What Data for This Category Looks Like
Data for this category originates from the formula system, raw material batch management module, internal mixer, Mooney viscometer, vulcanizing machine, and laboratory testing equipment. Daily documentation includes formula sheets, weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold usage logs, and finished product performance reports. Data update frequency follows per raw material truck batch, per production batch, and per mold production cycle. Document fields include process parameters such as mixing temperature (unit: ℃), vulcanization time (unit: seconds), batch number, mold number, and others. The dataset includes both structured tables and unstructured curve charts.
Constraints Imposed on the "Model Access and Configuration" Workflow
The category data updates per raw material batch, production batch, and mold cycle. A single round of Q&A must cover multiple sets of process parameters, so the model context length must support recall of multi-batch data. Documents include unstructured content such as mixing curves and process cards, so the model must have image recognition capabilities. Production errors can lead to rubber compound scrap or unqualified product performance. Generated model content must have high rigor, so overly high randomness parameters are not recommended. Data sources cover both equipment and systems, requiring simultaneous processing of structured and unstructured inputs. Parsing and calling rules for multiple data types must be configured to ensure recalled content matches category-specific process details.
Configuration Settings
These configuration items are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category of data updates frequently, and a single round of Q&A needs to cover multiple batches of mixing and vulcanization process parameters, to avoid context truncation |
maxTokens | 4000–6000 token | Limit the output length of a single round of answers, avoid generating redundant content, and ensure answers are concise and accurate |
temperature | 0.1–0.3 | Equipment inspection and maintenance require accurate process parameters, reduce the randomness of generated content, and avoid deviation from actual production requirements |
vision | Enabled | This category of documents includes chart-containing content such as mixing curves and vulcanization process cards, requiring the model to identify parameter information in images |
| Workflow/API tool selection | auto | Allow the model to independently choose to call knowledge base plugins or directly generate inspection and maintenance plans, adapting to multiple types of question scenarios |
AIPROXY_API_ENDPOINT | Exclusive interface address corresponding to the deployed model | Ensure model requests are properly routed to the specified large model service |
AIPROXY_API_TOKEN | Exclusive key bound to the current application | Ensure the security of interface access and prevent unauthorized calls |
Three Common Misconfigurations
- Symptom: After enabling the knowledge base plugin's question optimization function, the knowledge base result rearrangement switch appears unavailable. Cause: The question optimization logic and context call parameters of the rearrangement model are not linked for verification, resulting in abnormal configuration status.
- Symptom: After integrating a text-image generation model, the generated output only includes text, with no image-related results. Cause: The
visionconfiguration item was not enabled correctly, so the model does not have enabled image input processing capabilities. - Symptom: After importing category documents with embedded images, the model cannot read process parameters or curve information from the images. Cause: The
visionparameter was not configured, and the image parsing function was not enabled during the knowledge base import process, so images were not synchronized to the model input context.
How to Confirm Successful Configuration
- Submit an equipment inspection question that includes a mixing curve image, and verify that the model can accurately identify temperature and time parameters in the image and generate corresponding maintenance suggestions.
- Adjust the
temperatureparameter range, verify that the rigor of the generated content meets process requirements, and confirm that the parameter takes effect. - Enable the knowledge base question optimization and result rearrangement functions, check that the switch status is normal with no abnormal markers.
- Send a test request using
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, confirm that the interface returns normally without errors.
The values provided are common starting points and should be evaluated against the reader's own samples.