What this category of data looks like
This category of data primarily originates from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistical data, and remote sensing imagery. Daily processed documents include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summary materials. Data update frequency is annual. Most documents contain spatial coordinate fields, constraint indicator values, regional scope descriptions, and similar content. Their structure is typically comprehensive documents assembled from multiple sources. Single documents usually have large text and spatial data volumes.
What constraints these characteristics impose on the model access and configuration workflow
The annual update feature means the model access link does not require frequent index rebuilding. A periodic update strategy can be adopted to reduce resource consumption. However, single documents contain multi-source spatial data, structured indicators, and unstructured text, which places high demands on the model's context carrying capacity. The context length parameter must be set appropriately. Planning document review requires strict alignment with compliance logic. Over-reliance on randomly generated content should be avoided. The model's creativity and rigor must be balanced. Most deployment environments are government cloud or dedicated internal networks. Dedicated API access endpoints and authentication methods must be adapted to ensure data transmission and storage comply with security specifications.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context requirements of multi-source planning documents, avoiding truncation of core compliance content |
maxTokens | 4000 token | Controls the length of single-round responses, ensuring review conclusions are clear and complete, and meeting the rigorous output requirements of government documents |
quoteMaxToken | 2000 token | Limits the length of quoted original text, ensuring review can accurately associate specific clauses of declaration materials |
toolChoice | none | Planning review must strictly rely on existing document content, no external tool calls are needed to avoid introducing irrelevant information |
temperature | 0.2–0.3 | Reduces the probability of random generation, ensuring consistency and compliance of review conclusions, matching the rigorous requirements of government review |
AIPROXY_API_ENDPOINT | Fill in the dedicated API address for government cloud | Adapts to the network environment and authentication requirements of government cloud deployment, ensuring secure data transmission |
Three common mistakes
- Symptom: The DeepSeek R1 chain-of-thought function cannot be enabled in the web interface, and no valid feedback is displayed after configuration. Cause: The web interface of the current version does not provide a configuration entry for the chain-of-thought switch. Manual configuration via API parameters is required.
- Symptom: After deploying FastGPT locally, locally deployed large models or model providers not on the default list cannot be selected. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in correctly, making it impossible to connect to custom model channels. - Symptom: The dropdown list of index models is empty in the backend configuration page, and the corresponding model cannot be selected. Cause: The data source synchronization for the territorial spatial basic information platform is not configured correctly, resulting in missing metadata required for model indexing.
How to confirm the configuration is correct
- Test a call to the custom model channel, upload a planning document for review, and check that normal results are returned, with no connection timeout or authentication failure errors.
- View the context reference content in the model's response, confirm that the length of quoted original text does not exceed the
quoteMaxTokenconfiguration range, and core clauses are not truncated. - Adjust the
temperatureparameter to 0.1 and 0.4, compare the differences between the two review conclusions, and confirm that the parameters take effect and the differences meet the expected rigor requirements. - Check the model call logs in the FastGPT backend, confirm that the API endpoint and authentication information match the configured items, and there are no abnormal error records.
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.