What this category’s data looks like
This category of data originates from system inventories, security device logs, and project interview records exported by evaluation tools. Four core document types are produced regularly: classification rating reports, gap analysis reports, rectification plans, and evaluation reports. Each document is grouped by cybersecurity classified protection control items, and includes these fields: control item number, compliance determination result, rectification evidence link, project number, and evaluation date. Updates follow a per-project cycle: archive a project once it is completed, and refresh the project’s data during annual reviews.
Constraints imposed on model access and configuration
Multi-source data format differences require configuring model access to support parsing multiple document types. Control-item-based document structure requires the context window to accommodate long text fragments related to a single control item. Project-level update cycles require knowledge base synchronization configurations to support incremental pulls, rather than full refreshes. High compliance requirements for compliance reviews limit model generation randomness, while requiring tool call rule configurations to align with fixed cybersecurity classified protection standard determination logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long document fragments tied to a single control item within a project, preventing context truncation that loses critical compliance information |
temperature | 0.1–0.3 | Ensures strict alignment with cybersecurity classified protection standards for compliance reviews, reducing model generation randomness and minimizing determination deviations |
toolChoice | auto | Enables the model to independently decide whether to call the compliance matching tool, automatically linking cybersecurity classified protection control items to document content |
quoteMaxToken | 2000 token | Retains sufficient original reference fragments to allow tracing compliance determination results back to source materials |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths like /v1 or /chat/completions, and do not use single-model addresses such as VLLM/Ollama/Xinference | Required AI Proxy server environment variable, used to specify the AI Proxy service address to ensure correct model call routing |
This document applies to FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: After migrating a knowledge base, all documents are visible in the interface, but the model returns an empty knowledge base during conversations. Cause: The
maxContextparameter is not adjusted to match the total length of the migrated documents, so the context window cannot load full knowledge base content. - Phenomenon: Calling the MCP tool returns a 400 error when using a large language model deployed via Xinference, but functionality works normally without tool calls. Cause: The
toolChoiceparameter is set to force tool calls, but the target model does not support the tool call protocol, leading to mismatched request formats. - Phenomenon: An error occurs when running an initialized AI model or saving and publishing a newly created question classification node, and normal operation resumes after switching the AI model. Cause: The initialized model’s
maxTokensparameter is not configured to match the input length of the classification task, causing requests to exceed the model’s supported limits.
How to Verify Correct Configuration
- Upload a single cybersecurity classified protection evaluation report, start a compliance review conversation, and confirm that the model’s returned determination result includes original text reference fragments.
- Set
toolChoiceto auto, run a query that includes a control item number, and confirm that the model automatically links to the corresponding document fragment. - Validate that the server-configured
AIPROXY_API_ENDPOINTis correct, with no connection errors. - Adjust the
temperatureparameter to any value within the recommended range, run multiple rounds of review conversations, and confirm that generated results have no noticeable random deviations.
The values provided here are common starting points and should be evaluated against suitable sample datasets.
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.