> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embed

| Step | Fast | Description |
| - | - | - |
| [embed\_dataset](/api-docs/analyse/embed/embed_dataset) | | Reduce the dataset to an n-dimensional numeric vector embedding |
| [embed\_images](/api-docs/analyse/embed/embed_images) | | Embed images using pretrained DL models |
| [embed\_items](/api-docs/analyse/embed/embed_items) | | Trains an *item2vec* model on provided lists of items (or sentences of words, etc.) |
| [embed\_sessions](/api-docs/analyse/embed/embed_sessions) | | Trains an *item2vec* model on provided lists of items |
| [embed\_text](/api-docs/analyse/embed/embed_text) | | Parse and calculate a (word-averaged) embedding vector for each text |
| [embed\_text\_with\_model](/api-docs/analyse/embed/embed_text_with_model) | | Use language models to calulate an embedding for each text in provided column |
| [embed\_with\_trees](/api-docs/analyse/embed/embed_with_trees) | | Reduce the dataset to an n-dimensional numeric vector embedding using a Forest model's tree indices |
| [layout\_dataset](/api-docs/analyse/embed/layout_dataset) | | Reduce the dataset to 2 dimensions that can be mapped to x/y node positions |
| [vectorize\_dataset](/api-docs/analyse/embed/vectorize_dataset) | | Create a vectorized (numeric) dataset, (optionally) of reduced dimensionality |