What this category's data looks like
The data for this category mainly comes from daily production documents of slaughterhouses, cooked meat and frozen prepared food enterprises, including quarantine certificates, slaughter and cutting records, ingredient marination records, thermal processing records, cold chain temperature records, and microbial test reports. Data sources cover animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection results, and laboratory test data. Update frequency follows production batches and shifts. Cold chain temperature records are collected continuously. Document fields include batch numbers, quarantine numbers, temperature values, test indicators, and other related content. Some data requires matching with raw material batches and quarantine information.
What constraints these characteristics impose on the "model access and configuration" link
The data includes biosecurity-related quarantine information and real-time temperature control data. Strictly limit the context scope during model access to avoid unrelated data leaks or incorrect associations. The strong binding requirement between batches and quarantine information requires ensuring that the batch fields of retrieval results match accurately during configuration. Continuously collected temperature control data has a high update frequency, so limit the number of documents and context length retrieved in a single session to avoid loading expired or redundant data. The control requirements for raw and cooked product zoning and sterilization temperature require the configured model to accurately identify temperature thresholds and operating specifications, so adjust model parameters to adapt to the understanding accuracy of professional terms.
How to set the configuration
This document is compatible with FastGPT v4.17.0. The following configuration items are based on the interface and parameter logic of this version.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category of documents includes quarantine and production data associated with multiple batches, so retain sufficient context to ensure accurate matching of batch and quarantine information, and meet the information needs of the business scenario |
temperature | 0.1–0.3 | Q&A for process specifications and technical standards requires strict matching with standards to avoid generating content that deviates from standards, ensuring the accuracy of production operations |
quoteMaxToken | 2000–3000 token | Retain sufficient original document fragments to corroborate key data such as quarantine and temperature control, ensuring the traceability of responses |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Ensure network stability for model calls, and adapt to internal enterprise networks and security policies |
AIPROXY_API_TOKEN | Bind the dedicated enterprise call key | Restrict unauthorized model calls to protect the security of production data and business processes |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call the retrieval tool to obtain process and standard data, adapting to information acquisition needs in different scenarios |
Three common mistakes
- Phenomenon: The interface displays "No available channel for model gpt-4o-mini under current group default". Cause:
AIPROXY_API_TOKENor access endpoint has not been configured under the current group, resulting in uncompleted binding of model call permissions. - Phenomenon: Retrieved temperature control data does not match the current production batch. Cause: The scope of
maxContexthas not been limited, causing redundant data from old batches to be recalled first and interfering with the model's matching logic. - Phenomenon: The results of application calls are directly used as final output, and are not fed to the large model as retrieval materials. Cause: The application's call link has not been correctly configured, and the result output node and retrieval node are not separated.
How to confirm the configuration is complete
- Run a model call test, input process-related questions, check whether content that meets enterprise standards can be returned normally, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Retrieve quarantine certificates and production records for a specific batch, check whether the batch fields of the recalled results match the retrieval keywords, and confirm that the context scope restriction takes effect.
- Adjust the
temperatureparameter and compare the generated results to ensure that the professional terms in the generated content are consistent with enterprise standards, and confirm that the parameters adapt to business needs. - Check the system logs to confirm that there are no errors in model call requests, and that the number of retrieved documents meets the expectations of the business scenario.
The values provided are common starting points and should be evaluated against applicable internal samples.