What this category’s data looks like
Core data for this category comes from animal quarantine information, supplier batch records, production traceability ledgers, real-time temperature collection, metal detection results, and laboratory microbial test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient curing logs, thermal processing parameter records, cold chain temperature ledgers, and microbial test documents. Data updates follow production batch and shift schedules; cold chain temperature data requires continuous collection and updates. Data fields include batch number, quarantine number, temperature value, detection indicators, operator number, and others. Some fields must match batch and quarantine information, with strict association verification requirements.
What constraints these characteristics impose on model access and configuration
The batch association, continuous temperature control data, and multi-type document structure of this category create multiple constraints for model access and configuration. Per requirements for batch-based and real-time updated data, configure parameters adapted for batch document parsing and real-time data docking to avoid single-processing overload. Multiple document formats (paper scans, electronic ledgers, sensor export files) require the model to support diverse content extraction. Strong association verification for fields such as quarantine, temperature, and microbial detection also requires the model to have accurate entity matching capabilities. Limit the context length of single calls to ensure the accuracy of association parsing. Sensitive biosafety-related data requires securing the call link to avoid data leakage.
How to set configurations
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multi-batch associated quarantine and production records, avoiding truncation of key associated information |
maxTokens | 4000–6000 token | Ensures the output length of single-batch document parsing and verification results, covering complete detection and record content |
temperature | 0.1–0.3 | Ensures the accuracy of field extraction and association verification, reducing interference from randomly generated content on business verification |
quoteMaxToken | 1500–2000 token | Ensures that referenced original document fragments sufficiently cover key information related to quarantine and batch association |
AIPROXY_API_ENDPOINT | Fill in a compliant access address | Adapts to the security call requirements for sensitive data of this category; local deployment links can be selected to avoid data transmission risks |
| Workflow/API tool selection | auto | Supports automatic tool calls to complete batch association verification and field extraction, adapting to the processing needs of multiple document types |
Three common mistakes
- Phenomenon: Empty values are returned when the model is called to extract key fields from quarantine certificates and batch records. Cause: Context parameters adapted for long document segmentation are not configured, resulting in truncation of document fragments containing key fields.
- Phenomenon: When accessing a locally deployed model, the interface shows that the index status has not changed for a long time or returns an error. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the interface of the locally deployed model does not have normal access permissions enabled. - Phenomenon: When accessing the DeepSeek-R1 series models, the generated reasoning content is not correctly identified, resulting in missing reasoning basis for audit results. Cause: The label format of the model output is not adjusted as required, causing the platform to fail to parse reasoning-related content.
How to verify a successful configuration
- Upload a complete quarantine certificate and the corresponding batch production records, trigger model extraction and verification, and check whether the returned fields cover all mandatory business items.
- View the model call log to confirm that parameters such as
maxContextandmaxTokensin the interface request match the configured values, with no abnormal truncation. - Test the access link of the locally deployed model to confirm that the interface returns a normal status and the content meets business expectations.
- Simulate a batch document upload scenario, check whether the system can complete association verification by batch without timeouts or abnormal errors.
The values given are common starting points and should be measured against the reader's own samples.