What data belongs to this category
This category of data mainly comes from the territorial spatial basic information platform, 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. The data contains fields such as spatial coordinates, constraint indicators, and regional scope. Units mostly use industry standard units such as hectares, square kilometers, and ten thousand people. Document structures are mostly chaptered text paired with spatial attachments.
What constraints do these characteristics impose on the multi-turn dialogue and prompts link
This category of data has a low update frequency but a large volume, and mixes text and spatial attachments. The multi-turn dialogue link must retain the messages context to track cross-link planning opinion summary progress and avoid repeated requests for annual fixed data. There are many professional terms. Prompts must clearly define field definitions for the planning industry to avoid non-standard outputs from general models. Fields from multi-source data vary widely. The data source and format requirements must be clarified at the initial stage of the dialogue to reduce subsequent extraction errors.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | First 8000–12000 characters | Adapt to the chapter length of planning documents and avoid truncating core constraint indicators |
messages history retention count | First 10 dialogue turns | Cover multi-stage communication for planning formulation and retain key nodes of opinion summary |
variables preset variables | Bind planning regional scope and annual indicators | Unify data scope in dialogues and avoid cross-region confusion |
| Response language control | Force use of Chinese | Adapt to official expression requirements for industry documents |
chatId session isolation | Create per individual planning project | Distinguish planning tabulations for different regions and avoid data cross-use |
| Timeout setting | 600 seconds | Adapt to the time required for spatial data parsing and avoid premature task termination |
Three common misconfigurations
- Phenomenon: After text content extraction fails, only preset prompt content is returned, and the system does not enter the state waiting for user supplementary input. Cause: No dialogue branch logic is configured. No input-waiting link is triggered after extraction failure, causing the process to terminate directly.
- Phenomenon: After configuring an English prompt and specifying English output, the model still only outputs Chinese content. Cause: No preset variable for language constraint is bound in
variables, or the prompt does not explicitly cover the model's default output language rules. - Phenomenon: An error is returned after uploading spatial planning attachments, with empty fields or incorrect formats. Cause: No separate configuration is made for spatial data formats. The model only supports text extraction and cannot parse spatial attachments.
How to verify correct configuration
- An operator starts a new dialogue, enters a vague description of the planning region, and checks whether
chatIdautomatically binds the preset variables for the current project to avoid cross-project data confusion. - An operator simulates a text extraction failure scenario, enters content without planning indicators, and checks whether the system returns a preset prompt and waits for subsequent input.
- An operator configures an English prompt and launches a test, then checks whether the model output strictly follows the language requirements specified in the prompt, with no unspecified language content.
- An operator uploads a spatial planning attachment, and checks whether the parsing result includes core fields from the attachment, with no empty values or incorrect formats.
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.