What data for this category looks like
Data for this category primarily comes from package managers, code repositories, vulnerability databases, and license databases, with updates occurring daily or when a new version is released. Daily produced documents include SBOM lists, dependency risk reports, license lists, and repair tickets. Fields typically include dependency package names, version numbers, vulnerability CVE IDs, license types, risk levels, and more. These assets are maintained daily by security teams, developer efficiency teams, and open source governance offices. This guide is compatible with FastGPT v4.17.0.
What constraints do these characteristics impose on model access and configuration
Dependency asset identification, vulnerability version matching, and upgrade impact assessment are the most time-consuming steps for this category. Corresponding data volume varies significantly with project scale. SBOM lists and vulnerability reports have inconsistent field formats, so models must adapt to multi-source heterogeneous data. Frequently updated data requires model call response speeds to match the daily update rhythm. Models must also handle large numbers of dependency packages and vulnerability entries. Context length and output token limits must be adapted for batch analysis scenarios to avoid task interruptions caused by insufficient single-processing capacity.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Adapts to the batch data length of SBOM lists and vulnerability reports, avoiding context overflow |
maxTokens | 4000–8000 token | Meets the output requirements for single-batch dependency risk analysis, covering vulnerability descriptions and repair suggestions |
temperature | 0.1–0.3 | Reduces output randomness, ensuring consistency in vulnerability matching and risk rating |
AIPROXY_API_ENDPOINT | Fill in the AI Proxy service root address according to the deployment environment, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions | Required environment variable for the App/Admin server, specifying the AI Proxy service access address |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration. Cannot be empty | Required environment variable for the App/Admin server, used for AI Proxy service identity verification |
Three common mistakes
- Failing to locate the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables in the docker compose configuration after deployment. In the deployment template for FastGPT v4.17.0, some deployment scenarios do not include these environment variables by default. Corresponding parameters must be manually added to the compose file. - Model output text length being limited, preventing complete output of batch dependency analysis results. This occurs because the
maxTokensparameter is not adjusted to the value range suitable for batch analysis, and the default upper limit cannot cover the output requirements for multi-entry analysis. - App/Admin service startup verification failure. Troubleshooting steps: confirm that the docker compose configuration includes the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables, that the parameter values match the AI Proxy server configuration, and that the AI Proxy service starts normally.
How to confirm the configuration is correct
- Check whether the docker compose configuration file for the App/Admin service includes the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables, with parameter values matching the AI Proxy server configuration. - Initiate a small-scale single dependency analysis task, and verify whether the vulnerability rating and repair suggestions in the model output meet expectations, with no context overflow or truncation.
- View model call logs to confirm there are no error records for identity verification failures, timeouts, or parameter format errors.
- After adjusting the
maxContextandmaxTokensparameters, verify that batch analysis tasks can complete normally without interrupting mid-task.
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.