Environment Check and Confirmation

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Function Description

After the SERVICEME platform deployment is completed, administrators need to perform environment check and confirmation operations to ensure that model configurations, system dependencies, and authorizations are all in a valid state.
This step is a key link to ensure 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 it contains models within the standard supported range (GPT, Embedding, OCR, STT, etc.).	Yes
Model Group	Check whether each model group is configured correctly and is available.	Yes
Default Model Setting	Check whether the default model and scenario binding relationship is correct.	Yes
System / ENV Environment Variables	Check key system variables and model connection status (such as 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, such as:
  • 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

• Enter the "Default Model Setting" page, and confirm the default bound models item by item (as in the example below):
  • 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 large computational load or high inference capability requirements (such as knowledge retrieval, complex problem analysis, prompt optimization, etc.), models with stronger performance should be prioritized;

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

Common Issues and Solutions

Issue	Possible Cause	Solution
OCR call failure	API Key invalid or not configured correctly	Update the key in environment variables again
Whisper no response	Model not enabled or server not deployed	Check model group configuration and deployment status
Default model setting is empty	License incomplete or import failed	Confirm License file and authorization scope
Call delay too high	Network instability when accessing external API	It is recommended to use model services in the same region as the deployment location