What this category of data consists of
This category of data is generated by central enterprise internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. Daily documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification cancellation certificates. Data sources cover enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Updates occur annually. Document structures include fields such as issue identification, rectification requirements, and supporting material association information. Document formats vary across systems used for export.
Constraints Imposed by Characteristics on Citation Sources and Traceability
The annual update requirement means citation traceability does not need frequent data refreshes. It must ensure retrieved documents are the latest year’s rectification and audit materials. The multi-system data source requirement means traceability information must clearly mark the system that owns the data. This avoids confusion between audit records from different entities. The presence of structured fields such as issue number and rectification requirement means citations must accurately match corresponding fields. This ensures traceability accuracy. The high-severity consequence requirement means traceability information cannot be missing. It must be fully linked to specific documents and supporting materials. Otherwise, repeated audit issues or regulatory notification risks may arise.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 8000–12000 token | Adapts to the single-block content length of internal control and audit documents, preventing truncation of key rectification and issue information |
responseData | Enabled, includes source system, document type, issue number | Meets audit traceability needs, clearly marking the specific source and identifier for each cited piece of content |
detail | Full, includes rectification supporting material number | Ensures cited content can be linked to specific cancellation certificates, meeting the rigor requirements of internal control evaluation |
| Single search limit | Top 10 entries | Controls the number of documents retrieved per search, avoiding context overload that impacts model output |
| Maximum context | Calibrated via actual testing | Adapts to the context window limits of the large language model in use, avoiding exceeding the model’s processing capacity |
Three Common Configuration Mistakes
- Phenomenon: The model context displayed on the page shows 30 entries, but the actual number of cited entries does not match the configured single search limit. The number of entries sent to the calling interface also differs. Cause: Context filtering rules are not configured correctly. Retrieval data is not filtered using party building-related keywords or fields for party building learning Q&A, leading to non-target documents being included.
- Phenomenon:
quoteMaxTokenis set to 1500, but content in knowledge base blocks exceeding 1500 characters is still cited. Cause:quoteMaxTokenonly limits the token limit of a single retrieved block. No truncation rule is configured after overall context splicing, so excess content is included. - Phenomenon: Cited variables are not correctly embedded into the document body. Traceability information cannot be displayed in the output. Cause: The output fields of
responseDataare not correctly bound to the citation variables, or the variable replacement configuration is not enabled.
How to Verify a Correct Configuration
- View the retrieval result list in FastGPT v4.17.0. Confirm that each entry is marked with traceability information such as source system, document type, and issue number.
- Call the test interface. Check whether the returned
responseDatafield contains complete traceability identifiers and does not exceed the presetquoteMaxTokenlimit. - Simulate an audit-related query for party building learning. Verify that the actual number of retrieved documents matches the configured single search limit.
- View the system log. Confirm that the context content received by the model matches the retrieval results displayed on the page, with no abnormal differences in the number of entries.
The values provided are common starting points and should be evaluated against samples from 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 2026-09-28.