What the data for this category looks like
Data for this category includes product drawings and 3D models, CAPP/CAM files, operation logs collected by machine tools, tool lists and wear records from the tool management system, process data from the MES system, as well as calibration records of measuring tools and inspection forms. Data is updated per work orders, individual workpieces, sampling batches, or tool change nodes. It includes structured and unstructured content such as process routes, process cards, CNC programs, and first-piece inspection results. Fields cover machining-specific parameters including dimensional deviation, tool wear amount, and process duration.
How these characteristics affect model access and configuration
Data sources for this category cover machine tool collection, tool management systems, MES systems, and measuring tool records. It includes structured parameters and unstructured documents, and is updated frequently based on work orders, individual workpieces, or tool change nodes. The volume of process, tool, and inspection data linked to a single machining task is large. Model access and configuration must reserve sufficient context capacity to avoid losing key associated information. Quality traceability requires association of multi-source historical data, so configuration must support mixed processing of multi-format data. This ensures the model can accurately match defect phenomena with parameters from corresponding machining links.
How to set the configuration
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets context carrying needs for multi-source data of a single work order, avoiding truncation of key associated information |
maxTokens | 4000 token | Controls single-round response length, adapting to output specifications for quality analysis reports |
quoteMaxToken | 1500 token | Limits the length of referenced source data, ensuring referenced content focuses on machining parameters related to defect analysis |
temperature | 0.2–0.4 | Reduces random output, ensuring consistency of quality traceability conclusions |
| Workflow/API tool selection | auto | Automatically triggers tool calls, adapting to association query requirements for multi-source data |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Matches the access address of a local or third-party model |
AIPROXY_API_TOKEN | Fill in with the key generated by the platform | Completes identity verification for model access |
Three common configuration mistakes
- Issue: A prompt about port occupation appears when accessing a local model, or the model fails to load normally. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to point to the correct port of the local model service, or the port is occupied by other processes and not released. - Issue: A prompt about no available channels appears when selecting a specified model version, or the target model does not appear in the model list. Cause: Channel information for the corresponding model is not added in the group configuration, or the model name does not match the deployed version.
- Issue: Traceability conclusions output by the model lack key machining parameters, or context is truncated. Cause: The value of
maxContextis too small, resulting in truncation of multi-source associated data, which cannot cover complete machining link information.
How to confirm successful configuration
- Check the filled model access address and key, confirming they match the deployment information of the local or third-party model.
- Import associated data from a single typical machining work order, and test whether the model can fully load and associate multi-source document content.
- Set a test defect analysis query, and verify whether the conclusions output by the model cover parameters from the corresponding machining link.
- Review system logs, confirming there are no prompts for model call failures, context truncation, or parameter verification errors.
The values provided are common starting points and should be measured against the reader's own samples.