Characteristics of This Category’s Data
This category of data originates from territorial spatial basic information platforms, natural resource survey and monitoring data, population and economic statistical data, and remote sensing images. The update frequency is annual. Daily produced documents include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data fields cover spatial scope, constraint indicators, layout requirements, feedback opinions, and other relevant items. Common units include hectares, square kilometers, and population counts, which are spatial and socio-economic statistical units.
Constraints on Model Access and Configuration
This category’s data is annually updated multi-source spatial and socio-economic information, with large volume and inconsistent field units. Sufficient context capacity must be configured during model access to avoid truncation of core planning data. Non-structured public opinions and planning documents account for a high proportion. Appropriate context truncation parameters must be set to ensure full retention of key information. Planning documents have strict rigor requirements. The original data reference retention function must be enabled to prevent output content from deviating from the original basis.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the volume of multi-source planning documents and data for annual summaries, avoiding context overflow |
maxTokens | 2000–3000 token | Meets the generation length requirement for a single planning implementation evaluation report, balancing output completeness and response speed |
quoteMaxToken | 1500 token | Retains sufficient original data reference content, meeting the rigor requirements of planning documents |
temperature | 0.2–0.4 | Reduces output randomness, ensuring the accuracy of planning indicators and constraint conditions |
toolChoice | auto | Automatically adapts to the needs of calling tools for multi-source data, no need to manually specify tool types |
AIPROXY_API_TOKEN | Fill in the exclusive token generated by the platform | Completes identity verification, ensuring the security of API calls |
Three Common Misconfigurations
- A
model stream response is emptyerror may occur, even though terminal curl requests return results normally. The root cause is unreasonable context truncation parameter configuration in FastGPT v4.17.0, which prevents the model from obtaining complete input content. - The m3e model may fail to call correctly after a platform upgrade. The root cause is failure to synchronously update proxy configuration parameters for model access, leading to abnormal request routing.
- A large model API may return a parameter error prompt. The root cause is incorrect configuration of the
quoteMaxTokenparameter, which causes the model’s output reference content to exceed the preset length and triggers API verification rules.
How to Verify Proper Configuration
- Execute a terminal curl command to call the model interface, then check if the returned result includes structured planning data and reference markers.
- Upload a single detailed planning document for testing, then confirm that the generated summary includes source annotations for the original data.
- Adjust the
temperatureparameter to 0.1 and 0.5 separately for testing, then compare the consistency of output results to verify the effectiveness of the parameter configuration. - Check the entered values for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTto ensure they match the information provided by the platform exactly.
The values provided here are common starting points and should be evaluated against local deployment 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.