What data for this category looks like
Data for this category comes from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency is annual. Daily documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Most documents contain fields such as business sample numbers, voucher numbers, issue descriptions, rectification requirements, and supporting material numbers. There is no unified fixed format, and the size of individual documents varies widely.
What constraints these characteristics impose on model access and configuration
The annual update frequency means frequent incremental synchronization is not required, so full update configuration can be used. Documents have no unified format and include professional fields such as voucher numbers and rectification nodes, so unstructured text parsing logic must be adapted. The wide variation in individual document sizes requires limiting the context loading upper limit to avoid service overflow. Business data involves sensitive content such as audit issues and rectification closure, so a permission verification link must be configured. Support must be provided for extracting specific fields such as voucher numbers and rectification status for associated retrieval, to meet the precise Q&A needs of audit scenarios.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the unstructured size of single-batch audit-related documents, avoid context overflow that causes answer interruptions |
maxTokens | 4000 token | Control the output length of a single round of answers, match the detailed description requirements of rectification content |
temperature | 0.1–0.3 | Maintain the rigor of party building learning Q&A and audit content, reduce the probability of generating irrelevant or divergent content |
quoteMaxToken | 1500–2000 token | Retain sufficient original reference length to support original evidence for audit issues |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise's private deployment node | Adapt to internal network access restrictions, ensure internal accessibility of API calls |
toolChoice | auto | Call retrieval tools as needed to supplement audit-related document content, improve Q&A accuracy |
Three common misconfigurations
- Symptom: After adjusting the
quoteMaxTokenparameter above 2000, the model answer does not reference any retrieved content, and the retrieval details show that the retrieved data is normal. Cause: The parameter configuration exceeds the model's context carrying limit, causing the reference link to break. - Symptom: After connecting to a third-party API, the reference links in the non-login window are not rendered, only plain text links are displayed. Cause: The front-end configuration switch for reference rendering is not enabled, or the standard reference format field is not included in the API response.
- Symptom: A locally deployed large model has insufficient answer accuracy for a self-built knowledge base, and cannot match audit issue details in the documents. Cause: No adaptive configuration is performed for professional fields of audit-related documents, resulting in key information such as voucher numbers and rectification nodes not being effectively retrieved.
How to confirm the configuration is properly set
- Upload a single audit working paper to the FastGPT v4.17.0 knowledge base, trigger audit compliance Q&A related to party building learning, check whether the answer includes the issue description and rectification requirements in the document.
- Adjust the
temperatureparameter to 0.2, launch Q&A that combines party building theory and audit compliance, check whether the answer remains rigorous and has no redundant content. - Set
maxContextto 10000 token, import multiple annual audit documents, verify that the context loads without errors and the answer fully covers relevant content. - View the API call log, confirm that the
AIPROXY_API_TOKENverification is passed, and there are no permission error or timeout messages.
The values listed here 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.