Environment Check and Confirmation

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Function Description

After the SERVICEME platform is deployed, the administrator needs to perform environment check and confirmation operations to ensure that model configuration, system dependencies, and authorization are all in a valid state.
This step is a key part of ensuring the stable operation of system functions such as document recognition, speech recognition, translation, RAG, etc.

Scope of Check

The environment check mainly includes the following modules:

Check Item	Description	Mandatory
Model Set	Check whether the standard supported models (GPT, Embedding, OCR, STT, etc.) are included.	Yes
Model Group	Check whether each model group is configured correctly and available.	Yes
Default Model Setting	Check whether the binding relationship between the default model and the scenario is correct.	Yes
System / ENV Environment Variables	Check key system variables and model connection status (such as the availability of OCR, Whisper, Embedding models).	Yes

Check Steps

Open Model Management

Go to Management > Model Management and check the following items in order:

Model Set

• Confirm whether the following standard models exist:
  • LLM
  • Embedding
• If missing, please contact the system administrator to re-import the model set.

Model Group

• Check whether model groups have been configured according to business scenarios, for example:
  • Chat / RAG / Translation / PDF Parsing / OCR, etc.
• Confirm that the models referenced in each model group are consistent with the actual supported range.

Default Model Setting

• Go to the "Default Model Setting" page and confirm the default bound models one by one (as in the following example):
  • translate → gpt-4.1-mini
  • gallery rednote → gpt-4.1-mini
  • recommend config → gpt-4.1-mini
  • gallery chat lead → gpt-4.1
  • optimize prompt → gpt-4.1
  • rag → gpt-4.1
  • i18n translation → gpt-4.1-mini
  • gallery mindmap → gpt-4.1-mini

Tip: For tasks with high computational requirements or high inference capability demands (such as knowledge retrieval, complex problem analysis, prompt optimization, etc.), it is recommended to prioritize models with stronger performance.

Tip: For lightweight scenarios (such as text translation, summary generation, daily copywriting, etc.), you can choose models with faster response and lower cost to balance performance and efficiency.

Common Issues and Solutions

Issue	Possible Cause	Solution
OCR call failed	API Key expired or not configured correctly	Update the key in the environment variables
Whisper no response	Model not enabled or server not deployed	Check model group configuration and deployment status
Default model setting is empty	License is incomplete or import failed	Confirm the License file and authorization scope
High call latency	Unstable network to external API	It is recommended to use model services in the same region as the deployment location