What the data for this category looks like
This category of data covers the full production flow for injection molded parts. Daily documents include injection molding process cards, raw material drying records, mold histories, first-piece inspections, patrol logs, and material change and machine cleaning records. Data sources include resin batch and property sheets, injection molding machine parameters, mold temperature controllers, MES systems, and dimensional and appearance inspection results. Data updates are triggered by production batches, mold and material change operations, and shift production schedules. Different documents correspond to fields such as injection molding parameters, raw material indicators, mold status, and inspection results. Units cover standard injection molding industry units including temperature, pressure, time, and weight.
Constraints imposed by these characteristics during model access and configuration
Data update frequency is tied to production batches, mold changes, material changes, and shifts. This requires model access configurations to support real-time or near-real-time data synchronization, and avoid using expired production parameters. Differences in fields across multiple document types require clear field mapping rules for different data sources in the configuration, to prevent parameter confusion. The injection molding industry has high precision requirements for parameters such as raw material drying and recycled material ratios. Model calls must retain the original units and precision of data, and must not perform arbitrary conversions. Inspection data from different production links must be associated with corresponding batches. Configurations must support associating document content by batch dimension, to improve the accuracy of bid documents and quotations. The configuration logic of FastGPT v4.17.0 adapts to the association requirements of this type of multi-source data.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate multi-document content such as multi-batch injection molding processes and mold histories, to avoid context truncation |
maxTokens | 4000–6000 token | Bid documents and quotation preparation require complete parameter descriptions and quotation details. Control generation length to avoid redundancy |
quoteMaxToken | 1500–2000 token | Must reference long document fragments such as injection molding process cards and raw material property sheets, to retain sufficient integrity of referenced content |
temperature | 0.2–0.4 | Bid documents require strict accuracy. Reduce randomness to ensure consistency between generated content and production data |
AIPROXY_API_ENDPOINT | Fill in based on actual deployment | Adapt to proxy addresses for different environments, and ensure normal model call access |
AIPROXY_API_TOKEN | Fill in based on authorization key | Complete identity verification for the proxy channel, and ensure call permissions |
Three common configuration mistakes
- Symptom: Call returns code 500 Cannot read p. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, leading to proxy channel identity verification or address resolution failure. - Symptom: Model test returns 400 error. Cause:
maxContextvalue exceeds the context limit supported by the model, or parameter format exception caused by unmatched field mapping rules for injection molding data. - Symptom: Adding a specified model channel test returns 404 page not found. Cause: The proxy service pointed to by
AIPROXY_API_ENDPOINTdoes not deploy the corresponding model interface, or the model identifier in the channel configuration does not match the proxy service.
How to confirm the configuration is complete
- Perform a basic test of the proxy interface, confirm the returned results meet expectations, and adjust
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENuntil the test passes. - Import a single injection molding process card and raw material property sheet, test model calls, confirm the generated content includes correct parameter fields and units, and adjust
temperatureto the range that meets strictness requirements. - Import multi-batch production data, test context loading, confirm
maxContextvalue does not trigger context truncation, and adjust the value to cover the total token count of all imported documents. - Simulate the bid document generation process, confirm that referenced injection molding inspection data and mold histories are accurately associated, and adjust
quoteMaxTokento cover the length of required reference fragments.
The values provided are common starting points, and should be evaluated against local production samples.