What Data for This Category Looks Like
This category's data originates from advertising platform backends, the National Enterprise Credit Information Publicity System, customer-submitted account opening application materials and industry qualification documents, and public industry access rules. Data updates occur daily or when a single account opening application is submitted. Core documents include account opening application forms, scanned business licenses, corresponding industry operation qualification documents, and historical account opening audit records. Data fields include subject name, unified social credit code, applied advertising industry, qualification validity period, audit status, and more. No unified fixed units exist; values vary based on field attributes.
Constraints Imposed on Model Integration and Configuration
Data for this category is scattered across multiple sources, updates flexibly, and uses diverse document types. These factors create multiple constraints for model integration configuration. Multi-source data requires integration logic that supports multi-context stitching, to adapt to format differences across data sources such as advertising platform backends and publicity systems. Unstructured qualification scan documents require visual recognition support, to ensure the model can parse image-based materials. Update triggers tied to single applications require a dynamic context loading mechanism, to avoid loading expired historical data. The lack of unified field units requires the model to adapt to flexible field mapping rules, without hard-coded unit validation logic.
Configuration Guidelines
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 10000–15000 token | Adapts to the total length of multi-source audit materials, prevents context overflow |
maxTokens | 2000–3000 token | Meets output length requirements for audit conclusions and correction opinions |
vision | Enabled | Supports parsing unstructured image materials such as business licenses and qualification documents |
temperature | 0.2–0.4 | Reduces randomness, ensures consistency of audit judgments |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. Do not configure as a single-model address such as VLLM/Ollama/Xinference | Required environment variable for the App/Admin backend, specifies the AI Proxy service root address |
AIPROXY_API_TOKEN | Enter a non-empty administrator token matching the AI Proxy backend configuration | Required environment variable for the App/Admin backend, specifies the AI Proxy administrator token. Do not use application API keys, model vendor keys, or other types of tokens |
Three Common Configuration Mistakes
- If the model configuration page shows old body parameters after saving, compare the submitted settings, server response, saved result, and actual browser request. Page appearance alone does not establish a persistence or cache failure. Back up the configuration and reproduce each step against the deployed version.
- Phenomenon: After a previous model request fails, the process terminates directly with no clear error prompt. Cause: No exception capture node is configured, and node error interception logic is not enabled.
- Phenomenon: The model node cannot disable the thinking process, and can only control whether the thinking content is displayed in the result. Cause: The configuration only sets the result display switch, and does not associate the start/stop parameters of the thinking logic.
How to Confirm Proper Configuration
- Check the environment variable configuration of the App/Admin backend. Confirm that
AIPROXY_API_ENDPOINTis the AI Proxy service root address, andAIPROXY_API_TOKENis a non-empty administrator token matching the AI Proxy backend configuration. Missing required variables will cause service startup verification failure. - Submit test account opening application data to trigger the model node run. Confirm that image-based qualification materials can be parsed normally.
- Simulate a model request exception scenario, verify that the error capture process can be triggered and clear error messages are output.
- Adjust the
temperatureparameter, run tests multiple times, confirm that the consistency of output results meets expectations.
The values provided here are common starting points. They should be evaluated against relevant samples.
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.