What the data for this category looks like
Daily documents for this category include land pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, red line drawings, and planning conditions. Data sources include the national land and spatial planning one-map system, project approval and supervision platform, current land use database, and ecological protection red line data. Update frequency is irregular. Document structures mostly include project subject information, site selection scope, land use scale, control requirements, approval nodes and other content. Some documents are CAD or PDF format spatial vector or raster files, with no unified standardized field format.
What constraints do these characteristics impose on the "model access and configuration" link
Since data sources include spatial vector and raster files, configure modules that support multi-format document parsing when accessing the model, to adapt to unstructured text and spatial coordinate data extraction. Because update frequency is irregular, configure a flexible synchronization mechanism with adjustable parameters to ensure the timeliness of policy and approval data. Documents have no unified standard fields, so enable support for custom field mapping to avoid information extraction deviations caused by format differences. Configure tool call trigger logic to support cross-data source information verification, to check the matching between site selection and control requirements.
Configuration recommendations for FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate the full context of multiple planning approval documents and land and spatial control data to avoid truncation of core policy basis |
quoteMaxToken | 1500–2000 token | Must fully reference key policy texts such as planning conditions and red line scope to ensure accurate and complete citation of policy basis in responses |
toolChoice | auto | Automatically trigger cross-data source verification tools to check the matching between project site selection and ecological protection red lines, current land use status |
vision | Enabled | Used to parse documents with visual spatial content such as red line drawings and planning conditions to accurately extract land scope information |
temperature | 0.1–0.3 | Must maintain response rigor and avoid generating non-standard content that does not comply with natural resource control policies |
AIPROXY_API_ENDPOINT | Fill in according to the actual node of private deployment | Adapt to the model call address of the internal network environment to ensure data transmission compliance |
Three common configuration errors
- Redundant model thought process fragments may be included in responses, reducing the readability of policy Q&A. This occurs when the
quoteMaxTokenparameter is not set reasonably, retaining unnecessary internal model reasoning content. - Garbled characters or failed spatial coordinate extraction may occur when parsing documents such as red line drawings and planning conditions. This occurs when the
visionconfiguration is not enabled, so the format of documents with visual content cannot be parsed correctly. - Connection to the specified model may fail after upgrading the platform version. This occurs when the
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENconfiguration is not updated synchronously, which does not match the interface verification rules of the new platform version.
How to confirm configurations are correctly set
- Upload a land pre-examination and site selection opinion document, check whether the model can correctly extract core information such as project subject and site selection scope.
- Initiate a query that includes policy basis verification, confirm whether the model automatically triggers cross-data source compliance verification and outputs corresponding results.
- View the model call log to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct, with no connection errors. - Adjust the
temperatureparameter, test the same query multiple times, confirm that response content remains consistent with no obvious deviations.
The values provided here are common starting points, and should be measured against the samples used in the actual deployment.
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.