What the data for this category looks like
Data for this category primarily comes from risk control systems, audit records, user behavior logs, and report evidence. The update frequency is daily or real-time. Four types of documents are produced daily: ban records, appeal materials, review opinions, and user notifications. Ban records include the fields: account identifier, violation trigger rule, and disposal time. Appeal materials include the fields: appellant information, evidence content, and appeal request. Review opinions include the fields: review conclusion, and adjusted disposal level. User notifications include the fields: notification recipient, send time, and notification content.
What constraints these characteristics impose on the "context and token" link
Multi-source heterogeneous data requires context to integrate field information from different systems, to avoid missing information from a single data source. Real-time or high-frequency updated data requires controlling the total length of context loading, to prevent model response timeouts caused by data overload. Different document structures require context to be sorted by business priority, prioritizing loading violation evidence and appeal evidence content. Account-level business attributes require context to be bound to the account identifier for filtering, to avoid recalling historical data from unrelated accounts. Severe error consequences require context to accurately recall core evidence, to prevent false bans or return of violating accounts due to missing information.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Total length of integrated multi-source documents, matches the mainstream model context limit of FastGPT v4.17.0 |
quoteMaxToken | 3000–4000 token | Prioritize retaining core content of violation evidence and appeal reasons, avoid redundant information occupying context space |
| Chunk overlap length | 200–300 characters | Avoid splitting critical evidence chains when splitting ban records and appeal materials, ensure context integrity |
| Single recall limit | Top 8 entries | Matches the typical number of account-level historical records, avoids irrelevant data interfering with model judgment |
| Context sorting rule | Sort by evidence priority | Load violation trigger logs and appeal evidence materials first, then review opinions and user notifications |
Three Common Misconfigurations
- Issue: Search results do not associate the current account’s historical ban records. Cause: The account ID is not bound as a context filter condition to the search request, resulting in the search scope covering all data.
- Issue: The model returns an error prompt that the context length exceeds the limit. Cause: The value of
maxContextis not restricted, causing the total loaded context length to exceed the model’s supported range. - Issue: Evidence recalled during appeal review is unrelated to the current account. Cause: Historical records are not filtered by account identifier, leading to mixing of business data from different accounts.
How to Confirm the Configuration Is Correct
- A complete single account ban and appeal document is imported. The system-recalled context content is checked to confirm it includes all relevant records for the associated account.
- A search query is triggered. Returned results are verified to only associate historical data of the current account, with no information from unrelated accounts.
- The
maxContextparameter is adjusted. The time consumption change of system context loading is observed to confirm the value meets business response requirements. - Model-generated review suggestions are viewed. Referenced evidence is confirmed to all come from the configured context range, with no irrelevant content mixed in.
The values given 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.