Documentation Index
Fetch the complete documentation index at: https://docs.varios-ai.com/llms.txt
Use this file to discover all available pages before exploring further.
Embedding models convert text into vectors, enabling semantic search in knowledge bases. They are required for document processing and the Retrieval-Augmented Generation (RAG) approach.
Basic Data
| Field | Required | Description |
|---|
| Image / Title | Yes | Display name and optional profile image of the model (e.g. “Azure OpenAI - text-embedding-3-small”). |
| Model Name | Yes | Technical model name (e.g. text-embedding-3-small). |
| Credentials | Yes | Stored credentials for the selected provider (dropdown selection). |
Costs
| Field | Required | Description |
|---|
| Cost in $ per Million Output Tokens | No | Cost per one million output tokens for cost tracking. |
DLP Security Settings
GDPR-Compliant Data Protection
Configure how the model should handle personal data.
| Option | Description |
|---|
| enabled | GDPR protection is always active and cannot be disabled by users. |
| optional | GDPR protection is active by default but can be disabled by users. |
| disabled | GDPR protection is disabled. |
ICAP DLP Integration
Connect your data exit with your enterprise DLP solution.
| Option | Description |
|---|
| On | ICAP server is used for all content. |
| Off | ICAP server is not used. |
Embedding models have fewer configuration options compared to chat models,
as they are used exclusively for text vectorization and do not have direct
user interaction.
