What the data for this category looks like
Core data for this category comes from content moderation backends, user reports, regulatory policies, and moderation sample libraries. Daily produced documents include moderation rules, disposal guidelines, appeal records, and quality inspection reports. Moderation rule documents contain trigger threshold and disposal action fields. Appeal records include appeal reason and review result fields. Data is updated with routine sample refreshes daily. Rule documents are synchronized immediately in case of policy changes. Business entities include content security operations, moderation quality inspection, and platform governance teams. These teams regularly complete rule explanations, difficult sample determinations, and appeal reviews using multi-source data.
What constraints these characteristics impose on the context and token workflow
Multi-source data means context must cover moderation backend rules, user report records, policy documents, and historical appeal samples. This can easily lead to excessive token consumption. The high-frequency update requirement means context must quickly load the latest versions of rules and policy content to avoid using outdated information. The diverse document structure requires context to distinguish field attributes of different data types. This ensures retrieved content matches the current moderation task. The time-consuming rule explanation and appeal review processes require context to accurately retrieve relevant historical samples and rule clauses. This reduces redundant content that occupies token resources. Additionally, custom context input must be supported to meet batch processing needs for multi-source data.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers total token consumption for multi-source data including moderation rules, appeal records, and policy documents, and aligns with the context configuration logic of FastGPT v4.17.0 |
quoteMaxToken | 2000–3000 token | Matches the average length of a single moderation rule or historical appeal sample, preventing single referenced content from occupying excessive tokens |
| Context retrieval count | Top 5–7 entries | Sorts based on multi-source data relevance to retrieve the most relevant rules and historical samples, balancing retrieval accuracy and token consumption |
| Context update latency | Real-time | Aligns with the daily refresh or immediate policy change update characteristics of this category's data, ensuring context always uses the latest rules and policy content |
| Context input method | Supports API-based custom input | Meets batch processing requirements for multi-source data, without relying on system-automatically spliced historical context |
Three common mistakes
- Phenomenon: When classifying moderation rules, only the current input is matched, and historical appeal records or rule explanations are not associated, leading to classification errors. Cause: Context retrieval function is not enabled. Only single current input is used for content generation, and historical moderation data is not loaded.
- Phenomenon: After moderation rule documents are segmented, context cannot associate corresponding disposal clauses. Cause: Segment length does not match the complete semantic unit of the rule document, leading to context fragmentation and failure to retrieve complete rule content.
- Phenomenon: The service reports token encoder-related errors and restarts indefinitely. Cause: The value range of
maxContextis not restricted, leading to token consumption exceeding the model's supported upper limit and triggering encoding errors.
How to confirm configuration is correct
- Import a single moderation rule document, check whether the context retrieval result includes the disposal guideline content corresponding to that rule.
- Submit a query containing historical appeal records, confirm whether the returned result associates the corresponding review result and rule clauses.
- View the configuration item values, confirm that the values of
maxContextandquoteMaxTokenfall within the preset ranges. - Simulate a rule update after a policy change, check whether the context loads the latest version of the rule content.
The values provided are common starting points and should be measured against samples relevant to the specific deployment.
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.