---
title: "STACKITDocumentEmbedder"
id: stackitdocumentembedder
slug: "/stackitdocumentembedder"
description: "This component enables document embedding using the STACKIT API."
---

# STACKITDocumentEmbedder

This component enables document embedding using the STACKIT API.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [DocumentWriter](../writers/documentwriter.mdx) in an indexing pipeline                   |
| **Mandatory init variables**           | `model`: The model used through the STACKIT API                                           |
| **Mandatory run variables**            | `documents`: A list of documents to be embedded                                           |
| **Output variables**                   | `documents`: A list of documents enriched with embeddings                                 |
| **API reference**                      | [STACKIT](/reference/integrations-stackit)                                                       |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/stackit |

</div>

## Overview

`STACKITDocumentEmbedder` enables document embedding models served by STACKIT through their API.

### Parameters

To use the `STACKITDocumentEmbedder`, 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.

Then component needs a list of documents as input to operate.

## Usage

Install the `stackit-haystack` package to use the `STACKITDocumentEmbedder` 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 STACKITDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = STACKITDocumentEmbedder(model="intfloat/e5-mistral-7b-instruct")

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

## [0.0215301513671875, 0.01499176025390625, ...]
```

### In a pipeline

You can also use `STACKITDocumentEmbedder` in your pipeline in a following way.

```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).