What this category of data looks like
The data for this category focuses on creator onboarding and identity verification. Daily documents include onboarding application forms, scanned identity qualification materials, audit review circulation records, cooperation framework agreements, and more. Data sources include account information submitted via the creator backend, entity qualifications obtained through enterprise credit information disclosure channels, historical account behavior logs, and cooperation term files stored in contract systems. Data updates are triggered by the application process; the full update frequency for a single application is daily or after each audit node is completed. Data fields include application unique identifier, creator account ID, qualification type, audit status, associated agreement number, and other fields. There are no unified fixed batch statistics dimensions.
What constraints these characteristics impose on the model integration and configuration link
The requirement for multi-source data access means model configurations must support adaptation to different input formats. Structured account data from the creator backend, public text from enterprise credit information, and historical account behavior logs each require adaptation to different field splitting and encoding rules. The update rhythm triggered by application nodes means model calls must support on-demand triggering, with parameters configured to meet the processing time requirements of single requests. For core tasks such as qualification verification and risk judgment, the model’s output format and confidence threshold must be adjusted to meet business requirements for identity entity extraction and prohibited text classification. The non-standardized characteristics of data fields require configurations to support custom field mapping parameters, to avoid model input exceptions caused by field differences.
How to set the configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| AIPROXYAPIENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions | This variable is the AI Proxy communication endpoint configured by the FastGPT server, not the address of a single model service |
| AIPROXYAPITOKEN | Enter a non-empty administrator token consistent with the configuration of the AI Proxy server | This variable is the AI Proxy authentication credential configured by the FastGPT server, not the model service provider’s key or application API Key |
| maxTokens | 8000–12000 token | Covers the combined text length of onboarding applications, qualification materials, and audit records, to avoid input content being truncated |
| temperature | 0.2–0.4 | Reduces random output during qualification verification, improving consistency of identity judgments |
| maxContext | 10 historical audit records | Matches the context length of daily audit circulation, avoiding redundant data occupying model context resources |
| toolChoice | auto | Supports on-demand calling of entity extraction tools, adapting to multi-field identity verification requirements |
Three Common Misconfigurations
- After enabling a specified embedding model and clicking test, a 404 status code (no body) is returned. Troubleshooting steps: Confirm that the FastGPT server has correctly configured AIPROXYAPIENDPOINT (AI Proxy service root address) and a non-empty AIPROXYAPITOKEN, and that the AI Proxy service is running normally.
- After commercial edition deployment, only reranking models are displayed in the channel model list. Troubleshooting steps: Confirm that the FastGPT server has correctly configured AIPROXYAPIENDPOINT and a non-empty AIPROXYAPITOKEN, and that service startup verification passed.
- After resetting the oneapi username and password, model calls continue to fail. Troubleshooting steps: Confirm that the AIPROXYAPITOKEN environment variable of the FastGPT server has not been accidentally modified, and that it matches the configuration of the AI Proxy server.
How to confirm the configuration is complete
- Enter the model configuration test page, enter the sample text of a creator onboarding application, click test call, and confirm that the returned result has no errors and includes the expected qualification verification fields.
- View the channel model list, confirm that the target language model is displayed in the available list and can be selected and called normally.
- After configuration is complete, submit a test onboarding application to trigger model calls, and check whether the model output in the audit record conforms to the business logic of identity verification.
- Adjust the temperature parameter, call the same test text multiple times, and confirm that the consistency of the output results meets business requirements.
The values provided are common starting points and should be measured against the reader's own 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.