This guide shows how you can create text embeddings.

The following table lists the available models for generating text embeddings and their key characteristics:

The “Marengo-retrieval-2.7” 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.

To create text embeddings, invoke the create method of the embed class specifying the following parameters:

• model_name: The name of the video understanding model you want to use.
• text: The text for which you want to create an embedding.
• (Optional) text_truncate: Specifies the behavior for text that exceeds 77 tokens. It can take one of the following values:
  • start: Truncate the beginning of the text.
  • end: Truncate the end of the text (default).
  • none: Return an error if the text exceeds the token limit.

The response is an object containing the following fields:

• model_name: The name of the model the platform has used to create this embedding.
• text_embedding: An object that contains the embedding.

For a description of each field in the request and response, see the Create embeddings for text, image, and audio page.

Prerequisites

• You’re familiar with the concepts that are described on the Platform overview page.
• To use the platform, you need an API key:
  1

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

  2

  Go to the API Key page.

  3

  Select the Copy icon next to your key.

Example

The example code below creates a text embedding using the default behavior for handling text that is too long. Ensure you replace the placeholders surrounded by <> with your values.

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from twelvelabs import TwelveLabs
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from typing import List
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from twelvelabs.models.embed import SegmentEmbedding
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def print_segments(segments: List[SegmentEmbedding], max_elements: int = 5):
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    for segment in segments:
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        print(f"  embeddings: {segment.embeddings_float[:max_elements]}")
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client = TwelveLabs(api_key="<YOUR_API_KEY>")
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res = client.embed.create(
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  model_name="Marengo-retrieval-2.7",
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  text="<YOUR_TEXT>",
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)
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print("Created a text embedding")
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print(f" Model: {res.model_name}")
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if res.text_embedding is not None and res.text_embedding.segments is not None:
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        print_segments(res.text_embedding.segments)

The output should look similar to the following one:

Created a text embedding
 Model: Marengo-retrieval-2.7
  embedding_scope=None start_offset_sec=None end_offset_sec=None
  embeddings: [, -, 0, ., 0, (truncated)]