Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
Data for this category primarily comes from the national land spatial basic information platform, natural resource survey and monitoring data, population and economic statistical data, and remote sensing imagery, with an annual update frequency. Daily produced documents include overall land spatial plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Document structures contain spatial coordinate fields, constraint indicator parameters, population and economic statistical items, and facility layout descriptions. There is no fixed uniform single-document length standard.
What constraints these characteristics impose on the model access and configuration link
Multiple heterogeneous data sources require the model access link to adapt to spatial and statistical data in different formats. The annual update frequency means there is no need to configure high-frequency data synchronization logic. Long-text structured planning documents occupy a large amount of context window space, so context and output length parameters must be adjusted. Professional fields such as spatial coordinates and constraint indicators require the model to have accurate recognition and association capabilities. Therefore, appropriate tool calling rules must be configured to prevent generated content from deviating from planning professional specifications.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context accommodation needs of long-text planning documents, preventing key data from being truncated |
maxTokens | 4000–6000 token | Matches the conventional output length of single planning material drafting, meeting the needs for complete document generation |
temperature | 0.2–0.4 | Ensures the rigor of planning documents, reducing the degree of divergence in generated content |
toolChoice | auto | Automatically triggers tool calls to enable associated processing of spatial data and planning text |
quoteMaxToken | 2000–3000 token | Retains sufficient original quoted fragments, supporting the completeness of basis for planning demonstration |
AIPROXY_API_ENDPOINT | Fill in the API endpoint address provided by Azure official | Adapts to protocol differences between Azure OpenAI and standard OpenAI |
AIPROXY_API_TOKEN | Fill in the Azure OpenAI service key | Completes identity verification and access for Azure models |
Three common errors
- A 400 Bad Request error occurs when calling an Azure OpenAI model, caused by confusing the API endpoint formats of standard OpenAI and Azure, and incorrect configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. - Spatial constraint indicators are missing or incorrect in generated planning documents, caused by an excessively small
maxContextvalue that cannot fully accommodate the context information of multi-source spatial data. - The output planning text has loose logic and unprofessional wording, caused by an excessively high
temperaturevalue that causes generated content to deviate from the specification requirements for planning documents.
How to confirm correct configuration
- Test the configured model call to check whether planning-related text can be returned normally, confirming that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Import a typical planning document fragment to check whether the model can accurately identify spatial coordinate and constraint indicator fields, confirming that the context parameters match the document length.
- Generate a test planning material to check the rigor of the output content, and adjust
temperatureto a range that meets professional requirements. - Trigger the automatic tool call process to confirm that the
toolChoiceconfiguration can correctly associate spatial data processing logic.
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.