What This Category's Data Looks Like
Core data for this category comes from cloud bills, monitoring systems, business forecast reports, and stress test reports. Update frequency is typically weekly or monthly. Daily deliverables include capacity planning reports, scaling application forms, resource utilization reports, and budget calculation sheets. Data fields include peak business concurrency, node resource utilization rate, budget amount, number of scaled nodes, etc., with corresponding units of concurrency count, percentage, currency unit, and number of units. The data structure is relatively fixed, but the multi-source aggregation feature requires input formats to be compatible with file types exported by different systems.
What Constraints Do These Characteristics Impose on Model Access and Configuration
The source characteristics of multi-source data require model access configuration to support cross-format data parsing, to prevent the model from failing to read report content normally due to incompatible input formats. The weekly or monthly update frequency means the model call frequency should not be too high, and must match the update cycle of business documents. The fixed document structure requires that the model's output results must strictly match preset fields, otherwise they cannot be directly imported into subsequent approval and execution workflows. The time-consuming links of peak prediction and bottleneck positioning require that model configuration prioritize inference stability, to avoid deviations in capacity planning results caused by model errors. Unlike performance testing and capacity evaluation, the configuration of this category directly determines production resource allocation, so the accuracy requirements for model output are higher.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
maxContext | 8000–16000 token | Capacity planning data includes multi-week/month historical reports and current forecast content. A sufficient context window can fully carry all input information, and is compatible with the configuration logic of FastGPT v4.17.0 and above |
maxTokens | 2000–4000 token | The recommended content for capacity planning and scaling is usually structured analysis and solutions. This range can cover conventional output lengths |
quoteMaxToken | 4000 token | It is necessary to reference resource utilization rate and budget data from original reports as analysis basis. This upper limit can ensure the integrity of referenced content |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions paths. Do not configure it as a single-model address such as VLLM/Ollama/Xinference | This variable is an App/Admin server environment variable. Correct configuration ensures routing connectivity of the AI Proxy service |
AIPROXY_API_TOKEN | Non-empty administrator token consistent with the AI Proxy server configuration | This variable is an App/Admin server environment variable, used to verify the calling identity of the AI Proxy service and prevent unauthorized access. It must not be used as an application calling credential |
temperature | 0.3–0.7 | Balance the rigor and flexibility of analysis, avoid overly random or rigid output results |
Three Common Mistakes
- Symptom: 401 unauthorized error returned when calling the model. Cause: Incorrect
AIPROXY_API_TOKENconfiguration (inconsistent with AI Proxy server configuration or empty). This variable is an App/Admin server environment variable and must not be used as an application calling credential. - Symptom: The capacity analysis result output by the model does not match the fields in the resource utilization report, and cannot be directly imported into the scaling application form. Cause:
maxContextorquoteMaxTokenis not set, causing the model to fail to fully read all field information in the original report. - Symptom: Unable to find the local model access configuration entry. Cause: In FastGPT v4.9.9 and later versions, the local model configuration entry has been moved from the original sidebar to the model management module in system settings. Users who have not updated their operation path will not be able to find the entry.
How to Confirm Configuration is Complete
- Check whether the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables configured on the App/Admin server are correct. Initiate a test call to the AI Proxy service to verify connectivity and identity legitimacy. - Import a standard capacity planning report as input, and verify that the analysis result fields output by the model completely match the fields of the preset scaling application form and budget calculation sheet.
- Submit a stress test dataset simulating peak traffic, and confirm that the resource scaling recommendations output by the model fall within the reasonable range of business forecasts.
- Check the system operation logs to confirm that no error messages such as model call timeout, parameter limit exceeded, or format incompatibility appear.
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.