---
title: "WatsonxTextEmbedder"
id: watsonxtextembedder
slug: "/watsonxtextembedder"
description: "When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents."
---

# WatsonxTextEmbedder

When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

<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`: An IBM Cloud API key. Can be set with `WATSONX_API_KEY` env var.  <br /> <br />`project_id`: An IBM Cloud project ID. Can be set with `WATSONX_PROJECT_ID` 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** | [Watsonx](/reference/integrations-watsonx) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx |

</div>

## Overview

To see the list of compatible IBM watsonx.ai embedding models, head over to IBM [documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The default model for `WatsonxTextEmbedder` is `ibm/slate-30m-english-rtrvr`. You can specify another model with the `model` parameter when initializing this component.

Use `WatsonxTextEmbedder` to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the [`WatsonxDocumentEmbedder`](watsonxdocumentembedder.mdx), which enriches the document with the computed embedding, also known as vector.

The component uses `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` environment variables by default. Otherwise, you can pass API credentials at initialization with `api_key` and `project_id`:

```python
embedder = WatsonxTextEmbedder(
    api_key=Secret.from_token("<your-api-key>"),
    project_id=Secret.from_token("<your-project-id>"),
)
```

## Usage

Install the `watsonx-haystack` package to use the `WatsonxTextEmbedder`:

```shell
pip install watsonx-haystack
```

### On its own

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

```python
from haystack_integrations.components.embedders.watsonx.text_embedder import (
    WatsonxTextEmbedder,
)
from haystack.utils import Secret

text_to_embed = "I love pizza!"

text_embedder = WatsonxTextEmbedder(
    api_key=Secret.from_env_var("WATSONX_API_KEY"),
    project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
    model="ibm/slate-30m-english-rtrvr",
)

print(text_embedder.run(text_to_embed))

## {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
## 'meta': {'model': 'ibm/slate-30m-english-rtrvr',
## 'truncated_input_tokens': 3}}
```

:::info
We recommend setting WATSONX_API_KEY and WATSONX_PROJECT_ID as environment variables instead of setting them as parameters.
:::

### In a pipeline

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.watsonx.text_embedder import (
    WatsonxTextEmbedder,
)
from haystack_integrations.components.embedders.watsonx.document_embedder import (
    WatsonxDocumentEmbedder,
)
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = WatsonxDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

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

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])

## Document(id=..., mimetype: 'text/plain',
## text: 'My name is Wolfgang and I live in Berlin')
```