deepset-ai--haystack/docs-website/versioned_docs/version-2.18/pipeline-components/embedders/stackittextembedder.mdx

---
title: "STACKITTextEmbedder"
id: stackittextembedder
slug: "/stackittextembedder"
description: "This component enables text embedding using the STACKIT API."
---

# STACKITTextEmbedder

This component enables text embedding using the STACKIT API.

|                                        |                                                                                           |
| -------------------------------------- | ----------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline                 |
| **Mandatory init variables**           | "model": The model used through the STACKIT API                                           |
| **Mandatory run variables**            | “text”: A string                                                                          |
| **Output variables**                   | “embedding”: A list of float numbers                                                      |
| **API reference**                      | [STACKIT](/reference/integrations-stackit)                                                       |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/stackit |

## Overview

`STACKITTextEmbedder` enables text embedding models served by STACKIT through their API.

### Parameters

To use the `STACKITTextEmbedder`, ensure you have set a `STACKIT_API_KEY` as an environment variable. Alternatively, provide the API key as an environment variable with a different name or a token by setting `api_key` and using Haystack’s [secret management](../../concepts/secret-management.mdx).

Set your preferred supported model with the `model` parameter when initializing the component. See the full list of all supported models on the [STACKIT website](https://docs.stackit.cloud/stackit/en/models-licenses-319914532.html).

Optionally, you can change the default `api_base_url`, which is `"https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1"`.

You can pass any text generation parameters valid for the STACKIT Chat Completion API directly to this component with the `generation_kwargs` parameter in the init or run methods.

The component needs a text input to operate.

## Usage

Install the `stackit-haystack` package to use the `STACKITTextEmbedder` and set an environment variable called `STACKIT_API_KEY` to your API key.

```shell
pip install stackit-haystack
```

### On its own

```python
from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder

text_embedder = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")

print(text_embedder.run("I love pizza!"))

## {'embedding': [0.0215301513671875, 0.01499176025390625, ...]}
```

### In a pipeline

You can also use `STACKITTextEmbedder` in your pipeline.

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.stackit import (
    STACKITTextEmbedder,
    STACKITDocumentEmbedder,
)
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore()

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 = STACKITDocumentEmbedder(model="intfloat/e5-mistral-7b-instruct")
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

text_embedder = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")

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

query = "Where does Wolfgang live?"

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

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

## Document(id=..., content: 'My name is Wolfgang and I live in Berlin', score: ...)
```

You can find more usage examples in the STACKIT integration [repository](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/stackit/examples) and its [integration page](https://haystack.deepset.ai/integrations/stackit).