Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category mainly comes from the national unified construction project EIA declaration and approval system, ecological environment zoning control results, pollution discharge permit platform, and monitoring data. Documents processed daily include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical evaluation opinions, EIA approvals, and similar materials. Data updates have no fixed cycle, and are determined by project declaration progress and approval process timelines. Data fields focus on core business elements such as project category, EIA level, pollution prevention measures, and public participation status. There are no unified fixed units, and values vary by document type.
What constraints do these characteristics impose on the "model access and configuration" link
Data for this category comes from multiple government systems in a scattered manner, and document structures vary greatly. This requires model access logic to adapt to multi-source heterogeneous data parsing. The irregular update rhythm requires configured context management rules to be flexibly adjusted, to avoid interference with current approval judgments from old data. EIA documents have strong business attributes, so model parameter configuration must bind recognition weights for core business fields such as project category and pollution prevention measures. At the same time, the consequences of approval errors are severe, so sufficient fault tolerance mechanisms and log recording parameters must be configured to ensure process traceability.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of long documents such as EIA reports and approval applications, avoiding truncation of core project information |
maxTokens | 2000–3000 token | Balances output length and response speed, ensuring complete output of process steps and verification key points |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring accuracy and standardization of government service guidance content |
toolChoice | auto | Automatically calls tools to integrate multi-source business data, adapting to the parsing requirements of multiple types of documents in EIA approval |
quoteMaxToken | 1500 token | Limits the length of a single quoted document, avoiding context overflow caused by overly long quotes |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address of the deployment node | Adapts to the network isolation requirements of private deployment, ensuring the security of government data transmission |
Three common mistakes
- Garbled characters or incorrect output formatting. The cause is failure to adapt to professional terms and official document format markers in EIA documents, and failure to configure character encoding compatibility rules, leading to encoding errors during model parsing.
- Uploaded DOC format EIA documents cannot be analyzed. The cause is failure to configure parsing adaptation rules for the corresponding document type, or failure to enable the corresponding document parsing configuration, leading to incorrect splitting and recognition of unstructured documents.
- Model call fails with authentication or timeout error. The cause is incorrect
AIPROXY_API_TOKENconfiguration, or the interface address does not match the deployment environment, leading to a blocked model call link.
How to confirm the configuration is correct
- Upload a single typical EIA report, check if the model can completely extract core fields such as project category and EIA level, and confirm that the context configuration can cover the current document length.
- Simulate a multi-source data integration scenario, check if the model can automatically call tools to obtain external information such as zoning control results and monitoring data, and confirm that the tool call configuration takes effect.
- Adjust the
temperatureparameter to different ranges, compare the randomness of the output content, and confirm that the configuration meets the strictness requirements of government approval. - Test abnormal inputs such as damaged DOC files, check if the model returns clear error prompts, and confirm that the fault tolerance configuration takes effect.
The values provided are common starting points and should be evaluated 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.