What data looks like for this category
Data for this category mainly comes from enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Daily documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, rectification closing certificates, and other related documents. The data update frequency is annual. Documents cover two formats: structured lists and unstructured work records, and include fields such as business sample identifiers, issue descriptions, rectification requirements, and supporting material numbers.
Constraints on model access and configuration
The data characteristics of this category create clear constraints for model access and configuration: Multi-source heterogeneous data sources require configuration options that support unified cross-system data access and format adaptation. The annual update frequency means the knowledge base synchronization cycle does not need frequent adjustments; annual trigger updates are suitable. Documents include two formats: long-text working papers and structured lists. This requires model context processing parameters to adapt to varying text input lengths. Strict output rigor must be maintained to avoid content that does not meet internal control requirements, so model parameters should prioritize stable output.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to long-text documents such as audit working papers and rectification ledgers, preventing truncation of key context |
maxTokens | 2000–3000 token | Meet output length requirements for audit issue descriptions and rectification requirements, ensuring complete information |
temperature | 0.1–0.3 | Internal control audit requires rigorous output, reducing randomness to avoid inaccurate rectification suggestions |
toolChoice | auto | Automatically select tools for multi-source data extraction, adapting to business process guidance needs for multi-system access |
AIPROXY_API_ENDPOINT | Fill in intranet or cloud interface addresses based on deployment environment | Adapt to model access scenarios for internal deployment by state-owned enterprises and public institutions, ensuring data security |
AIPROXY_API_TOKEN | Configure dedicated tokens per enterprise security specifications | Comply with internal control requirements, securing identity verification for internal model calls |
Three common misconfigurations
- Symptom: Connection timeout errors occur when calling the model, or the interface returns a 403 permission denied error. Cause: Proxy rules for the internal network of state-owned enterprises and public institutions are not configured, or
AIPROXY_API_TOKENis not updated per enterprise security specifications, leading to identity verification failure. - Symptom: After adding the knowledge base, the model returns empty results or missing key fields. Cause: The
maxContextparameter is not adjusted for long audit working paper text, leading to truncated context and loss of key information such as business samples and rectification requirements. - Symptom: Rectification suggestions generated by the model deviate significantly from actual business scenarios. Cause: The
temperaturevalue is too high, resulting in excessive output randomness that does not meet the rigor requirements of internal control audit.
How to confirm successful configuration
- Access the configured
AIPROXY_API_ENDPOINTto verify interface connectivity, and confirm the returned response matches the standard response format of FastGPT v4.17.0. - Upload a single audit working paper document, initiate a test request, and check whether the business samples, issue descriptions and other content extracted by the model are complete and not truncated.
- Initiate a test request for business process guidance, confirm the model automatically calls adaptive tools to process multi-source data, and generates guidance content that aligns with audit processes.
- Check system logs to confirm
AIPROXY_API_TOKENverification passes, with no permission error or connection abnormality records.
The values provided here 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.