This document applies to FastGPT v4.17.0.
What This Category of Data Looks Like
Data for this category mainly comes from content review backends, user reporting channels, public regulatory policy documents, and internal review sample libraries. Daily maintained documents include review rule entries, violation content disposal guidelines, user appeal records, and quarterly quality inspection reports. Data update frequency is daily synchronization, or immediate updates with regulatory policy adjustments. The document structure takes rule number, trigger condition, and disposal threshold as core fields. The sample library stores labeled compliant/non-compliant content fragments, categorized and stored by content type and review scenario.
How These Characteristics Impact Model Integration and Configuration
Dispersed data sources and inconsistent update frequencies require model integration to support batch calls and incremental synchronization across multiple data sources. Ununiform field formats for review rules and sample libraries require the configuration phase to support custom prompts to adapt to different document structures. The most time-consuming rule interpretation and difficult sample judgment phases require models to handle long texts and complex scenarios, so context and token consumption must be limited. Error outcomes directly relate to regulatory compliance and user rights, so model call stability and result traceability are required. Clear failure retry and log recording mechanisms must be configured.
How to Set Configuration
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text length of review rules and appeal records, avoiding truncation of key rule descriptions |
maxTokens | 4000 token | Controls the output length of single-round review results, preventing exceeding interface limits of content platforms |
temperature | 0.1–0.3 | Reduces random output probability, ensuring consistency of review rule judgments |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, e.g. http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Non-single-model service address | Environment variable for the App/Admin server environment dependent on AI Proxy, ensuring the service can connect to AI Proxy |
AIPROXY_API_TOKEN | AI Proxy admin Token. Must match the AI Proxy server configuration and be non-empty. Not application API Key, model vendor key, etc. | Environment variable for the App/Admin server dependent on AI Proxy, used for identity authentication |
vision | Enabled | Supports access to vision models for processing image-based review samples |
Three Common Configuration Mistakes
- Phenomenon: Batch execution nodes work normally in online debugging, but cannot complete all cycles during API calls. Cause:
maxContextis not configured to adapt to context accumulation from batch requests, leading to context overflow triggered by long cycles. - Phenomenon: Cannot process review image samples after connecting a vision model. Cause: The
visionconfiguration item is not enabled, or the processing logic for visual input is not explicitly specified in the prompt. - Phenomenon: Review results returned by model calls deviate significantly from preset rules. Cause: The
temperaturevalue is too high, leading to random fluctuations in judgment results that do not match the strict consistency requirements of review rules.
How to Confirm Configuration Is Correct
- Execute test cases for a single review rule, check if the interface return results include complete rule judgment logic.
- Check if the App/Admin server has correctly configured the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables, with no format errors, andAIPROXY_API_TOKENis non-empty. - Adjust the
temperatureparameter, compare multiple call results for the same sample, confirm that the consistency of judgment results meets expectations. - Submit batch sample tests, check the completeness of loop execution and result traceability.
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.