Create Embeddings

Description

Get a vector representation of a given input that can be easily consumed by machine learning models and other algorithms. This activity is intended to be used in conjunction with a vector database and/or the embedded model for the text and chat completion.

Project compatibility

Windows | Cross-platform

Configuration

• Connection ID - The connection established in Integration Service. Access the drop-down menu to choose, add, or manage connections.

• Model - The model to generate embeddings. Select an option from the available drop-down list.
• Input - The text input for generating embeddings. The number of input tokens varies depending on what model you are using.

Manage Properties

Use the Manage Properties wizard to configure or use any of the object's standard or custom fields. You can select fields to add them to the activity canvas. The added standard or custom fields are available in the Properties panel (in Studio Desktop) or under Show additional options (in Studio Web).

Additional options

• API version - The API version to use for this operation. This follows the YYY-MM-DD format.
• User - A unique identifier representing your end-user. This helps Azure OpenAI monitor and detect abuse.

Output

• Embeddings - An information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. Automatically generated output variable.
• Create Embeddings - The created embeddings. Automatically generated output variable.