What data for this category looks like
Data for this category primarily comes from cloud management platforms, order systems, IAM permission systems, and billing systems. Updates are synced in real time for each order. Daily generated documents include resource activation forms, instance configuration tables, account permission forms, and delivery confirmation forms. Core fields include instance specifications, deployment regions, permission binding scopes, and billing-associated order numbers. Common units include cores (CPU/memory), GB (storage), tokens (context length), and others. There is no unified standardized format, with slight variations based on cloud service provider configurations. This category focuses on computing resource creation, and differs from core data dimensions of categories such as cloud network and load balancer configuration.
What constraints these characteristics impose on the model access and configuration workflow
Data for this category is scattered across multiple systems. When accessing the model, multi-source API connection permissions must be configured to ensure full-link data including orders, permissions, and billing can be pulled. The real-time update feature requires the model context synchronization frequency to match the order flow rhythm, to avoid using expired resource configuration information. The inconsistent document format requires the model to adapt to multi-field parsing rules, with parameters adjusted to meet long-text parsing requirements. Unit differences in core fields require the model to automatically identify and convert values. The generation logic rigor must be controlled to ensure accurate conclusions for test cases and defect analysis. Additionally, the category’s focus on computing resource creation requires the model to prioritize verification of core fields such as instance specifications and permission configurations when called, while ignoring verification logic for non-computing configurations.
The following configuration items are compatible with FastGPT v4.17.0.
How to configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Core documents for this category (resource activation forms, instance configuration tables, etc.) are mostly several thousand tokens in length per copy, requiring coverage of all order-associated data |
maxTokens | 2000 token | Outputs for test cases and defect analysis must include clear verification results. Excessively long outputs increase manual review costs |
quoteMaxToken | 1500 token | Original configuration fragments from cloud management platforms must be retained as supporting evidence for defect analysis |
toolChoice | auto | This scenario requires automatic calling of tool interfaces for order, IAM, and billing systems, with no need to manually specify called tools |
temperature | 0.3–0.5 | Test cases and defect analysis must maintain rigor, avoiding generation of unsupported verification conclusions |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration; cannot be empty | This variable is an environment variable for the App/Admin server, used for AI Proxy service identity verification, not a model vendor key or application API Key |
Three common configuration mistakes
- Phenomenon: Service startup verification failure, AI Proxy connection exception, or proxy authentication failure. Troubleshooting items include incorrect configuration of
AIPROXY_API_ENDPOINT(root address of the AI Proxy service, no /v1 or other paths required) andAIPROXY_API_TOKEN(administrator token consistent with the AI Proxy server configuration), or unopened outbound permissions for upstream model interfaces. - Phenomenon: Confusion of secrets when calling after configuring multiple models, leading to permission verification failure. Cause: Failure to bind model secrets by instance, and failure to distinguish configuration parameters for different models.
- Phenomenon: Redundant historical context data returned when a workflow node executes, interfering with current cloud host provisioning verification logic. Cause: Failure to limit the node configuration to only use the current session’s context, with incorrect enabling of global context synchronization.
How to verify correct configuration
- Execute a simulated test for single resource activation, check whether the test cases returned by the model cover the three core fields of instance specifications, permission configurations, and billing items, and confirm that the field matching logic is correct.
- View model call logs, confirm that the context length under the
maxContextparameter limit meets the configuration requirements, with no key information lost due to truncation. - Configure a multi-model scenario, switch vendor credentials configured for different model channels to initiate calls, confirm that the corresponding models can normally initiate requests, with no credential confusion issues.
- Trigger a test for workflow nodes, confirm that the nodes only use current session context data, with no global redundant information introduced.
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.