Reference only; verify against the target FastGPT release and deployment documentation.
What this category's data looks like
The data for this category mainly comes from real estate registration information systems, cadastral survey databases, housing transaction online signing systems, tax systems, and electronic certificate libraries. The update frequency is daily. Daily business documents include registration applications, ownership certificates, survey results, tax payment vouchers, real estate right certificates, and registers. Structured documents contain standardized fields such as rights subject, location, and area. Unstructured documents are mostly signed PDF forms. Data is directly linked to administrative confirmation of rights and business processes.
What constraints these characteristics impose on model access and configuration
Real estate registration scenario data is multi-source, heterogeneous, and updated daily. This requires model access configuration to adapt to unified parsing of multi-system data formats, and configure scheduled synchronization mechanisms to ensure data timeliness. Business documents include structured forms and unstructured PDFs, so targeted content extraction rules must be configured to avoid parsing omissions or errors. Since data is directly linked to administrative confirmation of rights, the accuracy of model output has high requirements, so output randomness must be limited. The party building learning Q&A scenario needs to integrate business data and party building policy content, so context integration length control must adapt to the integration needs of both types of content.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the integrated length of real estate registration business documents and party building policy content, avoiding context overflow that affects responses |
maxTokens | 4000 token | Limits single-round output length, ensuring that business Q&A responses comply with the concise specifications of administrative documents |
temperature | 0.2–0.4 | Reduces output randomness, ensuring consistent results for rights verification and policy interpretation |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapts to the dedicated model call link for real estate registration scenarios, ensuring secure transmission of business data |
AIPROXY_API_TOKEN | Token bound with dedicated permissions | Limits the scope of model calls, preventing unauthorized access to sensitive business data |
Three common errors
- No available channel prompt in the interface: The phenomenon is that FastGPT displays a pop-up prompt indicating no available model channels. The cause is that
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, or the token does not have call permissions for the corresponding model. - Empty content in some PDF files: The phenomenon is that after uploading some real estate registration PDFs, the content is displayed as empty, while some files can be recognized normally. The cause is that the parsing rules for signed PDFs are not adapted, or some files are encrypted and cannot be read.
- Model does not call the knowledge base by default: The phenomenon is that after sending a Q&A that combines party building learning and real estate registration, the bound knowledge base content is not automatically associated. The cause is that the knowledge bases for party building policies and real estate registration data are not associated with the current model configuration.
How to confirm the configuration is complete
- Send a test request to
AIPROXY_API_ENDPOINTwithAIPROXY_API_TOKENto confirm the interface returns a normal status code. - Upload two types of documents: real estate registration application forms and party building policy documents, and verify that all files can be parsed normally and extract valid content.
- Initiate a test Q&A of "Verify a real estate right in accordance with party building requirements", and check whether the model output associates the corresponding business data and party building policy content.
- Set
temperatureto 0.3, initiate 3 identical Q&A sessions, and confirm that the consistency of the output results meets expectations.
The values provided 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.