What data for this use case looks like
Data for policy and regulatory basis of infrastructure construction comes primarily from industry specifications, project approval documents, and industry association standard documents publicly released by the Ministry of Housing and Urban-Rural Development, and housing and urban-rural development authorities of all provinces and cities. Data updates follow no fixed cadence; they are synchronized when new policies are issued, standards are revised, or project filing information is updated. Most documents are structured text, containing fields such as document number, issuing authority, effective date, scope of application, and core clauses. Field units follow standard official document formats: for example, effective dates use the YYYY-MM-DD format, clauses are marked as "Article X", and project-related parameters use industry-standard units such as square meters and ten thousand yuan.
Constraints on model integration and configuration
Infrastructure construction policy documents are often long texts containing specialized official terminology. Model integration must support sufficient context length to fully load a single policy document. Data updates have no fixed cadence, so a flexible knowledge base synchronization mechanism must be configured to prevent models from using expired policy content. Structured document fields require models to accurately match metadata such as document numbers and effective dates. During configuration, the model's recognition weight for official document formats must be explicitly set. Unit matching for project-related parameters is required, so parameters output by models must match the units used in documents, and unit conversion errors must be avoided.
How to configure parameters
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Infrastructure construction policy documents are often long texts. This range ensures core content of a single document is fully loaded, avoiding truncation of critical clauses |
maxTokens | 2000–3000 token | This range allows model outputs of policy interpretations to cover sufficient clause details while avoiding redundant information |
quoteMaxToken | 1500 token | This length ensures sufficient content is retained when quoting excerpts from original policy documents, maintaining completeness and readability |
temperature | 0.1–0.3 | Policy interpretations require strict accuracy. This range reduces randomness, preventing interpretations that conflict with original documents |
AIPROXY_API_ENDPOINT | Fill in the official interface address provided by the deployment service provider | Ensures model requests are correctly routed to the specified large model service, meeting high accuracy requirements for infrastructure construction scenarios |
AIPROXY_API_TOKEN | Match the exclusive token assigned by the deployment service provider | Validates request legitimacy, prevents unauthorized access, and ensures secure access to policy data |
Three common configuration errors
- Symptom: No available models appear in the platform model list, and changes to BASEURL alone do not take effect. Cause: The `AIPROXYAPITOKEN
is not configured synchronously, or the parameter validation logic forAIPROXYAPI_ENDPOINT` fails, resulting in failed interface calls. - Symptom: Model-generated policy interpretations deviate from original document clauses, and quoted excerpts are incomplete. Cause: The
quoteMaxTokenconfiguration value is too low, leading to truncation of quoted original policy content that fails to present core information fully. - Symptom: Model outputs include unofficial policy interpretations that conflict with actual documents. Cause: The
temperatureconfiguration value is too high, causing the model's generated content to exceed controllable randomness and deviate from strict policy interpretation requirements.
How to verify correct configuration
- Submit a test query targeting a publicly available infrastructure construction policy, and verify that the model's returned results include correct metadata such as document numbers and effective dates.
- Review knowledge base synchronization logs to confirm that the latest policy documents have been successfully loaded within the model's context range, with no truncation errors.
- Call the model interface, and check that the returned result format complies with official document interpretation specifications, with no unit or parameter matching errors.
- Verify the legitimacy of the
AIPROXY_API_TOKEN, and confirm that the interface can normally return model responses via simulated requests, with no authentication failure errors.
The values provided above are common starting points, and should be evaluated against individual use cases.
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.