What data for this category looks like
This reference is based on the context and token configuration logic of FastGPT v4.17.0. Data sources for this category include content review backends, user reporting channels, industry regulatory policy documents, and review sample libraries. Daily maintained documents include review rule entries, disposal operation guides, appeal review records, and monthly quality inspection reports. Data updates occur daily by syncing backend operational data, with real-time updates aligned with adjustments to industry regulatory policies. Data fields include rule code, effective time limit, matching sample type, disposal level, and others. Common units are characters, entries, and time intervals.
Constraints imposed on the context and token workflow
Documents such as review rules and appeal records have significant differences in format and length. When splicing context, filter content by business priority to avoid invalid token usage. Daily or real-time update rhythms require the context window to dynamically pull the latest rule entries. Limit the total token amount passed in a single request to adapt to interface restrictions. Multi-source field characteristics require token statistics to be linked to the length of corresponding business fields, to avoid token overflow from field redundancy. The difficult sample determination link requires accurate recall of relevant rules and samples. Control the number of recalled contexts and their token proportion to ensure determination efficiency.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed text length of review rules and appeal records, avoids exceeding interface token quota limits |
quoteMaxToken | 2000–3000 token | Ensures complete passing of core review rules and matching samples, meets reference needs for rule explanation and sample determination |
| Context recall count | Top 8–12 matching samples | Covers rule reference ranges for common review scenarios, avoids excessive invalid content occupying token resources |
| Context update cycle | Sync daily at midnight | Matches the daily data update rhythm, ensures context always uses the latest review rules and disposal guides |
| Token statistics dimension | Count input and output separately | Corresponds to token consumption for rule calls and result returns in review scenarios, facilitates operational quota accounting |
Three common misconfigurations
- Phenomenon: The system cannot separately display the input token count and output token count of AI conversations, only showing a combined total. Cause: The token statistics dimension is not configured to split input and output, making it impossible to accurately calculate token consumption for review sessions.
- Phenomenon: The number of contexts displayed in the workflow does not match the actual number of passed review session histories. Setting
maxContextto 0 results in no historical data being passed. Cause:maxContextis incorrectly set to 0, which truncates all historical review session context data. - Phenomenon: The review rule documents referenced by the context do not retain their original markdown formatting, only showing plain text. Cause: A reasonable value for
quoteMaxTokenis not configured, and long text is truncated, causing format markers to be lost.
How to confirm proper configuration
- Check system logs to confirm that input and output token statistics are displayed separately, with no combined statistics.
- Test setting
maxContextto a specified value, verify that the number of passed contexts matches the configured recall count. - Submit a test request containing a long review rule, confirm that no token limit error is triggered and the rule content is fully passed.
- View the context reference module, confirm that format markers of review rule documents are correctly retained with no loss.
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.