This document applies to FastGPT v4.17.0.
Data profile for this category
Daily documents for this category include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, microbial test reports, and more. Data sources include animal quarantine information, supplier batches, production traceability systems, temperature collection devices, metal detection devices, and lab test results. Data updates are performed per batch and per shift. Cold chain temperature logs update continuously. Document fields include batch number, quarantine ID, temperature value (unit ℃), test results, processing time, and more. Field structures vary across different documents. Some records use structured numerical values, while others use text descriptions.
Constraints for model integration and configuration
This category’s data updates per batch and shift, and includes continuously collected temperature control data. Model integration must support small-batch, high-frequency data processing. Multiple document types have widely varying field structures. Configuration must adapt to document extraction and association across different formats. Data includes sensitive information related to biological risks. Access configuration must include permission isolation capabilities. Associating batches with quarantine certificates is a core time-consuming step. The model must quickly match associated fields to avoid delays from overly long context processing.
How to set configuration values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category’s single-batch review requires associating multiple documents, so the context must hold complete batch traceability context |
temperature | 0.1–0.3 | Cross-review of technical documents requires high consistency, to avoid incorrect association or extraction results |
| Workflow/API tool selection | auto | Automatic document extraction tool calls are needed to handle structured and unstructured production records |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Ensure model requests are routed to the correct service node, to avoid access exceptions |
AIPROXY_API_TOKEN | Bind dedicated access keys | Isolate document access permissions across production batches, to protect sensitive production data |
maxTokens | 1500–2000 token | The output content for single-batch review must be controlled in length, to ensure processing speed and readability |
Three common configuration errors
- Symptom: Returns an error of the form
'KokoroModel' object has no attribute 'mode'. Cause:AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, leading to abnormal model access link. - Symptom: Knowledge base retrieval prompts "no matching content". Cause:
maxContextis set too small, failing to hold complete batch-associated documents, so the model cannot associate quarantine certificates with production records. - Symptom: Extracted temperature values from cold chain temperature logs return empty. Cause: Correct extraction rules are not configured for numerical fields with units, so the model cannot recognize numerical content following the
℃unit.
How to confirm proper configuration
- Run an association test for single-batch documents. Check whether the batch and quarantine certificate correspondence returned by the model matches actual business logic. Adjust relevant parameters until results match.
- Call the test interface with temperature records in abnormal formats. Confirm the model can correctly extract numerical values and units. Verify that the
toolChoiceconfiguration takes effect. - Check access logs. Confirm
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no 401 or 404 access errors. - Import cold chain records from three consecutive shifts in bulk. Confirm model processing time meets business expectations. Adjust the
maxTokensparameter to balance processing speed and output completeness.
The values provided are common starting points, and should be evaluated against the reader’s own samples.