This guide shows how you can create text embeddings using the Marengo video understanding model. For a list of available versions, complete specifications and input requirements for each version, see the Marengo page.

The Marengo video understanding model generates embeddings for all modalities in the same latent space. This shared space enables any-to-any searches across different types of content.

For details on how your usage is measured and billed, see the Pricing page.

Key concepts

This section explains the key concepts and terminology used in this guide:

• Embedding: Vector representation of your content.

Workflow

This guide shows how to create text embeddings by passing text directly to the platform. The platform returns your embeddings immediately in the response.

Use these embeddings for similarity search, content classification, clustering, recommendations, or Retrieval-Augmented Generation (RAG).

Prerequisites

• To use the platform, you need an API key:

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  If you don’t have an account, sign up for a free account.

  2

  Go to the API Keys page.

  3

  If you need to create a new key, select the Create API Key button. Enter a name and set the expiration period. The default is 12 months.

  4

  Select the Copy icon next to your key to copy it to your clipboard.

• Depending on the programming language you are using, install the TwelveLabs SDK by entering one of the following commands:

  PythonNode.js

  $
  pip install twelvelabs

Complete example

Copy and paste the code below, replacing the placeholders surrounded by <> with your values.

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from twelvelabs import TwelveLabs, TextInputRequest
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# 1. Initialize the client
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client = TwelveLabs(api_key="<YOUR_API_KEY>")
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# 2. Create text embeddings
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response = client.embed.v_2.create(
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    input_type="text",
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    model_name="marengo3.0",
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    text=TextInputRequest(
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        input_text="<YOUR_TEXT>"
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    )
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)
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# 3. Process the results
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print(f"Number of embeddings: {len(response.data)}")
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for embedding_data in response.data:
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    print(f"Embedding dimensions: {len(embedding_data.embedding)}")
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    print(f"First 10 values: {embedding_data.embedding[:10]}")

Code explanation