What the data for this category looks like
Core documents for dairy processing include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling operation records, sample retention and microbial test reports. Data sources include dairy source testing equipment, production MES systems, online temperature and flow sensors for homogenization and heat treatment, CIP cleaning systems, and LIMS laboratory management platforms. Data is synchronized in real time per milk tank and per production batch. Continuous production of liquid biological raw materials requires uninterrupted data flow, and strict alignment with single-batch production cycles must be maintained.
What constraints these characteristics impose on the "model access and configuration" link
Real-time data per milk tank and per production batch requires model calls to match short-cycle, high-concurrency request rhythms to avoid delays disrupting continuous production processes. Multi-source heterogeneous data formats, including structured device values and unstructured paper-to-electronic test reports, require the configuration link to adapt to mixed-input parsing logic. The perishable nature of raw materials requires real-time exception report generation. The model context length must adapt to rapid integration of full-process data for a single batch, and invalid requests must be limited to reduce computing power consumption. In addition, compliance-related risks in dairy processing require model outputs to strictly match preset fields, avoiding omission of key test items or abnormal judgments.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the need to integrate multiple document contexts for the full single-batch production process, avoiding truncation of key raw milk testing or heat treatment parameters |
maxTokens | 2000–3000 token | Matches the generation length of a single exception report or process supplement document, adapting to the total data volume of a single dairy processing batch |
quoteMaxToken | 1500–2000 token | Limits the length of referenced source document fragments, ensuring the model can quickly locate core production fields such as raw milk antibiotic testing and filling temperature |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring consistency of process parameters and abnormal judgments, in line with production specification requirements |
AIPROXY_API_ENDPOINT | Fill in the actual access address according to the FastGPT v4.17.0 deployed instance | Adapts to the call link for external models or locally deployed models, ensuring requests can be properly routed to the target model service |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools, adapting to complex needs for multi-source data integration and abnormal judgment |
Three common configuration mistakes
- Empty results when calling local Ollama models. Cause: The Ollama service was not started synchronously with the FastGPT container, or network port mapping configuration was incorrect, preventing connection to the local Ollama instance.
- Unable to enable the image upload option after configuring an image model. Cause: The
visionrelated configuration items were not enabled correctly, or the deployed model itself does not support visual input capabilities. - Local vLLM model calls work normally after enabling AIPROXY, but Ollama model calls fail. Cause: The Ollama API path was not configured in the AIPROXY forwarding rules, or the listening port of the local Ollama service was not exposed publicly.
How to confirm the configuration is complete
- Enter the model testing page of FastGPT v4.17.0, input typical text from a single-batch raw milk acceptance record, trigger the model to generate process supplement descriptions, and check whether the output includes core raw milk testing fields.
- View the model call log to confirm that the
maxContextandmaxTokensparameters of the request match the configured values, with no abnormal truncation prompts. - Restart the FastGPT container and the local model service, then initiate a test request again to confirm that the result can be returned normally without null values.
- After configuring
AIPROXY_API_TOKEN, initiate an external model call request, and check whether the returned result meets the preset field format requirements.
The values given are common starting points and should be measured against the reader's own samples.