This document is written based on FastGPT v4.17.0.
What This Category of Data Looks Like
Data for this category comes from recipe systems, raw material batch records, internal mixers, Mooney viscometers and rheometers, vulcanizing presses, and laboratory testing equipment. Daily documents include recipe sheets, weighing records, mixing curves, rubber quick-test reports, vulcanization process cards, mold ledgers, and finished product performance reports. Data updates are generated synchronously with each truckload of rubber, each batch of raw materials, and each production cycle. Each individual document includes precise feeding weights, process parameters, and test values. Most fields use dedicated units such as kilograms, degrees Celsius, and minutes.
What Constraints Do These Characteristics Impose on Model Access and Configuration
This category of data has high update frequency and dense parameters per record. Model access must adapt to short-cycle documents with frequent updates, while also handling long-form content with multi-dimensional process parameters. Since data is linked to the entire production process, cross-review requires aligning parameter consistency across multiple source documents. Configuration must prioritize the accuracy and timeliness of context recall. The irreversible cross-linking nature of this category’s processes means production errors cannot be reworked. Configuration must strengthen the rigor of parameter verification to avoid batch scrap caused by model output deviations. Multi-source and heterogeneous document formats require configuration to support parsing of both text and visual data.
How to Set the Configuration
| Config Item | Recommended Value | Basis for This Value |
|---|---|---|
maxContext | 8000–12000 token | The total token count of a single associated document group (recipe + mixing + vulcanization records) for this category usually falls within this range, ensuring complete recall of historical data required for review |
maxTokens | 4000–6000 token | Cross-review requires comparing parameters across multiple documents simultaneously, avoiding truncation of critical process values due to token overflow |
quoteMaxToken | 1500 token/entry | The core parameter section of a single process document (such as a vulcanization process card) has a concentrated length, and accurate interception can improve review accuracy |
temperature | 0.1–0.3 | Cross-review requires strict alignment of parameter consistency, and high randomness output should not be used |
vision | Enabled | This category includes visual data such as mixing curves and test report images, requiring visual models to parse chart values |
AIPROXY_API_ENDPOINT | Fill in the dedicated address provided by the deployment service provider | Adapt to the network environment requirements of domestic or overseas model calls, ensuring interface connectivity |
Three Common Mistakes
- Symptom: A token overflow error occurs even after setting a large
maxTokensvalue, and some documents cannot complete review. Cause: ThemaxContextparameter was not adjusted synchronously, and the total token count of context recall exceeds the model's upper limit. - Symptom: Some mixing curve image uploads succeed while others fail, and the interface returns a parameter error prompt. Cause: The
visionconfiguration was not enabled, or the uploaded image resolution exceeds the range supported by the model. - Symptom: The review result parameters output by the model have large deviations and do not match actual process requirements. Cause: The
temperaturevalue was set too high, causing the model's output randomness to exceed the rigor required for review.
How to Confirm the Configuration Is Correct
- Import a single complete recipe sheet and corresponding mixing record, check whether the model can recall all field parameters completely without truncation.
- Upload a mixing curve image, verify that the model can correctly identify the temperature and time values in the chart without recognition failures.
- Initiate a cross-review task, and check that the parameter comparison result output by the model matches the original document.
- View the interface call log, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct, and there are no connection timeout errors.
The values provided here are common starting points and should be evaluated against the reader's own samples.