What this category of data looks like
Data for this category primarily comes from the national unified EIA declaration and approval system for construction projects, ecological environment zoning control results, pollution discharge permit platform, and monitoring data. Update frequency is irregular. Documents processed daily include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical assessment opinions, and EIA approval documents. Most documents contain fields such as project category, EIA level, pollution prevention measures, and public participation status. There are no unified fixed units, and values are adjusted based on specific project details. Typical processing entities include ecological environment department EIA offices, administrative approval bureaus, construction units, and EIA institutions.
What constraints do these characteristics impose on the "model access and configuration" link
Data for this category is scattered and updated without a fixed cycle. The model access link must support batch access and incremental synchronization of multi-source heterogeneous data, and adapt to the structure of EIA documents in different formats. There are no unified fixed units for document fields. A flexible field extraction logic must be retained to avoid hard-coded units or fixed fields. Data volume varies significantly with project scale. Reasonable context and token limits must be configured to prevent call failures caused by overload of single-project data. At the same time, policy-based Q&A requires accurate matching of project categories and EIA levels. The access configuration must be associated with the classification rules of the corresponding data sources to ensure that Q&A results meet business requirements.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average context length of single-project EIA documents, avoiding exceeding the supported range of FastGPT v4.17.0 and the selected model |
maxTokens | 2000–3000 token | Meets the output length requirement for policy-based Q&A, while controlling resource consumption for single calls |
quoteMaxToken | 1500–2000 token | Ensures that quoted EIA document fragments sufficiently cover policy-related content, without truncating key approval basis information |
temperature | 0.1–0.3 | Ensures the accuracy of policy Q&A, avoiding subjective content that deviates from business rules |
AIPROXY_API_ENDPOINT | Fill in the official interface address of the selected model | Adapts to FastGPT's proxy call rules to complete the address configuration for model access |
AIPROXY_API_TOKEN | Fill in the valid key assigned by the model platform | Completes identity verification and implements permission verification for model calls |
Three Common Misconfigurations
- Phenomenon: Some third-party models cannot be selected in the configuration interface, or call errors occur after selection. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the model has not completed access registration in the proxy service. - Phenomenon: Generated policy basis Q&A results are truncated, and complete required approval basis content cannot be displayed. Cause: The values of
maxTokensorquoteMaxTokenare set too low, resulting in forced truncation of output or quoted document fragments. - Phenomenon: Infinite restarts or token encoding errors occur after a call. Cause: The proxy service interface address is configured incorrectly, or the key does not correctly match the permission scope of the corresponding model.
How to Confirm Successful Configuration
- Verify the configuration content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, ensuring they match the official interface and permission key of the selected model. - Enter EIA category and application information for a typical construction project, initiate a test Q&A, and confirm that the returned results contain the corresponding policy basis content.
- Check the call log to confirm that the context and token values of a single call do not trigger the model's restriction rules.
- Adjust the
temperaturevalue and initiate multiple tests, confirming that the consistency of generated results meets business accuracy requirements.
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.