What data belongs to this category
Data for this category originates from code repositories, dependency lists, license texts, and contribution records. Daily generated documents include LICENSE files, NOTICE files, dependency license lists, and copyright statements. Data updates trigger on each version release or each dependency change. A single data entry includes fields such as license type, copyright holder, dependency package name, and version number, with no additional custom units.
Constraints for Model Integration and Configuration
Data sources for this category are scattered. Update frequency rises with version and dependency changes. Document structures are fixed with clear fields, and this setup is compatible with FastGPT v4.17.0 model integration configuration logic. The model integration link must support directional pulling of multi-source data. Configurations must adapt to stability requirements for high-frequency calls. The fixed document field structure requires the model to accurately identify and verify core fields such as license type and copyright holder, to prevent compliance verification errors caused by field matching deviations. Multi-source data association verification needs sufficient context carrying capacity to avoid missing key associated information due to context truncation.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions, and do not fill in single-model addresses such as VLLM/Ollama/Xinference | Acts as the root address of the AI Proxy service to ensure the App/Admin service can correctly connect to the AI Proxy and avoid failed request delivery |
AIPROXY_API_TOKEN | Administrator token consistent with the AI Proxy server configuration, cannot be empty, and cannot be an application API Key, model vendor key, or token issued per application | Completes identity verification for the AI Proxy service to prevent unauthorized access |
maxContext | 8000–12000 token | Adapts to the context length of multi-source compliance documents to avoid truncation of key verification information |
maxTokens | 2000–3000 token | Controls the output length of a single round of compliance verification to ensure results are clear and complete |
temperature | 0.1–0.3 | Reduces output randomness to ensure accurate and consistent results for license compatibility checks |
toolChoice | auto | Allows the model to autonomously select appropriate verification logic to adapt to different types of compliance documents |
Three Common Misconfigurations
- Workbench edit page reports error "No language model configured". Troubleshooting steps include incorrect configuration of server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, orAIPROXY_API_TOKENnot matching the AI Proxy server configuration. - Model stream response is empty. Causes include
maxContextset too small leading to context truncation,maxTokensset too low to support complete output content, and in some scenarios,temperatureset too high leading to unexpected output format. - Doubao 1.6 model outputs thinking content but no main text. Causes include
maxTokenssetting not covering the token quota required for main text output, or deviation in context parameter configuration for model integration.
How to Verify Successful Configuration
- Check the server environment variable configuration of the App/Admin service, confirm that the values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the official guidelines. - Trigger a small-scale compliance verification task, check the response status in the model call log to confirm that a normal successful status code is returned.
- Check the model option list in the workflow, confirm that the target compliance verification model appears in the optional list.
- Manually input a test dependency license list, run the verification process, confirm that the model output includes core content such as license compatibility check and copyright ownership confirmation.
The values provided here are common starting points and should be evaluated against the deployed system’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.