What data for this category looks like
This category uses single vehicle VIN as the core identifier. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, off-line inspection reports, rework records, and consistency release documents. Data sources cover ERP/MES systems, VIN and configuration management systems, tightening tools, EOL inspection lines, and quality traceability systems. Updates occur per vehicle, in real time, or per shift. Fields include VIN code, torque parameters, inspection item number, part batch number, and other related fields.
What constraints these characteristics impose during model integration and configuration
The single-vehicle VIN-centric data association logic requires model integration to support precise matching of associated documents by VIN, to avoid mixing data across vehicles. Frequent real-time and per-shift updates require the model invocation pipeline to have low latency, and vector indexes to support fast incremental updates. Large numbers of assembly-related numeric fields such as torque parameters require vector models to adapt to semantic and numeric matching for structured numeric data. Mixed-line production scenarios with parallel processing of multiple configurations require configuration items to limit the single context and recall range, to prevent resource overload.
How to set configurations
Configuration items are based on the standard logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of multiple documents associated with a single vehicle including work instructions and rework records, to avoid truncation of critical VIN-associated information |
maxTokens | 4000 token | Limits the output length of single-round responses to meet the precise instruction requirements of equipment maintenance Q&A, preventing overly long results from disrupting station operations |
quoteMaxToken | 2000 token | Limits the total length of recalled documents, matches the total volume of associated documents in a single vehicle assembly scenario, and avoids redundant data interfering with model judgment |
temperature | 0.2–0.4 | Reduces the randomness of responses, ensures the accuracy of equipment inspection and maintenance steps, and avoids generating operation instructions that do not meet process requirements |
AIPROXY_API_ENDPOINT | Enter the local or private API address of the deployed model | Adapts to internally deployed vector and large language models within the enterprise, ensuring compliance with calls for production-related data |
AIPROXY_API_TOKEN | Configure enterprise internal access keys | Restricts unauthorized access, preventing unauthorized calls of production data |
Three common configuration errors
- Vector model calls return errors, and the interface prompts model connection failure. The cause is failure to correctly configure the locally deployed vector model into the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters, or failure to open access permissions for the private API port. - The vector index remains in the "indexing" state for a long time with no progress updates. The cause is failure to adapt to the incremental update logic of single-vehicle batch data, or the
maxContextparameter of the vector model being set too small, causing chunking failures. - The model output includes unprocessed
<think></think>tag content. The cause is failure to perform pre-replacement for the reasoning output format of DeepSeek-like models, preventing the model from correctly recognizing structured reasoning content.
How to confirm successful configuration
- Test querying associated documents by VIN, check that the number and type of recalled documents match the expectations of a single vehicle assembly scenario, and adjust corresponding parameters to match the actual total volume of documents.
- Initiate a simulated equipment inspection query, verify that the maintenance steps output by the model match the content of the work instructions, and adjust parameters to control the accuracy of responses.
- View the model invocation logs, confirm that the request success rate for the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters meets expectations, and troubleshoot network or permission issues. - Test incremental upload of single-vehicle tightening data, verify that the vector index can complete updates within the preset time, matching the update rhythm of real-time data.
The values provided are common starting points and should be measured against the reader's own samples.