This page is written for FastGPT v4.17.0.
What this category of data looks like
Data for this category typically comes from historical traffic data, service topology, monitoring metrics, and business growth plans. Daily produced documents include technical solutions, architecture diagrams, capacity assessment reports, and technical review minutes. These documents mark structured fields such as cross-service boundary configurations, long-term evolution constraints, and resource allocation thresholds. Update frequency is usually monthly or triggered by major requirements. There are no fixed statistical values or percentages; only architecture decisions and associated business parameters are recorded in a structured manner.
What constraints do these characteristics impose on the "model access and configuration" link
Data in this category has a low update frequency and relies on historical service topology and business growth plans. Model access must adapt to non-frequent but long-cycle document parsing requirements. Documents contain multiple types of content such as architecture diagrams and review minutes. Configuration must support multi-modal input and structured information extraction. Architecture decisions are tied to long-term system evolution, so model output must remain consistent to avoid deviations in cross-service boundary configurations caused by parameter fluctuations. Data sources are mostly internal operation and business planning documents. Model call permissions and compliance must align with internal management processes.
How to set the configuration
| Config Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 4096–8192 token | Adapt to multi-segment splicing needs of architecture documents, avoid loss of key constraint conditions due to context truncation |
maxTokens | 2000–3000 token | Control single-round output length, adapt to structured extraction needs of technical solution review minutes |
vision | Enabled | Support parsing visual document content such as architecture diagrams and capacity assessment charts |
temperature | 0.2–0.4 | Reduce output randomness, ensure consistency of compliance review results related to architecture decisions |
AIPROXY_API_ENDPOINT | Calibrated according to deployment environment | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not add path suffixes such as /v1 or /chat/completions, and do not configure it as a single-model service address |
AIPROXY_API_TOKEN | Calibrated according to deployment environment | AI Proxy administrator Token, consistent with the AI Proxy server configuration and cannot be empty. Do not use application API Keys, model vendor keys, or other business authentication Tokens |
Three Common Mistakes
- Optional models in the interface do not match official introductions, and switching to GPT-4 or Claude 3.5 is not possible. Troubleshooting steps: Check whether the server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. Check whether the corresponding models are enabled in the upstream model channel configuration. - Large models fail to stably output Markdown-formatted review results. Cause: The
temperatureparameter is set too high, leading to random fluctuations in output format that do not match the parsing requirements of structured documents. - A call failure prompt appears after configuring the oneAPI Kimi key. Troubleshooting steps: Check whether the oneAPI key in the upstream model channel configuration is correct. Check whether the server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmeet the requirements.
How to confirm the configuration is complete
- Call the model interface for testing, verify that uploaded architecture diagram documents can be parsed normally, and confirm that the
visionconfiguration takes effect. - Submit technical review minutes for review, check the format stability of the output results, and confirm that the
temperatureparameter value meets expectations. - View the proxy logs, confirm that the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the deployment environment, and there are no network errors. - Switch the optional model list, confirm that newly added models such as GPT-4 and Claude 3.5 have been enabled in the configuration and can be selected normally.
The values provided are common starting points and should be evaluated against local 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.