What this category of data looks like
Core documents for this category include land use preliminary examination and site selection opinions, construction land planning permits, construction engineering planning permits, red line maps, and planning conditions. Data sources include the national spatial planning one-map, project approval and supervision platform, land use status database, and ecological protection red line data. Data update frequency is irregular. Document structures include fields such as project main information, site selection scope, land use scale, control rules, and spatial vector attachments. Systems must adapt to multi-source heterogeneous business data format requirements.
Constraints on model access and configuration
Multi-source heterogeneous data sources require configurations to support interfacing with different business systems. Irregularly updated data requires configurations to support incremental synchronization and real-time verification. Diverse document types including text and spatial vector attachments require configurations to support visual document parsing. The review process checks for conflicts between site selection and control rules, land use scale and red lines. Model outputs must strictly link to original document content, and avoid generating unsubstantiated judgments. Errors directly affect project approval and construction startup. Configurations must strictly limit the range of model generation deviations.
How to set configurations
The following configuration items apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate text descriptions of multi-source approval documents and spatial data, to avoid truncating key control rules |
maxTokens | 4000–6000 token | Adapts to the text length of single file reviews, to avoid exceeding large model processing limits and causing timeouts |
quoteMaxToken | 2000–3000 token | Limits the length of quoted original document content, ensuring review basis comes entirely from actual files |
vision | Enabled | Used to parse scope and control requirements from spatial documents such as red line maps and planning condition drawings |
temperature | 0.1–0.3 | Reduces randomness in model generation, ensuring the rigor of review judgments |
AIPROXY_API_ENDPOINT | Fill in the interface address of the privately deployed large model | Adapts to large model access requirements for private cloud deployments or local Docker setups |
Three common configuration errors
- Issue: Generated review results contain forged reference IDs not linked to actual file documents. Cause: The
quoteMaxTokenlimit for reference content range is not configured, causing the model to generate false references not bound to original documents. - Issue: When configuring a locally or privately deployed large model, the target model cannot be selected in the interface, but the published workflow can call it normally. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in correctly. Front-end verification fails, but back-end configuration is not verified after publishing. - Issue: The classify module cannot match file types after configuration. Cause: The
temperatureparameter is not set reasonably, causing deviations in the model's matching of classification rules.
How to confirm configurations are properly set
- Upload a single planning condition document and red line map. With the
visionconfiguration enabled, check if the model can correctly extract red line scope and control indicators. - Fill in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, then call the test interface. Check if review results are returned normally, with no connection timeout or authentication failure errors. - Adjust the values of
maxContextandmaxTokens, then upload multiple associated files. Check that no content truncation or timeout errors occur. - Set
temperatureto 0.2, then test the classify module's classification results for file types. Check that results match expectations.
The values provided here are common starting points, and should be evaluated 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.