What this category of data looks like
This category of data comes from software asset management systems, procurement systems, terminal scan results, and vendor authorization files. Daily maintained documents include license ledgers, software usage lists, procurement contracts, and compliance audit reports. This category of data is updated monthly or quarterly. Core fields include software name, authorized license count, actual installed terminal count, authorization expiration date, and vendor authorization number. No additional custom statistical fields are included.
What constraints these characteristics impose on the context and token link
This category of data is updated monthly or quarterly. A single change review requires associating historical data spans that match the current audit cycle. Therefore, the time range for context recall must be limited to this cycle to avoid introducing expired data that interferes with reviews. There are obvious structural differences between different documents: structured ledgers have fixed field lengths, while unstructured contracts have large fluctuations in text length. Token allocation limits need to be adjusted for different data sources to prevent core authorization information from being truncated. At the same time, authorization quantity verification is a core time-consuming link. The context must accurately recall key fields such as authorized quantity and installed terminal count. Excessive irrelevant text will occupy token quotas and lengthen review time.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 10000–15000 token | Matches the total token count of core documents within a monthly cycle, adapts to the context window limit of most open-source models |
quoteMaxToken | 800–1200 characters | Targets the combined length of core fields for structured license ledgers, ensures key information such as authorized quantity and terminal count is fully recalled |
maxContext | Adjust according to audit cycle | Can be increased to 18000 token for quarterly audit scenarios, covering more historical procurement contracts and terminal scan data |
quoteMaxToken | Calibrate via actual testing | For unstructured compliance audit reports, prevents truncation of critical authorization clauses |
maxContext | Filter expired data | Exclude documents earlier than the current audit cycle, reduce invalid token usage, and focus on valid data required for current reviews |
Three common mistakes
- Phenomenon: After configuring
quoteMaxToken, no fields related to authorized license count appear in review results. Cause: ThequoteMaxTokenvalue is too small, leading to truncation of core field text, which cannot be recognized by the model. - Phenomenon: An error is returned when triggering a change review task in an English scenario, with a prompt that the context token exceeds the upper limit. Cause: The
maxContextvalue is not adjusted for English documents, and the configuration from the Chinese scenario is reused directly, leading to English token conversion exceeding the model window limit. - Phenomenon: The authorization verification result returned by a single change review is incomplete, with authorization status of some terminals missing. Cause: The
maxContextvalue is too low, failing to recall enough historical license ledger data, preventing the model from completing full authorization quantity verification.
How to confirm the configuration is correctly set
- Import a single complete license ledger, use the context preview feature of FastGPT v4.17.0, and confirm that core fields are not truncated.
- Trigger a change review task, check the token consumption statistics in platform logs, and confirm that no context overflow errors occur.
- Import documents of different cycles, and confirm that only data within the current audit cycle is included in the context scope.
- Adjust the
quoteMaxTokenvalue, observe the length change of recalled text, and confirm that the configuration parameter takes effect.
The values provided are common starting points and should be evaluated against samples relevant to the specific use case.
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.