What does the data for this use case look like?
Data sources include enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Daily processed documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Data update frequency is annual. Document structures are mostly a mix of structured tables and unstructured text, including fields such as problem description, rectification requirements, supporting material number, and responsible department. There is no unified fixed format, and formats adjust based on enterprise internal control specifications.
What constraints do these characteristics impose on model access and configuration?
Annual updated data volume is large, and document structures are inconsistent. This requires model access to support multi-format document parsing. Data fields from different systems have significant differences. Parameters for unstructured text extraction must be configured. Association verification between rectification ledgers and closing certificates requires the model to have context association capability. Context window configuration must match document length. Data sources are scattered. It is necessary to ensure that model access API endpoints can connect to files exported from multiple systems. Format compatibility issues across different systems must also be addressed.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | Adapts to the length requirements of single documents such as internal control self-assessment reports and audit working papers, ensuring complete reading of core file information |
| temperature | 0.1–0.3 | Ensures accurate and stable extraction of audit issues, rectification requirements and other content, reducing subjective bias |
| maxTokens | 2000–3000 token | Meets the generation and verification output length requirements for texts such as rectification ledgers and closing certificates |
AIPROXY_API_ENDPOINT | Fill in the local model service address according to the deployment environment | Adapts to locally deployed model runtime environments such as VLLM and Ollama, supports connection to Qwen3 series models |
AIPROXY_API_TOKEN | Fill in the actual generated key | Ensures secure and controllable access permissions for model services, preventing unauthorized access |
quoteMaxToken | 1500–2000 characters | Retains sufficient original file fragments for audit issue verification, improving verification credibility |
Three Common Configuration Errors
- Interface displays model connection failure with status code 404. Cause:
AIPROXY_API_ENDPOINTis not filled correctly. The locally deployed VLLM, Ollama or other large model service address is not mapped to an accessible network path. - Extracted rectification issue fields are empty. Cause:
temperaturevalue is too high, causing model-generated content to deviate from the preset audit issue format and fail to match preset field extraction rules. - Context overflow causes processing interruption. Cause:
maxContextis set too small, unable to accommodate the complete content of a single audit working paper, leading to loss of key file information.
How to Verify Successful Configuration
- Manually upload a single internal control self-assessment report, check whether the model can correctly extract the issue list and rectification requirements from it, and verify the matching degree between the extraction results and the original text.
- Call the test interface of the model service, verify whether the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan return normal responses, with no connection timeout or permission errors. - Adjust the
maxContextparameter, upload the longest audit working paper, confirm that the processing process is not interrupted, and the output content completely covers the core information of the document. - Import mixed-format documents exported from multiple systems, check whether the model can correctly identify the field structures of different documents, with no format parsing errors.
The values provided are common starting points and should be evaluated 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.