What the data for this category looks like
Data for this category comes from steel coil labels and quality certificates, press and feeder operation data, mold management systems, MES systems, and coordinate measuring machine or fixture inspection results. Daily associated documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Data is updated per coil of material, per batch of materials, or each time a mold is changed. Fields include steel coil grade, press pressure parameters, mold number, workpiece dimensional deviation values, and similar items, with corresponding units such as millimeters, kilonewtons, and others.
What constraints these characteristics impose on the "model access and configuration" workflow
Data for this category is multi-source, heterogeneous, and updated at high frequency. New data is generated for each coil of material, per batch of materials, or each mold change. This requires model access to adapt to high-frequency updated datasets, to avoid impacting traceability accuracy due to data lag. At the same time, formats of associated documents, equipment, and quality inspection data vary widely, so access logic that supports parsing multiple data types must be configured. In addition, process parameters differ significantly across different mold change batches. The model must quickly associate mold and material information for the corresponding batch. Therefore, access configuration must reserve flexible parameter mapping space to adapt to data association rules across different scenarios, while controlling the volume of data per single request to avoid timeouts.
How to set the configurations
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category requires associating multi-source mold change, quality inspection, and equipment data. Sufficient context covers complete process and inspection information for a single batch |
maxTokens | 2000–3000 token | Defect analysis in stamping scenarios requires outputting structured causes and rectification suggestions, to avoid truncating key information due to exceeding output limits |
quoteMaxToken | 4000 token | Sufficient reference documents such as dimensional inspection reports and mold change records must be retrieved to support complete quality traceability logic |
temperature | 0.2–0.4 | Quality traceability requires rigorous conclusions. A lower temperature improves the stability and consistency of responses |
AIPROXY_API_ENDPOINT | Access address of locally deployed models | Adapt to access requirements of locally built large language models, and meet data localization processing requirements |
AIPROXY_API_TOKEN | Access key generated by the local model | Ensure the security of model access and restrict unauthorized access |
Three common configuration mistakes
- Symptom: When adding a new model channel, the protocol type option is fixed, and custom protocol cannot be selected. Cause: Custom model access permission is not enabled in the FastGPT backend, or the FastGPT version in use does not support custom protocol configuration.
- Symptom: Key process parameters are missing from defect analysis results returned by the model. Cause: The
maxContextconfiguration value is too small, failing to cover necessary context information such as mold change records and mold inspection sheets. - Symptom: Model response times out after batch importing multiple batches of quality inspection data. Cause: The
maxTokensconfiguration value is too low, causing the model to fail to process the data volume of multiple batches and triggering request truncation.
How to confirm the configuration is correct
- Test a call to the locally built large language model to verify that a normal response is returned, confirming that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct. - Upload a single batch of mold change records and dimensional inspection reports, test whether the model can associate and output corresponding process and inspection information, verifying that context and reference parameter configurations are reasonable.
- Input typical defect problems such as stamping part cracking and wrinkling, test whether analysis results output by the model include actionable traceability directions, confirming that the
temperatureparameter fits scenario requirements. - Initiate more than 5 consecutive high-frequency requests, verify that model response time meets business expectations, confirming the stability of the overall configuration.
The values provided are common starting points and should be measured against the user's own samples.