What This Category of Data Looks Like
This category of data comes from multiple business documents and system-collected data from forging plants, aerospace and automotive forging suppliers, and forging workshops. Daily documents include forging process specifications, blanking cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data sources cover raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Data update frequency is per piece, per batch, or per furnace charge. Fields include furnace temperature, holding duration, pressure tonnage, forging dimensional tolerance, non-destructive testing results, furnace batch number, forging batch number, and more.
Constraints on Model Access and Configuration
These data characteristics impose multiple constraints on model access and configuration. Multi-source heterogeneous data sources require configuring access rules adapted to different system formats. High update frequencies require supporting data synchronization triggered by furnace charge or batch to avoid outdated data affecting inspection results. Diverse document structures require adapting text splitting for different lengths and fields, ensuring key identifiers such as furnace batch numbers and forging batch numbers are not truncated. Severe consequences of errors require configuring parameters with high confidence to ensure maintenance and inspection plans comply with industry standards.
Recommended Configuration Settings
This page’s configuration is based on FastGPT v4.17.0. Specific parameters and recommended settings are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multi-source documents such as forging process specifications and ultrasonic testing reports, supports cross-batch and cross-process context association |
maxTokens | 2000–3000 token | Matches the single-response length requirement for equipment inspection and maintenance Q&A, avoids exceeding model output limits |
quoteMaxToken | 1500 characters | Adapts to the effective length of single-section documents such as heating furnace records and heat treatment work orders, retains key identifiers such as furnace batch numbers and forging batch numbers |
temperature | 0.2–0.4 | Ensures the accuracy of maintenance and inspection plans, avoids generating content that does not comply with forging process specifications |
AIPROXY_API_ENDPOINT | Fill in the proxy address matching the deployment environment | Adapts to API access paths for overseas or local deployments, resolves cross-region access network issues |
AIPROXY_API_TOKEN | Fill in the assigned access key | Enables secure API call authentication, prevents unauthorized access |
Three Common Configuration Errors
- Symptom: An error
tls: failed to verify certificate: x509: certificate signed by unknown authorityis returned when calling overseas models. Cause:AIPROXY_API_ENDPOINTis not configured correctly, or certificate verification rules for the deployment environment are not adapted. - Symptom: The number of vector recall results is far lower than expected, and cannot cover the current equipment parameters for inspection. Cause: The
maxContextvalue is too small, causing the system to fail to load sufficient historical forging batch and equipment data. - Symptom: Uploaded heating furnace records and ultrasonic testing reports cannot be correctly associated with the corresponding forging batch. Cause: The
quoteMaxTokenparameter is not adjusted based on document fields, causing key furnace batch number and batch number fields to be truncated.
How to Validate the Configuration
A test query for the heating parameters of a specific furnace charge may be performed, and the returned content may be checked to confirm it includes complete data for that furnace charge. API call logs may be reviewed to confirm the authentication request for AIPROXY_API_TOKEN returns a normal status code. A batch of simulated forging testing reports may be uploaded, and the range of recall results may be checked to match the configured context range. The temperature parameter may be adjusted, and differences between two responses may be compared to confirm the expected level of rigor is achieved.
The values provided here are common starting points, and should be evaluated against samples appropriate for the specific deployment.