What the data for this category looks like
The core data involved in this category comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, evaluation agency reports and financial statements, with a monthly update frequency. Daily processed documents include property rights registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval forms. Most document structures include fields such as ownership identification, asset details, transaction terms, and approval processes. The length of individual documents varies widely, with some documents being relatively long.
What constraints these characteristics impose on the "model access and configuration" link
Data sources are scattered and mostly internal state-owned assets systems, with strict interface permission verification rules. Therefore, model access must adapt to the identity verification rules of internal systems. Monthly updated data requires regular synchronization, and the model context window must adapt to long document processing needs. Documents include sensitive fields such as ownership identification and transaction base prices, so the scope of model output information must be strictly limited to avoid sensitive data leaks. Field formats vary significantly across different documents, so it must be ensured that the model can accurately identify category-specific fields and avoid generating content that does not comply with state-owned asset management regulations.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Adapt to the context requirements of long documents such as asset evaluation reports and property rights transfer plans, to avoid truncation of key content |
maxTokens | 2000–4000 token | Meet the generation length requirements for documents such as property rights transfer announcements and asset disposal approval forms |
quoteMaxToken | 1000–2000 token | Limit the length of referenced internal system data, ensuring that referenced content is accurate and does not exceed the context range |
toolChoice | auto | Allow the model to independently choose whether to call tools to complete data verification, adapting to the complex verification needs of multiple documents |
temperature | 0.2–0.4 | Reduce the randomness of generated content, ensuring that expressions in property rights-related documents are rigorous and compliant |
AIPROXY_API_ENDPOINT | Fill in according to the internal proxy address | Adapt to network access restrictions of internal state-owned assets systems, ensuring the security of model calls |
AIPROXY_API_TOKEN | Configure according to internal authentication rules | Meet the identity verification requirements of internal state-owned assets systems, ensuring interface call permissions |
Three common configuration mistakes
- The interface displays "No available channels for model gpt-4o-mini under current group default". The cause is that the proxy address or token for this model has not been configured in the corresponding group, or the proxy service has not completed connectivity verification.
- Generated property rights registration form fields are missing. The cause is that the
maxContextconfiguration value is too small, causing ownership field information in long documents to be truncated, so the model cannot fully identify the field requirements. - Generated evaluation report expressions are too casual. The cause is that the
temperatureconfiguration value is too high, causing the randomness of generated content to exceed compliance requirements.
How to confirm the configuration is complete
- The model channel management page is accessed, the configured proxy interface is tested, and the return status code is confirmed to be 200 with no permission error messages.
- A standard property rights transfer plan is uploaded, the document generation process is triggered, and the generated content is checked to confirm it includes category-specific fields with no truncation.
- The
temperatureparameter is adjusted to 0.1, a test text is generated, and the generated content is confirmed to be expressed rigorously without redundancy. - System logs are reviewed to confirm the context length of model calls meets the configuration requirements, with no timeout or truncation records.
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.