What this category of data looks like
The daily operational documents for this category include four types: ban records, appeal materials, review opinions, and user notifications. Data sources include risk control systems, audit records, user behavior logs, and report evidence. Update cycles are divided into real-time and daily. Ban records contain fields such as account ID, violation time, disposal measures, and associated report ID. Appeal materials include supporting content submitted by users and communication traces. Review opinions include audit conclusions and adjustment basis. Most data fields are enumeration values or structured text, and the text length of single records varies widely.
What constraints do these characteristics impose on the context and token link
The need to integrate multi-source data requires context recall to cover multiple sources such as risk control, audit, and behavior logs. Token consumption per recall round fluctuates with the number of associated pieces of evidence. Real-time updated ban and appeal data requires context synchronization timeliness. maxContext and quoteMaxToken must be used to balance data freshness and token usage. Structured fields and widely varying single-record text lengths require that the granularity of context matching be adapted to records of different lengths, to avoid token overrun caused by overly large blocks, or loss of associated logic caused by overly small blocks. At the same time, account-level review requirements require context to be aggregated around the target account, to avoid irrelevant data occupying token quotas.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the aggregated length of multi-source ban and appeal data, avoiding token overrun in single-round conversations |
quoteMaxToken | 2000–3000 characters | Limits the length of single recalled content, matching the typical text scale of appeal materials and audit records |
| Single search limit | Top 8 entries | Controls the number of associated pieces of evidence recalled, reducing token usage from irrelevant data |
| Chunk length | 800–1200 characters | Adapts to the text granularity of structured records, balancing recall accuracy and token consumption |
| Reranking switch | Enabled | Prioritizes recalling evidence highly associated with the target account, reducing token usage from invalid context |
Three common configuration mistakes
- Phenomenon: Single-round conversation triggers token overrun error, returning a prompt of the type "token quota exceeded". Cause: Fail to adjust
maxContextandquoteMaxTokenparameters, using default configurations directly, leading to token quota being exceeded after multi-source context aggregation. - Phenomenon: The number of recalled associated pieces of evidence exceeds the preset limit, leading to excessively long single-request processing time. Cause: Fail to set a single search limit, recalling all associated records, which additionally occupies token and computing resources.
- Phenomenon: Context matching accuracy for appeal reviews is insufficient, leading to incorrect judgments of violation types. Cause: Chunk length is set too large, causing irrelevant content from adjacent records to be merged, losing matching logic for key associated fields.
How to confirm configurations are set correctly
- View the context configuration panel in FastGPT v4.17.0, confirming that the values of
maxContextandquoteMaxTokenfall within the preset ranges. - Import a single ban record and associated appeal materials, testing the number and single-item length of recalled results to verify compliance with configuration requirements.
- Initiate a simulated review conversation, checking token consumption logs to confirm no overrun errors occur.
- Compare recall results for different accounts, confirming that only evidence associated with the target account is included in the context.
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.