What Data Looks Like for This Category
The daily documents for this category include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data mainly comes from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images. The update frequency is once per year. Document structures include fields such as spatial coordinates, planning constraint indicators, and regional development data. Some documents include multi-format spatial layer data, and data volume increases as planning coverage expands.
Constraints Imposed on Model Access and Configuration
The combination of multi-source spatial data and long-text documents requires the model context window to adapt to a long input length. The annual update data characteristic means the model does not need frequent data source refreshes, and fixed-cycle incremental synchronization can be configured. Complex document structures and multiple types of fields require the model to have structured information extraction and cross-field association capabilities. The strict requirements of planning review make the stability and accuracy of model calls core constraints. Any configuration deviation may lead to delays in planning compilation or distortion of implementation evaluation results.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context requirements of multi-source planning documents, avoiding truncation of key information |
maxTokens | 4000 token | Matches the single-segment output length requirements of planning evaluation reports, ensuring complete presentation of planning scheme suggestions |
temperature | 0.3–0.5 | Balances the rigor and flexibility of planning content, reducing unnecessary subjective adjustments |
AIPROXY_API_ENDPOINT | http://localhost:8080/v1 | Standard access path adapted to locally deployed rerank or large model services |
AIPROXY_API_TOKEN | Security credentials generated by the local model | Used for identity verification of locally deployed models, must fully match the credentials configured for the model |
Three Common Configuration Errors
- Incorrectly failing to add the Bearer prefix when configuring
AIPROXY_API_TOKEN, or using a configuration value that does not match the credentials generated by the local model. This causes model call failure, and the interface displays no valid model response. - After deploying the rerank model using docker-compose, FastGPT does not display the model. The causes are incorrect container port mapping configuration, or FastGPT not being configured with the correct API access address.
- Selecting the v2 version model but actually calling the v1 version. The causes are that the model name in the configuration file does not fully match the name selected in the FastGPT interface, or the system cache has not been updated in a timely manner.
How to Confirm Successful Configuration
- Visit the model management page in the FastGPT v4.17.0 backend, and confirm that the added local model has a normal running status.
- Initiate a test call for a planning document, and check whether the returned result includes accurately extracted planning constraint indicator content, with no obvious format errors.
- Check the model call log in the conversation details page, and confirm that the used model version matches the configured v2 version.
- Verify the access connectivity of the local model, ensuring that the environment where FastGPT is located can normally request the model interface, with no timeout or connection refused prompts.
The values given 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.