What this category of data looks like
The core documents for this category include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Data is sourced from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems, with an annual update cycle. The data structure centers on three core contents: audit identified issues, rectification requirements, and supporting materials. Fields include issue identifiers, rectification responsible entities, rectification completion status, and other common audit business fields. Common units include entries, copies, and items.
What constraints do these data characteristics impose on model access and configuration
The data characteristics of this category impose multi-dimensional constraints on model access and configuration. Batch data updated centrally once per year requires configuration items to support long-text processing. Context and token limits must be set appropriately to fully parse multiple audit working papers and rectification ledgers. Heterogeneous data from multiple sources requires configuration to unify cross-system data formats, avoiding model parsing errors caused by field differences. Diverse document types require the model to adapt to different input structures. Parameter adjustments must be made to ensure consistent field recognition across all document types. The verification requirement for rectification closing certificates requires tool call configuration to match audit workflow nodes, ensuring the model can associate corresponding supporting materials.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total length of audit working papers and rectification ledgers processed in a single batch for this category is usually large, so complete context input must be ensured |
maxTokens | 2000–3000 token | The output length of ledger summary and report generation must cover the complete issue list and rectification progress to avoid truncation |
temperature | 0.1–0.3 | Stable Markdown format output must be ensured, avoiding format errors caused by increased randomness |
toolChoice | auto | Automatic tool calls must be triggered to associate rectification supporting materials, adapting to the multi-step requirements of the audit workflow |
quoteMaxToken | 1000–1500 token | The original issue text in audit working papers must be fully quoted, avoiding truncation of quoted content |
AIPROXY_API_ENDPOINT | Fill in the address provided by the connected model service provider | Ensure correct routing for model requests and avoid connection failures |
AIPROXY_API_TOKEN | Fill in the valid key assigned by the service provider | Complete identity verification for model access and ensure request legitimacy |
Three common configuration errors
- The ledger summary output by the model has disorganized formatting and fails to use Markdown headings and lists as required. The cause is that the
temperatureparameter is set too high, which increases output randomness and disrupts format stability. - Model calls return a 401 status code, and requests cannot be completed. The cause is that the
AIPROXY_API_TOKENconfiguration is incorrect, or the key has expired, and valid identity verification is not completed. - Single processing tasks time out, and complete reports are not generated. The cause is that
maxContextormaxTokensis set too low, causing the model to fail to fully parse input data, or the request duration exceeds system limits.
How to confirm the configuration is complete
- Submit a test request for a single audit working paper and rectification ledger, and check whether the output Markdown format conforms to the preset structure.
- Use the configured API key to initiate a test request, confirm that the returned status code is 200, and there are no identity verification related errors.
- Submit a batch data test, check whether the model can fully parse documents imported from multiple source systems without content truncation.
- Configure a tool call node, trigger a test for associating supporting materials, and confirm that the model can correctly call the corresponding tool to complete verification.
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.