What data for this category looks like
This category of data comes from enterprise internal control systems, ERP systems, 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, and rectification closure certificates. The data update frequency is annual. Document structures include content such as issue descriptions, responsible entities, rectification requirements, and supporting material associations. Some documents are presented in structured table format, others are unstructured text, with no unified fixed format.
What constraints these characteristics impose on model access and configuration
Data for this category comes from multiple dispersed internal enterprise systems. Adapt to the export formats of different systems during configuration to avoid import failures caused by incompatible data formats. Documents include both structured tables and unstructured text, so set context and recall parameters appropriately to cover the information density of both types of documents. The data update frequency is annual, so set the knowledge base sync frequency to annual triggers, without requiring high-frequency updates. Since the business involves audit issue closure and regulatory requirements, configure to ensure the accuracy and compliance of recalled content, and avoid missing key policy basis.
How to configure
The following configurations follow the logic of FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers associated content from multiple audit working papers and rectification ledgers, ensuring complete question-and-answer context |
quoteMaxToken | 2000–3000 token | Ensures complete policy document content is cited per entry, avoiding truncation of key compliance basis |
temperature | 0.1–0.3 | Policy basis Q&A requires rigor, reducing randomness in generated content |
maxTokens | 4000 token | Controls the length of a single response, ensuring output complies with format requirements for compliance documents |
toolChoice | auto | Allows the model to independently choose whether to call knowledge base retrieval for policy basis |
AIPROXY_API_ENDPOINT | Enter a compliant model interface address | Adapts to internal enterprise-compliant model call links |
AIPROXY_API_TOKEN | Enter the key bound to the model | Completes identity verification for model calls |
Three common configuration errors
- Issue: 500 error occurs when importing documents. Cause: Failed to adapt to the mixed format of audit working papers, resulting in format exceptions during parsing.
- Issue: No response after submitting a query. Cause:
AIPROXY_API_TOKENis configured incorrectly or the interface address is inaccessible, and model link verification was not completed. - Issue: Unprocessed
<think>tags appear in responses. Cause: No tag filtering was applied to model return content, and content was directly spliced into the final response.
How to verify successful configuration
- Test importing a single audit notice document, confirm no errors occur, and confirm parsed document fragments can be retrieved in the knowledge base.
- Call the configured model, submit an audit question associated with a known policy, confirm the returned content includes corresponding policy basis fragments.
- Check the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations, simulate a model call and confirm the return result is normal. - Test using a model that returns
<think>tags, confirm tag filtering has been completed, and no redundant tag content appears in responses.
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.