What data for this category looks like
This category’s data primarily originates from game operation backends, player segmentation systems, payment data platforms, and activity log systems. Daily produced documents include activity plans, reward configuration tables, launch checklists, and activity review materials. These documents typically contain fields such as activity identifiers, reward types, configuration values, effective time periods, trigger rules, and compliance check items. Data update frequency is mostly weekly, or temporary adjustments during holiday event cycles. Single or multiple activity configuration entries are updated per change.
What constraints do these characteristics impose on model access and configuration
Data sources for this category are scattered and have varying formats. The model access link must support standardized output from multiple data source types, to avoid format parsing errors. Activity configuration updates occur frequently. Model configurations must support quick adjustments to context and invocation parameters, to adapt to temporarily changed activity rules. Different documents have differing field structures. The model’s context window and invocation parameters must adapt to diverse document lengths and field types. Compliance reviews require accurate verification of reward values and effective time periods. Model parameters must balance accuracy and response speed, to avoid review result deviations caused by improper configuration.
How to set configurations
| Configuration Item | Suggested Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill the AI Proxy service root address as an App/Admin server environment variable, for example http://aiproxy:3000. Do not fill this in workflow global variables, application API request headers, or standard model management pages. Do not add path suffixes, and this is not a single-model address from VLLM/Ollama/Xinference. | Meets the environment variable requirements of FastGPT v4.17.0 and above, and no path suffixes may be added. |
AIPROXY_API_TOKEN | Fill a non-empty administrator token consistent with the AI Proxy server configuration as an App/Admin server environment variable. Do not fill this in workflow global variables, application API request headers, or standard model management pages. | Meets mandatory requirements for service startup verification. Do not use application API keys, model vendor keys, tokens issued per application, or tokens counted per request as replacements. |
maxContext | 8000–12000 token | Adapts to the total length of activity documents and checklists, to avoid review interruptions caused by context overflow. |
maxTokens | 1000–1500 token | Balances the level of detail in compliance reviews and response latency, to avoid overly long output or missing information. |
temperature | 0.1–0.3 | Reduces random output probability, to ensure accurate review results for compliance items such as reward values and effective time periods. |
quoteMaxToken | 2000–3000 token | Limits the length of a single referenced document, to ensure reviews focus on core configuration items. |
Three common misconfigurations
- Phenomenon: App/Admin service startup fails during startup verification, a prompt about missing mandatory environment variables appears, or the model call returns a 401 unauthorized status code. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured as App/Admin server environment variables, or the filled token does not match the AI Proxy server configuration. - Phenomenon: Compliance review results show incorrect reward value calculations or biased effective time period judgments. Cause: The
temperatureparameter is set too high, leading to excessive randomness in model output that deviates from requirements for precise reviews. - Phenomenon: Model calls time out, or a context overflow prompt is returned. Cause: The
maxContextparameter is set too small, failing to accommodate the total content of activity documents and checklists, preventing the model from completing a full review.
How to confirm configurations are correctly set
- Check the App/Admin server’s environment variable configuration, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly filled and non-empty. - Initiate a test compliance review task, observe whether the model returns review results normally, with no context overflow or timeout errors.
- After adjusting parameters such as
maxContextandmaxTokens, verify that activity documents of varying lengths can be processed normally. - Cross-check the consistency between the model’s returned review results and the original activity configuration, ensuring that judgments for core items meet expectations.
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.