What this category's data looks like
Core data sources for this category include animal quarantine information, supplier batches, production traceability records, temperature collection data, metal detection results, and laboratory test reports. Daily documents cover quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, and microbial inspection documents. Data is updated per production batch and shift. Continuous temperature control data is collected in real time. Fields include batch number, quarantine qualification mark, processing temperature, detection threshold, and cold chain point number. Common units include degrees Celsius, kilograms, batch numbers, and similar units.
What constraints these characteristics impose on model access and configuration
This category’s data includes biosecurity-related quarantine markers, real-time temperature control time-series data, and has strict batch association requirements. Model access must prioritize accurate matching of batch fields first. Real-time temperature control data updates at high frequency. Read logic adapted for high-frequency, small-batch data must be configured. In scenarios with mixed multiple document types, limit the total amount of single-batch data processed by the model to avoid context overflow. Compliance documents such as quarantine certificates have highly fixed fields. Parsing logic adapted for structured data is required to reduce errors from unstructured parsing.
How to set the configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to mixed context of multi-batch quarantine and production records, avoids exceeding model context limits |
maxTokens | 2000–3000 token | Ensures sufficient detail in defect analysis results while controlling single-request latency |
quoteMaxToken | 1500 characters | Limits the length of referenced production documents, matches the average length of single-batch documents for this category |
| Workflow/API tool selection | auto | Automatically selects whether to call retrieval tools, adapts to associated analysis needs for multi-source data |
temperature | 0.2–0.4 | Reduces result randomness, ensures consistency of quality traceability conclusions |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to local or cloud model call paths, ensures model access stability |
AIPROXY_API_TOKEN | Configure according to authorization | Ensures permission security for model calls, prevents unauthorized access |
Three common misconfigurations
- Phenomenon: After uploading a quarantine certificate, the returned traceability result does not associate with the corresponding batch’s production records. Cause: No accurate batch number matching rule is configured, so the model cannot establish a connection between raw material batches and production information.
- Phenomenon: An error occurs when parsing cold chain temperature logs when calling the deployed model. Cause:
maxContextis not adjusted to adapt to this category’s high-frequency time-series data, causing the request to exceed the model’s context limit. - Phenomenon: No traceability results are generated after configuring a custom knowledge base. Cause: Automatic calling of associated retrieval is not enabled, or
quoteMaxTokenis not configured to adapt to this category’s document length.
How to confirm the configuration is correct
- Upload a single batch’s quarantine certificate and slaughter and cutting records, check whether the traceability result returned by the model includes all associated documents for the corresponding batch.
- Submit a query containing the keyword for microbial overlimit, check whether the analysis conclusion in the returned result conforms to the compliance logic for this category.
- View the model call log, confirm that the configurations for
maxContextandmaxTokensdo not trigger context overflow errors. - Test queries for different temperature ranges, check whether the returned temperature control data matches the actual collected logs.
The values provided here are common starting points, and should be evaluated against the user’s own samples.