What the data for this category looks like
Daily documents for beverage and bottled water production include recipe sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspections, and finished product inspections. Data sources cover water source and raw material testing, recipe systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection equipment, and LIMS systems. Data is updated per production batch, generated per batch, shift, or through continuous collection. Single-batch associated documents include fields such as raw material test values, process parameters, equipment operation data, and test results. These fields include feeding amount, sugar and acidity values, filling liquid level, sealing pass rate, label batch number, and more.
Constraints imposed on the "model integration and configuration" link
Since data is updated per production batch and covers multiple types of process and inspection documents, model calls must adapt to integrated review of full-process documents for a single batch. Configure appropriate context length to cover all associated document content. Documents contain sensitive recipe parameters and aseptic process data. Ensure the security of the API call chain. The model must have high accuracy to avoid compliance judgment deviations. Mixed structured and unstructured data from multiple sources requires the model to recognize field rules for different document types. Configure appropriate parameters to balance flexibility and accuracy. Continuously collected real-time data requires the model to support reasonable response times to avoid affecting review efficiency in production links.
How to Set Configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | The total character count of single-batch associated documents, converted to tokens, usually falls within this range. It covers full-process document content including recipes, water treatment, filling, and more |
maxTokens | 2000–3000 token | The output result for single-batch review must be detailed enough to cover all compliance check points, while avoiding excessive response delays |
temperature | 0.1–0.3 | Technical document review requires high accuracy. This parameter range reduces the randomness of generated results, aligning with the rigor requirements of compliance judgments |
AIPROXY_API_ENDPOINT | Official API access address corresponding to the model provider | Ensure the API call path matches the requirements of the model provider, to avoid 404 path errors |
AIPROXY_API_TOKEN | Valid access key obtained from the model provider's backend | Complete identity verification for API calls, ensuring the legality and permissions of model integration |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call tools to process structured equipment data and inspection records, improving review efficiency |
Three Common Mistakes
- 404 error during model testing: Phenomenon: A 404 error pops up when testing a newly added model in the FastGPT model management page. Cause: The
AIPROXY_API_ENDPOINTconfiguration path does not match the actual access address of the model provider, or the model ID filled in does not match the format required by the provider. - Api response error log: Phenomenon: The FastGPT container backend returns an error message containing
/api/core/ai/model/test. Cause: ThemaxTokensconfiguration value exceeds the maximum output token limit of the used model, or no reasonable wait time is set for API call timeout. - Unable to switch AI models in the chat dialog box: Phenomenon: Other configured models cannot be selected in the chat interface. Cause: Independent
AIPROXY_API_TOKENis not configured for each model, or relevant configuration for multi-model support is not enabled in the model management page.
How to Confirm Configuration Is Complete
- Enter the model management page of FastGPT v4.17.0, select the configured target model, click the test button, and confirm that a normal model response is returned without 404 or timeout errors.
- Upload single-batch beverage production associated documents (including recipe sheets, water treatment records, filling records), initiate a review request, and confirm that the model can recognize key fields in the documents and generate compliant review results.
- Try to switch multiple configured models in the chat dialog box, confirm that the switching operation can be completed normally, and the review results returned by different models meet expectations.
- Check the backend running logs of the FastGPT container, confirm that there are no abnormal errors related to model calls, and the API call chain is normal.
The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.