Data Characteristics of This Category
Data sources for this category include online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. Data updates daily. Commonly processed documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Each document contains fields such as law enforcement subject, monitoring point, pollutant indicators, test values, and rectification requirements. Supported units include mg/m³, mg/L, ppm, and others. Individual document length varies widely, ranging from hundreds to thousands of characters.
Constraints on Model Access and Configuration
Data for this category updates daily and originates from multiple dispersed sources. Model access and configuration must support real-time synchronization for multi-source data, to avoid retrieving outdated or mismatched law enforcement and monitoring data. Individual document length varies widely. Set a reasonable limit on the total token count of documents retrieved per round, to prevent exceeding the model's context limit. Law enforcement and monitoring data must strictly align with policy basis. During configuration, ensure the accuracy of retrieved content, to avoid introducing irrelevant information that disrupts Q&A results.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the need to retrieve multiple law enforcement documents and monitoring reports per round of Q&A for this category, preventing context overflow |
maxTokens | 4000–6000 token | Limits the length of single-round model output, ensures complete expression of policy basis Q&A, and complies with the conventional call limits of most large models |
quoteMaxToken | 3000–5000 token | Restricts the maximum token count of a single retrieved document, prevents individual law enforcement or monitoring documents from occupying too much context space, and ensures simultaneous retrieval of multi-source data |
temperature | 0.1–0.3 | Ensures the rigor of policy basis Q&A, avoids generating content that deviates from law enforcement specifications |
toolChoice | auto | Automatically determines whether to call tools, adapts to the needs of multi-source data retrieval, and ensures Q&A results reference accurate law enforcement and monitoring data |
AIPROXY_API_ENDPOINT | Fill in the service address of the corresponding model proxy | Enables cross-platform model calls, adapts to local deployment or cloud deployment scenarios |
AIPROXY_API_TOKEN | Fill in the access key assigned by the platform | Ensures the security of model calls, prevents unauthorized access to law enforcement and monitoring data for this category |
Three Common Mistakes
- Phenomenon: Model output content is truncated, displayed as a fragment of around 4000 tokens. Cause: The
maxTokensparameter is not adjusted, the default 4000 token limit is used, which cannot support complete Q&A content for policy basis and law enforcement data. - Phenomenon: Model channel configuration error during local deployment, prompt for connection timeout or invalid key. Cause: The
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are not filled correctly, or the local network cannot access the specified model proxy address. - Phenomenon: Global variables in the workflow fail to correctly obtain the return value of the API access link, causing model call failure. Cause: The assignment logic of the API access link is not configured correctly, and the returned interface address is not bound to the corresponding global variable.
How to Verify Successful Configuration
- Initiate a test Q&A that includes multiple law enforcement documents and monitoring data, check whether the model output completely covers the required policy basis content, with no truncation.
- Initiate a test call for local deployment, check whether Q&A results can be returned normally, with no connection error prompts.
- Initiate a Q&A for a specific law enforcement scenario, check whether all retrieved documents are valid data from the corresponding monitoring points and law enforcement periods, with no irrelevant content mixed in.
- View the model call log, confirm that the actual values of the core configuration parameters match the preset configuration, with no overwriting by default parameters.
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.