> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.twelvelabs.io/v1.3/sdk-reference/python/create-embeddings-v-2/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.twelvelabs.io/_mcp/server.

# Create embeddings v2

> Create vector embeddings for text, images, audio, video, and documents. Enable semantic search and content understanding.

Use the `EmbedClient.V2Client` class to create embeddings for text, images, audio, video, and documents. Embeddings are vector representations that enable semantic search and content understanding.

# Choose a method

* To embed a query for retrieving matching content, use the [`embed.v_2.create`](/v1.3/sdk-reference/python/create-embeddings-v-2/create-sync-embeddings#create-sync-embeddings) method. It returns the result immediately and accepts text, images, and audio or video up to 30 seconds with Marengo 3.5.
* To embed content at scale, such as the media files you want to make searchable, use the [`embed.v_2.tasks.create`](/v1.3/sdk-reference/python/create-embeddings-v-2/create-async-embeddings#create-an-async-embedding-task) method. It runs in the background and accepts audio, video, images, and documents.

> **Retention policy**
>
> Embeddings created with the asynchronous method are stored for seven days. After this, you must recreate them to obtain the results again.

> Create vector embeddings for text, images, audio, video, and documents. Enable semantic search and content understanding.

## Docs

- [Create sync embeddings](https://docs.twelvelabs.io/sdk-reference/python/create-embeddings-v-2/create-sync-embeddings.md): Create embeddings synchronously.
- [Create async embeddings](https://docs.twelvelabs.io/sdk-reference/python/create-embeddings-v-2/create-async-embeddings.md): Create embeddings asynchronously for audio, video, images, and documents.