What this category of data consists of
Data for this category primarily comes from the national land spatial planning unified map, project approval and supervision platform, current land use database, and ecological protection red line data. Update frequency is irregular. Documents processed daily include land use pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, red line diagrams, and planning conditions. These documents contain fields such as project site selection scope, land use scale, planning control requirements, and pre-approval conditions. There is no fixed unified format, so corresponding information must be extracted based on actual project conditions.
Constraints for multi-turn dialogue and prompt engineering
Decentralized data sources and irregular update cycles require multi-turn dialogue processes to support regular pulling of the latest national land spatial planning unified map and current land use database data, to ensure timeliness of review basis. Documents have no unified format, so prompts must adapt to land use pre-examination and planning permission documents for different projects, to flexibly extract core fields. Review involves pre-conditions from multiple departments such as development and reform, housing and urban-rural development, and ecological protection. Multi-turn dialogue must guide users to supplement cross-departmental approval materials, while real-time verifying the match between site selection and control requirements, to avoid compliance issues in subsequent reviews.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context | 8000–12000 characters | Land use pre-examination and planning permission review requires associating multiple documents and dialogue history. This length can cover complete project review information |
chatId | Bind to unique project code | Ensure multi-turn dialogue context for the same project remains associated, avoiding confusion of review data across projects |
variables | Bind to fields such as project land use scale and red line scope | Provide fixed review and verification benchmark parameters for multi-turn dialogue, avoiding repeated extraction of document information |
messages | Pass historical interaction content in conversation order | Ensure logical consistency of multi-turn dialogue context, meeting the progressive verification requirements of review |
| Number of results displayed per single request | First 3 entries | Only core compliance verification results need to be displayed during review, avoiding interface information overload |
| Context refresh interval | 12–24 hours | National land spatial planning data has irregular update cycles. Refreshing at this interval ensures timeliness of review basis |
Three common configuration errors
- Symptom: Calling the dialogue termination interface returns a
400 Bad Requesterror, and the current session cannot be stopped. Cause: ThechatIdparameter is not correctly bound, so the system cannot locate the target dialogue session. - Symptom: After the workflow runs, multi-turn AI dialogue results are displayed, not just the final review conclusion. Cause: The workflow's result filtering logic is not configured, and the output content of only the last AI node is not specified.
- Symptom: After connecting to an external dialogue channel, the output content contains a large number of format punctuation marks such as
#and*, which cannot be displayed normally. Cause: Markdown format rendering for dialogue content is not disabled, causing the channel to fail to parse the format tags.
How to verify successful configuration
- Initiate a test dialogue containing multiple land use pre-examination materials, and check whether the context fully retains the project's historical information.
- Call the dialogue termination interface, verify that the current session can be stopped normally with no error returned.
- Trigger the workflow to run, check that the final output only retains the review conclusion from the last AI node.
- Connect to an external dialogue channel, verify that the output content has no redundant format punctuation marks.
The values provided here 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.