What This Category of Data Looks Like
This category of data primarily comes from system inventories, security device logs, and project interview records exported by assessment tools. Update frequency is once per project or annually. Core documents include classification determination reports, gap analysis reports, rectification plans, and assessment reports. Fields cover cybersecurity level protection control item numbers, corresponding rectification measures, evidence file paths, assessment results, and more. Total document volume per project fluctuates with the size of the assessed system, with no unified fixed single-document size limit.
Constraints Imposed on Context and Token Workflows by These Data Characteristics
These data characteristics impose multiple constraints on context and token workflows. First, total document volume per project fluctuates with system scale. The data includes structured control items, unstructured logs, and rectification evidence. The most time-consuming steps of sorting classification determination materials and verifying control items require recalling a large number of related documents. Precisely control the total token count for context recall to avoid exceeding model capacity limits. Second, the data update cycle is once per project or annually. Configure context to support recall of full project documents without frequent adjustments. Additionally, compliance requirements mandate that context must cover complete control item-related evidence, and critical judgment content cannot be truncated. This places higher requirements on precise token allocation.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Supports recall of full project documents, covering control items and evidence required for cybersecurity level protection assessment |
quoteMaxToken | 1500–2000 token | Matches knowledge base chunking and single-reference compliance requirements, preventing truncation of critical rectification evidence |
| Context recall count | Top 3–5 entries | Balances document coverage and token usage, avoiding exceeding model context limits |
| Context reset rule | Automatically execute when switching projects | Aligns with the project-based business model of this category, preventing cross-project data contamination |
| Token statistics node | Count after project completion | Matches the project-based data update rhythm, eliminating the need for real-time token consumption statistics |
Three Common Configuration Errors
- Symptom: A large response token limit is configured, but the token count for some question replies is only 200. Cause: Context token usage exceeds the
maxContextconfiguration, and the model automatically truncates the context, resulting in limited response length. - Symptom: The knowledge base chunk size is set to 5000 token, but only 1500 token of content is recalled during reference. Cause:
quoteMaxTokenis incorrectly set to 1500, which overrides the knowledge base chunking recall limit. - Symptom: Context functionality fails to start when deploying in cloud space, as the two server-side environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcannot be configured. Cause: Required server-side environment variable configuration requirements are not followed. The mandatoryAIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not set correctly, leading to startup verification failure.
How to Verify Successful Configuration
- Check the server-side environment variable configuration for FastGPT v4.17.0 or later. Confirm that
AIPROXY_API_ENDPOINT(the root address of the AI Proxy service, do not append /v1 or /chat/completions; this is not a single-model address for VLLM/Ollama/Xinference, etc.) andAIPROXY_API_TOKEN(the AI Proxy administrator token, consistent with the AI Proxy server-side configuration and cannot be empty; this is not an application API key, model vendor secret, application-issued token, or request-estimated token count) are set correctly, with no empty values or incorrect addresses. - Initiate a test call targeting an assessment report. Check whether the returned context token count falls within the
maxContextconfiguration range. - Review model invocation logs to confirm that the
quoteMaxTokenvalue is correctly applied to the reference content truncation logic. - Switch documents for different assessment projects. Confirm that context content is automatically reset, with no cross-project data residue.
The values listed are common starting points, and should be validated against samples from the target deployment context.
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.