What this category of data looks like
State-owned asset management companies’ policy and regulatory basis data comes primarily from official regulatory documents issued by the State-owned Assets Supervision and Administration Commission of the State Council and the Ministry of Finance, plus internal state-owned asset operation management rules and risk control compliance manuals developed by the company. Data updates align with new national policy releases or internal system revisions, with no fixed cycle. The update frequency for core regulatory documents and internal systems is no less than once per year. A single document typically includes fields such as release document number, issuing authority, effective date, clause text, applicable subjects, and penalty provisions. Some associated files include policy interpretation attachments.
What constraints do these characteristics impose on model integration and configuration?
Policy and regulatory basis documents follow a structured format but have long individual lengths. They also have strict requirements for content accuracy. Model integration must support long context processing to avoid losing key clauses due to context truncation. Data fields include clear effective dates and applicable scopes. The model must accurately match policy content for the target scenario. This requires limiting model generation randomness, adjusting context recall matching logic, and prioritizing the latest effective policy content. Policy document updates have no fixed cycle. Model configuration must support regular knowledge base synchronization checks to prevent calling outdated regulatory content. Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to complete single policy document and associated document splicing, avoiding key clause truncation |
maxTokens | 4000–6000 token | Ensures the model generates complete compliance Q&A responses, covering policy interpretation and applicable scenario details |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring policy interpretation matches original clauses and avoiding content inconsistent with regulatory basis |
quoteMaxToken | 2000 token | Limits the length of single quoted document fragments, ensuring quoted content is accurate and easy for users to verify |
AIPROXY_API_ENDPOINT | Fill in the actual access address of the locally or cloud-deployed large model service | Connects to the model service and ensures the request path is correct |
AIPROXY_API_TOKEN | Fill in the access key generated by the model service | Verifies model service access permissions and ensures call security |
Three common configuration mistakes
- Issue: Model calls return results that do not match the original policy text, or include custom clauses not listed in the regulatory documents. Cause: The
temperatureparameter is set too high, leading model output to deviate from policy clauses. - Issue: Configuring
AIPROXY_API_ENDPOINTprevents model calls, with the page showing connection timeout or insufficient permission prompts. Cause: The actual model service access address is incorrect, local firewall rules block the request port, or theAIPROXY_API_TOKENconfiguration is wrong. - Issue: The output of a prior AI model in a workflow cannot be updated to an empty value, with variables always retaining historical content. Cause: The overwrite original value option is not selected in the variable update step, or the variable refresh execution node is not triggered.
How to confirm successful configuration
- Send a test Q&A request for a single policy document, and verify the reply includes key clauses from the document.
- Review the model call log to confirm request parameters such as
maxContextandtemperaturematch preset values. - Manually update policy files in the knowledge base, send a test call, and confirm returned content uses the latest regulatory version.
- Send an independent model request using the configured
AIPROXY_API_TOKEN, and confirm normal model response retrieval.
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.