deepset-ai--haystack/docs-website/versioned_docs/version-2.30/pipeline-components/embedders/twelvelabstextembedder.mdx

---
title: "TwelveLabsTextEmbedder"
id: twelvelabstextembedder
slug: "/twelvelabstextembedder"
description: "This component transforms a string into a vector using the TwelveLabs Marengo multimodal embedding model. Because Marengo embeds text, images, audio, and video into one shared vector space, the resulting embeddings support cross-modal retrieval. Use this component to embed a query before searching with an embedding Retriever."
---

# TwelveLabsTextEmbedder

This component transforms a string into a vector using the TwelveLabs Marengo multimodal embedding model. Because Marengo embeds text, images, audio, and video into one shared vector space, the resulting embeddings support cross-modal retrieval (for example, searching a video collection with a text query). Use this component to embed a query before searching with an embedding Retriever.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline |
| **Mandatory init variables** | `api_key`: The TwelveLabs API key. Can be set with `TWELVELABS_API_KEY` env var. |
| **Mandatory run variables** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [TwelveLabs](/reference/integrations-twelvelabs) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/twelvelabs |
| **Package name** | `twelvelabs-haystack` |

</div>

## Overview

`TwelveLabsTextEmbedder` embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the [`TwelveLabsDocumentEmbedder`](twelvelabsdocumentembedder.mdx), which enriches each document with the computed embedding. The default model is `marengo3.0`.

Because Marengo embeds into a single shared space, embeddings produced from text are directly comparable (cosine similarity) with embeddings of images, audio, and video from the same model.

To start using this integration with Haystack, install the package with:

```shell
pip install twelvelabs-haystack
```

The component uses a `TWELVELABS_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```python
from haystack.utils import Secret
from haystack_integrations.components.embedders.twelvelabs import TwelveLabsTextEmbedder

embedder = TwelveLabsTextEmbedder(api_key=Secret.from_token("<your-api-key>"))
```

To get an API key, head to [playground.twelvelabs.io](https://playground.twelvelabs.io).

## Usage

### On its own

Here is how you can use the component on its own:

```python
from haystack_integrations.components.embedders.twelvelabs import TwelveLabsTextEmbedder

text_embedder = TwelveLabsTextEmbedder()

result = text_embedder.run(text="a cat playing piano")
print(result["embedding"])

# [-0.043398008, -0.025287028, -0.0061081843, ...]
print(result["meta"])

# {'model': 'marengo3.0'}
```

:::info
We recommend setting `TWELVELABS_API_KEY` as an environment variable instead of setting it as a parameter.
:::

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack_integrations.components.embedders.twelvelabs import (
    TwelveLabsDocumentEmbedder,
    TwelveLabsTextEmbedder,
)

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="a cat playing piano"),
    Document(content="a dog catching a frisbee at the beach"),
    Document(content="a timelapse of a city skyline at night"),
]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", TwelveLabsDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")
indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", TwelveLabsTextEmbedder())
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

result = query_pipeline.run({"text_embedder": {"text": "feline making music"}})
print(result["retriever"]["documents"][0].content)

# a cat playing piano
```