What the Data for This Category Looks Like
Data for this category includes structured documents such as injection molding process cards, raw material drying records, mold history, first-piece inspection reports, patrol inspection forms, and material change and machine cleaning records. Data sources include resin batch property sheets, injection molding machine parameters, mold temperature controllers, MES systems, and dimensional and appearance inspection equipment. Data is updated every production batch, each mold or material change, and each shift handover. Each individual document contains industrial parameters such as mold number, raw material model, mold temperature, injection pressure, holding time, and raw material drying parameters, with clear industrial unit identifiers attached.
What Constraints Do These Characteristics Impose on the "Model Integration and Configuration" Link
Dispersed data from multiple sources requires integration configuration to support cross-system document synchronization and parsing, and adapt to industrial records in different formats. The high-frequency update feature requires configuration to support near-real-time document pulling and processing, to avoid data lag that affects inspection and audit efficiency. Diverse document types and industrial parameters with units require configuration to adapt to structured document field recognition, ensuring the model can accurately read key parameters. Severe batch defects, mold damage or material mixing risks require configuration to reduce model output randomness, ensuring consistency and accuracy of inspection and audit results.
How to Set the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context requirements of injection molding process records, avoiding truncation of key process parameters and historical inspection data |
maxTokens | 2000–3000 token | Meets the output length requirements for inspection result analysis, covering defect type judgment and rectification parameter suggestions |
temperature | 0.2–0.4 | Reduces model output randomness, ensuring consistency of industrial parameter judgment and defect determination |
vision | Enabled | Adapts to image input requirements for appearance defect detection, identifying issues such as flash, short shot, and warping |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployment address of the accessed model | Matches the third-party large model call path, ensuring requests are correctly delivered to the target model |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification for large model calls, preventing unauthorized access |
Three Common Mistakes
- Phenomenon: Batch execution nodes fail to complete all cycles when called via API, but run normally during online debugging. Cause: Insufficient timeout parameters are configured for API calls, or the asynchronous callback mechanism for batch nodes is not enabled, resulting in request timeout interruptions.
- Phenomenon: The injection molded part appearance image cannot be recognized when calling the vision model. Cause: The
visionconfiguration item is not enabled, or image format data that meets the requirements is not passed, causing the model to fail to parse visual input. - Phenomenon: The requestid for large model calls cannot be queried. Cause: The platform's log recording configuration is not enabled, or the log storage path is not correctly configured, resulting in the requestid not being captured and stored.
How to Confirm the Configuration Is Correct
- Verify that the content filled in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches the configuration information of the third-party large model platform. - Initiate a single test call, check that the returned result contains the requestid field, and confirm that logs have correctly recorded call information.
- Upload an injection molded part appearance image and corresponding process records, verify that the model can correctly output defect analysis and parameter adjustment suggestions.
- Start the test process for batch execution nodes, confirm that both online debugging and API calls can complete all cycle steps.
The values provided are common starting points and should be evaluated against relevant samples.