What the data for this category looks like
Typical data sources for this category include CDN consoles, DNS resolution records, access logs, and monitoring alerts. Daily generated documents include domain access forms, cache rule configurations, refresh and preload records, and fault analysis reports. Most fields relate to domain identifiers, cache rule IDs, refresh request volumes, and alert thresholds. Fields have no unified fixed units, and are mostly string or number types. Data update frequency is daily or real-time when configuration changes. Core documents focus on configuration and compliance verification for public network content distribution. This document is based on FastGPT v4.17.0.
What constraints these characteristics impose on model access and configuration
CDN category data primarily consists of configuration content such as domains and cache rules, with a high update frequency. This requires model access configuration to adapt to fast compliance review scenarios, such as quickly verifying domain ICP filings and checking cache rule compliance. Data sources are scattered across consoles and logs, so configuration must support interface permissions for multi-source data access. Most document fields are structured configuration items, so the model context length must fit the total number of fields in a single document. Configuration errors directly affect public network access, so model call stability and accuracy must match business availability requirements to prevent CDN service outages caused by model configuration mistakes.
How to set configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service (e.g., http://aiproxy:3000) | This is the access address for the AI Proxy service. Do not append API paths such as /v1 or /chat/completions, and it is not a single-model service address. |
AIPROXY_API_TOKEN | Administrator token configured for the AI Proxy server, consistent with server configuration and non-empty | Used for identity verification with the AI Proxy service, not a model vendor key or application API Key. |
maxContext | 8000–12000 token | Adapts to the total number of fields in CDN domain access forms and cache rule documents, avoiding context content truncation. |
maxTokens | 2000–3000 token | Balances output length and response speed for compliance reviews, meeting information needs for CDN fault troubleshooting. |
vision | false | Most CDN configuration documents are in text format, so no additional image recognition capability is required. |
temperature | 0.3–0.5 | Maintains rigor in review results, avoiding uncertain content that does not meet compliance requirements. |
Three common configuration errors
- When calling the model, it returns "unable to provide image content". The cause is that the
visionconfiguration item is not disabled, so the model incorrectly triggers multimodal recognition logic and cannot handle plain-text CDN configuration documents. - Connection timeout or 404 error occurs after configuration. Troubleshooting items include: whether
AIPROXY_API_ENDPOINTis the correct root address of the AI Proxy service, with no extra API paths appended and accessible normally; whetherAIPROXY_API_TOKENmatches the server configuration and is non-empty. - Cache rule review results have omissions. The cause is that the
maxContextvalue is too small, causing some domain ICP filing information and cache rule details to be truncated, so the model cannot fully meet compliance check requirements.
How to confirm the configuration is complete
- Complete text content of a CDN domain access form is input, the model is called for compliance review, and the returned result is checked for relevant conclusions about filing verification and cache rule matching.
- FastGPT model call logs are reviewed, and it is confirmed that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations do not trigger permission errors or connection errors. - Values of
maxContextandmaxTokensare adjusted, CDN documents of different lengths are tested as input, and it is confirmed that no context truncation prompt appears in the output. - The
visionconfiguration item is disabled, input scenarios containing images are tested, and it is confirmed that the model does not return error prompts related to image recognition.
The values provided here 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.