What This Category of Data Looks Like
Data for this category originates from the national land spatial planning one-map system, project approval and supervision platform, land use status database, and ecological protection redline database. Update frequency is irregular. Daily processed documents include land use pre-review and site selection opinions, construction land planning permits, construction engineering planning permits, redline drawings, and planning conditions. Data fields cover project name, site selection coordinates, land use scale, planning control indicators, redline scope, and more. Units include spatial measurement units such as hectares and square meters.
What Constraints Do These Characteristics Impose on Model Access and Configuration?
Data sources for this category are scattered and have diverse formats, including text approval documents and spatial drawing documents. This requires the model access link to support multimodal visual parsing capabilities. Data updates have no fixed cycle, so rapid integration of incremental data must be supported. The model context length must cover the full approval materials for a single project. Fields such as spatial coordinates and land use scale have high accuracy requirements. Model output must strictly match preset fields and measurement units to avoid format deviations that affect ledger summary. Differences in field naming across cross-departmental data sources require the configuration link to support pre-processing for unified field mapping.
How to Set Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | true | This category includes spatial drawing documents such as redline drawings and planning conditions, so visual parsing capability must be enabled |
maxContext | 8000–12000 token | Approval materials for a single project usually include multiple documents, so full context coverage is required to ensure the completeness of ledger summary |
maxTokens | 2000–3000 token | Sufficient output length is required for single report generation, to meet the output needs of complete ledgers and planning permit conclusions |
temperature | 0.1–0.3 | High accuracy is required for spatial data and approval fields, so reduce randomness to ensure output consistency |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapt access paths for different models to ensure normal model call links |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Complete identity verification for model services to ensure legitimate call permissions |
Three Common Misconfigurations
- Symptom: Model stream response is empty, and the console prompts no valid output. Cause: The
visionconfiguration item is not enabled, so spatial drawings such as redline drawings cannot be parsed, resulting in empty input data and call failure. - Symptom: Prompt for failed image download when calling multimodal models. Cause: Associated image parsing service is not configured, or the service pointed to by
AIPROXY_API_ENDPOINTdoes not have image download permissions enabled. - Symptom: Ledger summary fields are missing or units are incorrect. Cause:
temperaturevalue is too high, leading to excessive randomness in model output, and failure to strictly match preset fields and measurement units.
How to Confirm Successful Configuration
- On the FastGPT v4.17.0 model configuration page, enable the
visionoption and save. Upload a single redline drawing for parsing testing, and confirm that coordinate and scope information can be extracted. - After filling in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, call the test interface to send a text request, confirm that a normal text response is returned. - After configuring
maxContextandmaxTokens, import the full approval materials for a single project, test whether the generated ledger includes all preset fields. - Adjust
temperatureto around 0.2, run multiple tests on the same input, confirm that there are no obvious deviations in the output fields and units.
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.