What the data for this category looks like
Daily data for complete vehicle final assembly includes production orders, VIN lists, assembly work instructions, tightening data, off-line inspection reports, rework records, and more. Data sources cover ERP systems, MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, and quality traceability systems. Data is generated in real time per individual vehicle. It also includes batch rework records and release documents summarized per shift. Fields include unique VIN codes, torque values, station numbers, inspection item numbers, and more. Some data uses professional units such as newton meters and milliseconds.
Constraints imposed by these characteristics on vector models and indexes
Since this category uses VIN as the main identifier for individual vehicles in mixed-line production, vector indexes must support precise filtering of search results by VIN to avoid mixing data across different vehicles. Real-time per-vehicle data requires indexes to support incremental synchronization, eliminating performance losses from full index rebuilding. In scenarios with mixed multi-type documents, vector chunking must balance structured tightening data and unstructured work instructions, while retaining cross-chunk associated fields such as VIN and station numbers to ensure coherent search results. High-concurrency query demands from large-scale mixed-line production require indexes to support low-latency response.
Configuration settings
This configuration scheme is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxToken | 1024 token | Matches the maximum input length of current mainstream vector models, and aligns with the average content size of single-chunk documents in the complete vehicle final assembly scenario |
| Chunk Length | 800–1200 characters | Balances chunk granularity and search accuracy, avoiding overly long chunks that prevent complete encoding by the model, or overly short chunks that break associated information |
| Overlap Length | 100–200 characters | Retains cross-chunk associated fields such as VIN and station numbers, ensuring different chunks of the same document can be linked to complete context during search |
| Task Timeout | 600 seconds | Fits scenarios where multiple vehicle rework records and off-line reports are processed in batches, preventing forced termination of tasks due to excessive single-run duration |
| Automatic Retry Count | 2–3 times | Addresses temporary local network fluctuations or model response anomalies, reducing task interruptions caused by single failures |
Three common misconfigurations
- Phenomenon: When uploading off-line inspection reports larger than 10MB, vectorization errors occur in some chunks after generating over 1000 chunks. Retrying multiple times restores normal functionality or removes error prompts. Cause: No reasonable overlap length is set, leading to loss of cross-chunk associated VIN or station numbers, which triggers vector model input format validation failures.
- Phenomenon: After deploying ollama on a local CPU server, vector index tasks remain unresponsive for extended periods, and the knowledge base status stays at "Not Ready". Cause: The
defaultTokenparameter is not adjusted to match the actual input limit of the local model, leading to model loading timeouts and failed task progression. - Phenomenon: After batch uploading rework records for multiple vehicles, the server crashes. After restarting, knowledge base index tasks stall and cannot recover automatically. Cause: Incremental index configuration is not enabled, and full index tasks have no resume-on-failure setting, so unfinished tasks cannot be automatically loaded after restart.
How to confirm correct configuration
- Upload a single complete vehicle final assembly work instruction, view the chunked chunk list, and confirm that single-chunk lengths fall within the
800–1200 charactersrange. - Input a search term containing a VIN code and station number, view the completeness of associated fields in search results, and confirm that the overlap length configuration is active, allowing cross-chunk content to be searched coherently.
- Simulate batch uploading rework records for 10 vehicles, view the index task execution logs, and confirm that no errors occur due to
maxTokenconfiguration exceeding the model's limit. - Restart the server, view the knowledge base list, and confirm that unfinished index tasks recover automatically without requiring manual re-upload or reconfiguration.
The values provided here are common starting points and should be evaluated against the reader's own samples.