What the data for this category looks like
Typical entities in stamping and sheet metal forming scenarios are stamping workshops of enterprises producing automotive body panels, household appliance sheet metal parts, and hardware components. Daily generated documents include stamping work instructions, material utilization sheets, mold inspection logs, first and last piece records, dimensional inspection reports, and mold change records. Data sources cover steel coil labels and quality certificates, real-time data from punch presses and feeders, mold management systems, MES systems, and coordinate measuring machine or fixture inspection data. Data is updated per steel coil, per production batch of materials, and per mold change operation.
What constraints these characteristics impose on model integration and configuration
Business data for stamping and sheet metal forming has high update frequency and significant batch differences. Each steel coil, production batch of materials, and mold change operation generates new business data. This requires model context configuration to adapt to multiple document fragments updated in short, frequent batches. Multi-source heterogeneous data sources mean the model must process structured and unstructured data in different formats. Configuration must account for adaptability to different data sources. The supplier and process quality audit scenario has extremely high requirements for result accuracy. Parameter adjustments must balance response speed and content credibility. Incorrect configuration must be avoided to prevent deviations in audit results. Cross-system data interaction requires proper network connectivity and reasonable permission configuration. It must adapt to local call requirements for private deployments.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple documents generated per steel coil or mold change, ensuring all relevant data required for a single audit is covered |
maxTokens | 2000–3000 token | Meets the answer length requirement for quality audit queries, avoiding exceeding the model's output limit |
quoteMaxToken | 4000 token | Limits the total length of document fragments referenced by the model, preventing the model from generating forged reference IDs outside the actual document scope |
temperature | 0.1–0.3 | Reduces answer randomness, improving consistency and accuracy of quality audit results |
AIPROXY_API_ENDPOINT | Fill in the intranet or public network address according to the deployment environment, for example https://volc-private-llm.example.com/v1 | Adapts to large model access for private deployments on Volcano Cloud, ensuring the docker container can normally access the target address |
AIPROXY_API_TOKEN | Fill in the access key for the private model | Completes model identity verification, ensuring access security |
Three common mistakes
- Phenomenon: The model's quality audit response includes forged reference IDs that do not appear in the uploaded documents. Cause: The
quoteMaxTokenparameter is not configured correctly, causing the model to fail to accurately match existing document fragments and generate reference content outside the actual document scope. - Phenomenon: Consecutive sessions fail to retain context-related information. Cause: Context association is not enabled in the session configuration of FastGPT v4.17.0, causing the session ID to not be correctly passed and bound.
- Phenomenon: FastGPT deployed locally via docker cannot connect to the Volcano Cloud private large model. Cause: The correct intranet address for
AIPROXY_API_ENDPOINTis not configured, or the container does not have network access permissions for the corresponding port.
How to confirm the configuration is complete
- Upload a stamping work instruction and a mold inspection log, send a test request asking "Verify the blank holder force parameters for this mold change", check if the returned results include the parameter content explicitly mentioned in the documents.
- Send two consecutive quality audit sessions, check the FastGPT backend logs to confirm the session ID is correctly recorded and associated with the context of the two requests.
- Run a model connection test command in the docker container, check if the logs show a prompt indicating successful model connection.
- Adjust the
temperatureparameter to 0.2, send three identical audit requests, check if the differences between the three returned results match the expected low randomness.
The values provided are common starting points and should be measured against the reader's own samples.