on solid-state drives. Mixed retrieval calls multiple data source vector libraries at the same time, resulting in excessive computational load.
- Phenomenon: Index building gets stuck at the final index group step, with no progress updates for an extended period. Cause: Incremental indexing mode is not enabled. Full indexing imports all VIN data for a single batch, exceeding the system's single-task processing threshold.
- Phenomenon: The knowledge base cannot correctly import tightening data, offline inspection reports, or other targeted files. Cause: Parsing plugins for corresponding file types are not configured in advance. Only general document formats are supported.
How to Confirm Configurations Are Properly Set
- Review system retrieval logs to confirm that single VIN-related retrieval response times meet expected standards.
- Test importing a single VIN list and tightening data files to confirm that index creation proceeds without stalls.
- Verify parsing results for different file types to confirm that core fields such as VIN codes and torque values are fully extracted.
- Adjust the similarity threshold to ensure that retrieval result matching accuracy meets the verification requirements of business inspection and audit workflows.
The values provided are common starting points, and should be measured against the reader's own samples.