What data in this category looks like
Typical stakeholders include customer success managers, training instructors and product operations teams. Daily produced documents include training slides, user manuals, training attendance sheets, Q&A lists and similar materials. Data sources include product help centers, customer organization structure information, training recordings and ticket systems. Update frequency is once per order or per version. Document structures cover structured tables, semi-structured demo content and unstructured text. Core fields include operation steps, permission descriptions, Q&A content and compliance clauses. No unified fixed unit format exists. Unlike the renewal health management category, this category focuses on building initial customer usage capabilities.
What constraints these characteristics impose on model access and configuration
The multi-source, high-frequency update and multi-structure characteristics of this category create multiple constraints for model access and configuration. First, multi-format documents covering structured tables, semi-structured demos and unstructured text require the model to support compliance review of cross-type content. Configure context parameters adapted for long texts. Second, high-frequency updates per order or per version require configuration items that support quick adjustments, with no complex underlying changes. Third, content involving permission descriptions and compliance clauses requires strict control over model output randomness and context length. This prevents leakage of customer sensitive information or omission of key compliance requirements. Fourth, errors directly impact customer launch conversion and ticket volume. Ensure configuration supports call stability, and reduces empty responses or incorrect review results.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | http://aiproxy:3000 | FastGPT server environment variable. Root address of the AI Proxy service. Conforms to the official format required for FastGPT v4.17.0. No /v1 or path suffixes are needed. This is not a single-model address such as VLLM/Ollama/Xinference. |
AIPROXY_API_TOKEN | Non-empty string matching the AI Proxy server configuration | FastGPT server environment variable. AI Proxy administrator token. Must match the AI Proxy server configuration and cannot be empty. Meets official mandatory validation requirements. Do not use application API keys, model vendor secrets, application-issued tokens or request-estimated token counts. |
maxContext | 8000–12000 token | Adapts to the long-text context length requirements of customer training manuals, slides and similar materials. Prevents truncation of key compliance content. |
maxTokens | 2000–3000 token | Controls the length of single-round review output. Ensures compliance review results are clear and complete. |
temperature | 0.1–0.3 | Reduces output randomness. Ensures consistency of security and compliance review results. |
quoteMaxToken | 1500 token | Limits the length of quoted document fragments. Focuses review on core compliance clauses. |
Three common configuration errors
- Phenomenon: The security and compliance review feature returns the LLMmodelresponseempty error, or occasional empty responses. Cause: FastGPT server environment variables `AIPROXYAPITOKEN
orAIPROXYAPI_ENDPOINT` are not set correctly, or upstream model channel configuration is abnormal. - Phenomenon: FastGPT service startup displays a validation failure prompt about missing required parameters. Cause: FastGPT server environment variables
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENare not set, or their values are empty strings. - Phenomenon: Security and compliance review results contain large amounts of irrelevant content or deviate from preset compliance requirements. Cause: The
temperatureparameter is set too high, leading to excessive model output randomness that does not meet review consistency requirements.
How to confirm configuration is complete
- Check FastGPT server startup logs to confirm there are no error messages about missing
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN. - Upload a typical customer user manual document to trigger a security and compliance review task, and verify that valid review results are generated.
- Adjust the
temperatureparameter to different ranges, compare the consistency of review results, and confirm the parameter configuration takes effect. - Check the
maxContextparameter configuration to ensure uploaded long documents are not excessively truncated, and all compliance-related content is retained.
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 2026-10-19.