What data looks like for this category
Data for this category includes quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, microbial test reports, and more. Data sources include animal quarantine information, supplier batch ledgers, production traceability systems, temperature collection devices, metal detection devices, and laboratory test results. Updates trigger based on production batch and shift frequency. Cold chain temperature records are collected continuously. Document fields include batch number, quarantine date, temperature threshold, microbial test indicators, workstation number, and others. Some fields have units: temperature uses degrees Celsius, weight uses kilograms.
Constraints for model access and configuration
This category’s data links across multiple stages and has strict batch binding requirements. Batch alignment logic for multi-source data must be adapted during model access. Continuously collected cold chain temperature data generates high-frequency incremental content. Limit the model’s context processing length to avoid overload. Drafting exception reports requires associating multiple document types such as quarantine certificates and production records. The model’s context window must support cross-document information integration. Some fields have clear units and compliance identifiers. The model must accurately identify field attributes to avoid confusing key parameter value logic such as raw/cooked zone divisions and temperature thresholds.
How to set configurations
The following configurations are defined for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to cross-document integration needs for multi-batch production records and quarantine certificates, avoiding loss of critical batch association information due to context overflow |
maxTokens | 1500–2000 characters | Matches the drafting length of a single exception report or process document, preventing output content from exceeding business document format requirements |
temperature | 0.2–0.4 | Reduces generation randomness to ensure accurate values for compliance fields such as quarantine identifiers and temperature thresholds |
vision | Enabled | Supports parsing image attachments in quarantine certificates and test reports, aligning with the proportion of image formats in documents for this category |
AIPROXY_API_ENDPOINT | Local deployment node port 11434 address | Adapts to common local model deployment scenarios for enterprises in this category, ensuring normal connectivity of model services |
AIPROXY_API_TOKEN | Application-specific key | Differentiates access permissions for production and test environments, ensuring access security for business data |
Three common configuration errors
- Incorrect port or address entered when configuring
AIPROXY_API_ENDPOINT, resulting in connection timeout errors (status code 504 or ECONNREFUSED). This happens when the actual listening port of the locally deployed model is not correctly matched, or network access permissions for the target port are not enabled. - After enabling the
visionfunction and uploading a quarantine certificate image, corresponding fields in the returned result are empty. This occurs when the model’s visual capability dependencies are not correctly configured, or the uploaded image format falls outside the model’s supported range. - Using a universal key to call the model for process document generation, with the output failing to associate quarantine information for the target batch. This happens when the application-specific key for the corresponding production environment is not used, so the permission scope cannot access batch-associated business data.
How to verify successful configuration
- Call the model interface to test text extraction from a single quarantine certificate. Verify that the returned result includes key fields such as batch number and quarantine date. Field completeness is aligned with mandatory item requirements for business documents.
- Input bulk continuous temperature control record data. Check that the model’s context processing output is complete, with no content truncation or loss.
- Generate a draft of a single exception report. Verify that the generated content links production records and test data for the corresponding batch, with field units matching business requirements.
- Test the
visionfunction by uploading a quarantine certificate image attachment. Confirm that the returned result accurately extracts text and identifier information from the image.
The values provided are common starting points and should be evaluated against the reader’s own samples.