What Data Looks Like for This Category
Core data for this category is generated around compliance materials across the full ad account opening workflow. Daily processed documents include account opening application forms, business license scans, industry-specific qualification documents, and historical account opening audit records. Data originates primarily from ad platform backend systems, authorized public business credit databases, paper and electronic materials submitted offline by clients, and public industry access rules. Data updates trigger with account opening applications. The material set for a single application updates once during the application cycle. Overall daily update volume fluctuates with business volume. Fields requiring verification include the unified social credit identifier, industry permit number, material validity period, and others. No fixed, uniform field sorting rule exists.
Constraints Imposed on Model Integration and Configuration
The scattered sources of materials and trigger-only update rhythm for this category require model calls to be triggered on demand, to avoid unnecessary preloading. The mixed multi-type material structure, including structured text and unstructured scanned documents, requires configuration to support multimodal recognition model capabilities. Single applications have limited material volume but strict field verification logic. This requires limiting the context length of model outputs to avoid redundant information interfering with qualification verification. Audit failures directly lead to account opening failure or non-compliant ad delivery. This requires model outputs to include traceable reference bases. Configuration must reserve interface space for result verification. Additionally, access rules vary across industries. Models must have the ability to flexibly adapt to verification logic for different industries.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category processes unstructured scanned documents such as business licenses and industry qualifications. Multimodal capabilities accurately recognize text and field information in images |
maxContext | 8000–12000 token | The material collection for a single account opening application typically includes 3 to 5 documents. This range fully carries the context information of all materials, avoiding truncation of key qualification verification content |
temperature | 0.1–0.3 | Qualification verification must strictly follow rules. Lower temperature values reduce the probability of model hallucinations and ensure consistent results across different audit tasks |
quoteMaxToken | 1500 token | Sufficient length must be reserved for reference bases in model outputs, to fully display material sources and verification logic for subsequent traceability |
toolChoice | Fixed to none | This scenario does not require calling external tools. Only qualification verification based on input materials is needed, to avoid additional errors from tool calls |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths | This variable is a required environment variable for the App/Admin server. It points to the root address of the AI Proxy service. Do not enter single-model service addresses |
Three Common Misconfigurations
- Symptom: Model call returns "context too long" error with status code 413. Cause: The
maxContextparameter is not adjusted based on the material volume of a single account opening application, causing the threshold for maximum supported context of the model to be exceeded. - Symptom: GPU resource usage stays below 30% for extended periods, while single CPU core usage remains at 100%. Cause: GPU acceleration scheduling for multimodal models is not configured. Reasoning tasks are only processed by CPU cores, failing to fully utilize GPU computing power. This issue is common in deployment scenarios where model running hardware is not specified.
- Symptom: Contradictory judgments appear in qualification verification results output by the model. Cause: The
temperatureparameter is set too high, causing inconsistent model output logic across runs, and failing to ensure consistent audit standards.
How to Confirm Proper Configuration
- Upload a single typical set of account opening application materials, including business license scans and industry qualification documents. Trigger a model call, and check that key field information in images is correctly recognized.
- Check the App/Admin server startup logs. Confirm that the environment variables
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with AI Proxy server configuration and non-empty) are properly configured. Verify that service startup checks pass and no timeout errors appear in call links. - Adjust the
temperatureparameter to the 0.1 and 0.3 ranges, compare the two verification output results, and confirm that result consistency meets business requirements. - Test qualification materials from multiple different industries, check that the model can adapt to the judgment logic of access rules for different industries, with no obvious logical deviations.
The values listed here are common starting points. They should be evaluated against the sample data used in the target deployment environment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.