What Data Looks Like for This Category
Data for this category primarily comes from animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports.
Data updates run per production batch and work shift. Continuous temperature control data is synchronized in real time. Data fields include batch number, quarantine number, temperature value, test time, test results, and additional relevant fields. Units include degrees Celsius, batch numbers, test indicator names, and other standard units.
Constraints During Model Access and Configuration
Batch-bound data for this category requires the model’s context to cover complete single-batch associated records. This prevents mismatches between batches and quarantine information.
The real-time nature of continuous temperature control data requires the model to support fast parsing of time-series data. Set reasonable upper limits for context loading parameters.
Scenarios with mixed input of multiple document types require the model to support both text and visual document processing. Enable visual recognition capabilities.
High-risk inspection audit demands require high consistency in model audit outputs. Reduce randomness in generated results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | A single batch has many associated documents, requiring coverage of complete quarantine, production, and test records |
temperature | 0.1–0.3 | Inspection and audits require high accuracy, reducing randomness in generated results |
AIPROXY_API_ENDPOINT | Fill in the access address of the OneAPI proxy | Connect to third-party model proxy services to enable cross-platform model calls |
AIPROXY_API_TOKEN | Fill in the valid token generated by OneAPI | Complete identity verification for the proxy service to obtain model call permissions |
vision | Enable | Recognize visual document content such as quarantine certificates and temperature record screenshots |
maxTokens | 2000–3000 token | Meet the output length requirements for a single audit report |
Three Common Misconfigurations
- After configuring a reranking model, a prompt indicates no available models. The cause is missing API key and access address configuration for the corresponding reranking model in the OneAPI platform, preventing FastGPT from accessing model resources.
- After entering the OneAPI token, an empty result is returned. The cause is either
AIPROXY_API_ENDPOINTnot correctly pointing to the OneAPI proxy address, or the token lacking call permissions for the target model. - The model audit result omits some batch-associated records. The cause is an overly small
maxContextvalue, which fails to cover complete single-batch associated documents and leads to loss of key information.
How to Verify Successful Configuration
The following verification steps are performed based on FastGPT v4.17.0.
- Check that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTvalues match actual parameters in the OneAPI backend. Initiate a test call to confirm the return status code is 200. - Upload quarantine certificates and cold chain temperature records for a single batch to trigger an audit task. Confirm the model correctly extracts the batch number, quarantine number, and temperature data.
- Adjust the
temperatureparameter value. Compare the consistency of two audit results to confirm alignment with inspection audit accuracy requirements. - Upload an image file of a quarantine certificate to trigger a visual recognition task. Confirm the model correctly identifies key text information in the image.
The values provided are common starting points and should be measured against the reader's own samples.