What the Data for This Category Looks Like
The data sources for this category include software asset management systems, procurement systems, terminal scanning tools, and vendor authorization files. Update frequency is monthly or quarterly. Daily deliverables include license ledgers, software usage lists, procurement contracts, and compliance audit reports. Data fields include authorization number, authorized license quantity, actual installed terminal count, authorization expiration date, vendor name, and other standard fields. No custom units are used. All fields use standardized text, integer, or date formats.
Constraints Imposed by These Characteristics on Model Integration and Configuration
Data sources for this category are scattered, and format standardization varies. Model integration must support unified parsing logic for multi-source data. Update frequency is monthly or quarterly. Model calls do not require high-frequency triggering, and can be paired with batch task scheduling. Diverse document types require the model to support parsing of multiple input formats. Standardized field structures simplify configuration of model extraction rules. Core business tasks including authorization verification and over-installation identification require model output structured results to strictly match preset field formats. Field missing or format errors must be avoided.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets context carrying requirements for long documents such as license ledgers and compliance audit reports, and avoids truncation of core authorization information |
maxTokens | 2000–3000 token | Meets structured output length requirements for tasks such as authorization quantity verification and over-installation identification, and avoids incomplete results |
toolChoice | auto | Allows the model to independently determine whether to call tools to complete comparison of batch authorization data |
temperature | 0.1–0.3 | Maintains consistency of authorization verification results, and reduces unnecessary format deviations |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Do not use single-model service addresses | Required environment variable for the App/Admin server, ensures correct AI Proxy request routing |
AIPROXY_API_TOKEN | Enter the administrator token consistent with the AI Proxy server configuration. Cannot be empty | Required environment variable for the App/Admin server, ensures identity legitimacy of AI Proxy requests and prevents unauthorized access |
Three Common Configuration Mistakes
- Symptom: App/Admin service startup verification fails, or model requests cannot be established normally. Cause: Required environment variables
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENare not configured on the server, resulting in failed request establishment. - Symptom: Multimodal visual model returns an error when calling image analysis tasks, prompting that the image cannot be downloaded. Cause: Image access permissions or network proxy for the model are not configured, preventing the model from obtaining software installation screenshots generated by terminal scanning.
- Symptom: After configuring
toolChoiceas auto, the model does not trigger tool calls to complete authorization quantity verification. Cause: The system prompt does not explicitly require the model to call tools to process batch authorization data, resulting in ineffective autonomous decision-making logic.
How to Verify Successful Configuration
- Initiate a single license ledger parsing test. Check whether the structured fields output by the model match the source data fields. Adjust corresponding parameters until the format meets business requirements.
- Trigger a batch over-installation identification task. View tool call logs. Confirm that the model autonomously triggers tool calls to complete batch verification of authorization quantities.
- Verify network connectivity for model requests. Check the validity of the configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENon the server. Ensure no connection errors occur. - Upload a software authorization credential image to the multimodal model. Confirm that the model can normally parse key authorization information in the image.
The values provided are common starting points, and should be evaluated 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.