Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for this category mainly comes from the national land spatial basic information platform, natural resource survey and monitoring data, population and economic statistical results, and public remote sensing images. Documents processed daily include overall land spatial plans, detailed plans, special plans, plan implementation assessment reports, and public opinion summaries. Data update frequency is once per year. Document structures contain fields such as spatial coordinate annotations, constraint indicator entries, and department opinion ledgers. There is no unified fixed single-document word count limit.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The annual data update frequency means conversation context does not require frequent full refresh of historical data. Work can focus on review of current batch planning materials. Multi-source data includes different types of content such as spatial coordinates and population economic indicators. Prompts need to distinguish three core tasks: spatial compliance verification, indicator compliance check, and opinion integration. The multi-field document structure requires multi-turn dialogue to gradually guide users to supplement missing fields. This avoids one-time input of excessive content causing context overflow. Declaration materials have strong approval correlation. Multi-turn dialogue must retain context of historical approval nodes to ensure consistent review logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Generate a unique value per approval batch | Ensure conversation context for the same batch of review is not confused with other batches, aligning with group management requirements for declaration materials |
messages | Retain the first 10 rounds of conversation content | Adapt to the annual update rhythm of planning material review, avoiding excessive historical data occupying context space |
variables | Inject 3 to 5 core constraint indicators for the current batch | Align with compliance verification requirements for land spatial planning, allowing the model to directly call preset data such as red lines and per capita indicators |
| Prompt template splitting | Trigger step-by-step by material type | Correspond to structural characteristics of different documents, gradually guiding users to submit complete materials |
| Context overflow handling | Automatically truncate earliest non-core approval node conversations | Adapt to annual data update frequency, reducing redundant context occupancy |
Three Common Misconfigurations
- An API call returns a token verification failure, and the OneAPI log shows the token is "fastgpt". API authentication parameters are not configured correctly, causing the token in the request header to be incorrectly overwritten to the default value.
- Historical context is accidentally cleared during multi-turn dialogue, making subsequent review steps unable to associate previous verification results. The
chatIdparameter is not correctly bound, so a new conversation ID is generated for each request, and context cannot be reused. - Conflicting context retention rules occur between two AI calls in a workflow, causing context from the first call to not be passed to the second step. Context retention parameters are not uniformly configured in workflow nodes, leading to inconsistent context management logic across different nodes.
How to Confirm Proper Configuration
- Initiate a test conversation, use the preset
chatIdparameter to repeat requests, and check if the conversation context retains historical interaction content. - Check the authentication token in the request header when calling the API, and confirm it matches the configured third-party model service token.
- Set a breakpoint in the workflow, and check if the
variablesparameter correctly injects core constraint indicator data for the current batch. - Submit a simulated planning material, and check if the model gradually guides users to supplement missing document fields according to the prompt template.
The values provided here are common starting points, and should be evaluated against the user'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.