What This Category's Data Looks Like
Data for this category originates from resin batches and property sheets, injection molding machine parameters, mold temperature controllers, manufacturing execution systems (MES), and dimensional and visual inspection equipment. Daily retained documents include injection molding process cards, raw material drying records, mold history logs, first article inspection reports, patrol inspection records, and material change and machine cleaning records.
Updates occur every production batch, each time a mold or material is changed, and after each shift's patrol inspection. Fields include process parameters, raw material batch numbers, mold IDs, inspection dimensional and visual results, and more. Some data is structured data exported directly from connected equipment, while other data exists as unstructured documents converted from paper to electronic format.
Constraints for Model Integration and Configuration
The multi-source, heterogeneous data characteristics of this category require that model integration must support mixed input of structured equipment parameters, semi-structured inspection records, and unstructured paper-converted documents. Frequently updated production data requires limiting the context window size during configuration to avoid redundant old data interfering with current audit logic.
Professional injection molding-related fields such as filling and holding pressure parameters, mold temperature, and recycled material ratio require the configured model to have the ability to recognize injection molding industry terminology. In addition, the audit data volume for a single production batch is concentrated, so the workflow must adapt to the rapid parsing requirement for batch data.
Configuration Settings
This configuration applies to FastGPT v4.17.0. Specific configuration items and their rationales are listed below:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single-batch audit data for this category includes injection molding process cards, patrol inspection records, mold history logs, and more. The total token volume falls within this range, avoiding context overflow that disrupts parsing |
temperature | 0.1–0.3 | Supplier and process quality audits require consistent, stable parameter judgments. This temperature range reduces deviations in audit results caused by random fluctuations |
vision | Enabled | Audits for this category require parsing visual documents such as injection molded part appearance defect images and mold photos, adapting to image parsing requirements |
maxTokens | 2000–3000 token | The output length of a single audit conclusion must be controlled within this range to ensure clear and complete results, matching the information density required for business audits |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Ensures compliance of model interface calls and avoids cross-region access restrictions |
AIPROXY_API_TOKEN | Fill in the assigned dedicated secret key | Verifies identity legitimacy for interface calls and prevents unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: After configuring the
visionparameter, uploaded images or images embedded in PDFs still cannot be parsed, and the audit result does not include content related to the images. Cause: The associated configuration of the visual model was not correctly enabled, or the uploaded image format is not supported, such as non-JPG/PNG formats. - Phenomenon: A 403 status code is returned when calling the model, and the interface call fails. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect or the secret key has expired, and the dedicated interface secret key was not filled in according to deployment requirements. - Phenomenon: When batch importing multiple batches of injection molding data, field missing or misalignment occurs in the parsing results. Cause: The
maxContextvalue is too small to accommodate the complete multi-document data of a single batch, resulting in truncated and lost field information.
How to Verify a Successful Configuration
- Upload a single injection molding process card and supporting appearance defect images, verify whether the model can correctly recognize the defect annotations in the images and the process parameter text.
- Initiate a batch audit test, check whether the interface return result completely includes the key information of all uploaded documents, with no field missing or misalignment.
- Verify the configuration content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto ensure they match the assigned dedicated interface information. - Adjust the
temperatureparameter to different ranges, compare the differences between the two audit conclusions, and confirm that the output stability meets the business audit requirements.
The values provided are common starting points and should be measured against the reader's own samples.