FastGPT v4.17.0
What this category of data looks like
Data for this category comes from configuration parameters of servers, middleware, and security devices, as well as scan results from baseline tools. Update frequency is monthly or quarterly. Documents generated daily include baseline checklists, configuration audit records, rectification suggestions, and exception approval forms. Data fields include baseline item unique identifier, actual configuration item value, compliance judgment result, exception approval status, and rectification completion mark. No additional custom statistical fields are included.
What constraints do these characteristics impose on model access and configuration
This category’s data comes from static configuration parameters with low update frequency. High-frequency knowledge base synchronization is not required during model access. Reduce synchronization frequency to save resources. Baseline checklists and similar documents have fixed structures and clear fields. When calling the model, limit retrieval scope to only match preset baseline item-related fields. This avoids interference from irrelevant content. Exception approval forms contain unstructured text. Configure parameters to handle long text inputs. Data volume is relatively stable. Limit per-call token consumption to avoid exceeding model limits.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of documents such as baseline checklists and rectification suggestions, prevents truncation of key configuration item content |
maxTokens | 2000 token | Limits the length of a single model output, meets the generation requirements for rectification suggestions, avoids redundant content |
quoteMaxToken | 1500 characters | Limits the length of retrieved and referenced knowledge base content, matches the length of a single record in baseline audit records |
toolChoice | auto | Allows the model to independently choose whether to call tools to complete configuration item verification and exception reason extraction |
temperature | 0.2 | Reduces generation randomness, ensures consistency between rectification suggestions and baseline requirements |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service according to the deployment environment, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions | Required environment variable for the App/Admin backend, used to connect to the AI Proxy service |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy backend configuration, cannot be empty | Required environment variable for the App/Admin backend, used for identity verification with the AI Proxy service |
Three common misconfigurations
- Phenomenon: The rectification suggestions returned by the model do not reference the baseline checklist content, or the retrieval result is empty. Cause: The
quoteMaxTokenparameter is not configured correctly, causing the retrieved knowledge base content to be truncated or out of the matching range. - Phenomenon: The App/Admin backend fails startup verification, or cannot call the AI Proxy service normally. Cause:
AIPROXY_API_ENDPOINTis configured incorrectly, for example, it is mistakenly filled as a single model service address, appended with forbidden paths such as /v1, or the port configuration is wrong. - Phenomenon: The model context is reset after each MCP tool call, and historical baseline audit results cannot be associated. Cause: The
toolChoiceparameter is not set correctly, resulting in failure to retain context association after tool calls, or the configuredmaxContextlength is insufficient.
How to confirm the configuration is correct
- Submit test data for one baseline audit record, check whether the rectification suggestions returned by the model include the compliance judgment content of the corresponding baseline item.
- Access the configured
AIPROXY_API_ENDPOINTaddress, useAIPROXY_API_TOKENto initiate a test request, confirm that the returned status code is 200. - Initiate a test call containing exception approval form content, check whether the model can correctly extract exception reasons and generate corresponding rectification suggestions.
- Initiate 3 consecutive test calls for different baseline items, confirm that the model context is not accidentally reset, and each call result is generated based on historical context.
The values provided here are common starting points, and should be evaluated against deployment-specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.