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.
