What the Data for This Category Looks Like
This category includes documents such as internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data is sourced from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency is annual. Document structures contain fixed business fields including issue number, rectification measures, responsible entity, and completion deadline. Some documents include structured quantitative indicators and unstructured supporting materials.
Constraints on Model Access and Configuration
The data characteristics of this category impose clear constraints on model access and configuration. Multi-system data sources require configuring content parsing rules compatible with different output formats, to avoid extraction errors caused by incompatible data formats. The annual update frequency does not require high-frequency incremental synchronization. A full-update vector database update strategy can be used to reduce synchronization costs. Documents include fixed business fields and supporting materials, so the model must distinguish between structured fields and unstructured content to avoid information confusion. The core time-consuming links involve business sample and voucher extraction, so the model must have structured information extraction capabilities to reduce manual sorting workload.
How to Set Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of multiple document types in this category, including internal control self-assessment reports, rectification ledgers, and other related content |
maxTokens | 2000–3000 token | Meets the output content length requirements for documents such as audit working papers and issue lists |
temperature | 0.1–0.3 | Ensures the accuracy of output content, avoiding deviations in audit issue descriptions caused by overly high randomness |
| AIPROXYAPIENDPOINT | Fill in the actual deployed model access address | Adapts to the path requirements of third-party model interfaces |
AIPROXY_API_TOKEN | Fill in the actually generated secret key | Ensures the validity of identity verification for model interface calls |
toolChoice | auto | Allows the model to automatically select processing logic based on task type, adapting to mixed processing scenarios for multiple documents |
Three Common Configuration Mistakes
- Symptom: The model returns an empty response, and the log shows
model stream response is empty. Cause:AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, resulting in failed connectivity of the interface request. - Symptom: Extracted audit issues do not match the business fields in the original document. Cause: Parsing rules adapted to the fixed business fields of this category are not configured, resulting in the model being unable to recognize exclusive field identifiers.
- Symptom: Vector database recall content lacks supporting materials from rectification ledgers. Cause: The value of
maxContextis too small, and insufficient historical document context is included, resulting in limited recall scope.
How to Verify Successful Configuration
- Run a curl command to call the model interface, verify that interface connectivity and basic responses are normal.
- Upload a single rectification ledger document, check whether the fields such as issue number and rectification measures extracted by the model match the original document.
- Check system logs to confirm that the actual call values of configuration parameters such as
maxContextandmaxTokensmatch the preset values. - Simulate the annual full data synchronization process, verify that the vector database update can be completed normally without format errors.
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 2026-09-28.