This guide shows how you can create text embeddings.

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

Model	Description	Dimensions	Max tokens	Similarity metric
Marengo-retrieval-2.7	Use this model to create embeddings that you can use in various downstream tasks	1024	77	Cosine similarity

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.

Prerequisites

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.

Ensure the TwelveLabs SDK is installed on your computer:

$ pip install twelvelabs

Complete example

This complete example shows how you can create image emebddings. Ensure you replace the placeholders surrounded by <> with your values.

1 from twelvelabs import TwelveLabs
2 from typing import List
3 from twelvelabs.models.embed import SegmentEmbedding
4 
5 # 1. Initialize the client
6 client = TwelveLabs(api_key="<YOUR_API_KEY>")
7 
8 # 2. Create text embeddings
9 res = client.embed.create(
10     model_name="Marengo-retrieval-2.7", text="<YOUR_TEXT>", text_truncate="start")
11 
12 # 3. Process the results
13 print(f"Created text embedding: model_name={res.model_name}")
14 def print_segments(segments: List[SegmentEmbedding], max_elements: int = 5):
15     for segment in segments:
16         print(f"  embeddings: {segment.embeddings_float[:max_elements]}")
17 if res.text_embedding is not None and res.text_embedding.segments is not None:
18     print_segments(res.text_embedding.segments)

Step-by-step guide

Python

Node.js

Import the SDK and initialize the client

Create a client instance to interact with the TwelveLabs Video Understanding platform.
Function call: You call the constructor of the TwelveLabs class.
Parameters:

api_key: The API key to authenticate your requests to the platform.

Return value: An object of type TwelveLabs configured for making API calls.

Create text embeddings

Function call: You call the embed.create function.
Parameters:

model_name: The name of the model you want to use (“Marengo-retrieval-2.7”).
(Optional) text_truncate: A string that specifies how the platform truncates text that exceeds 77 tokens to fit the maximum length allowed for an embedding. This parameter can take one of the following values:
- start: The platform will truncate the start of the provided text.
- end: The platform will truncate the end of the provided text. This is the default value.
- none: The platform will return an error if the text is longer than the maximum token limit. Return value: The response contains the following fields:
text_embedding: An object that contains the embedding data for your text. It includes the following fields:
- segments: An object that contains the following:
  - float: An array of floats representing the embedding
- metadata: An object that contains metadata about the embedding.
model_name: The name ofhe video understanding model the platform has used to create this embedding.

Process the results

This example prints the results to the standard output.