---
title: "MistralTextEmbedder"
id: mistraltextembedder
slug: "/mistraltextembedder"
description: "This component transforms a string into a vector using the Mistral API and models. Use it for embedding retrieval to transform your query into an embedding."
---

# MistralTextEmbedder

This component transforms a string into a vector using the Mistral API and models. Use it for embedding retrieval to transform your query into an embedding.

<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 Mistral API key. Can be set with `MISTRAL_API_KEY` env var. |
| **Mandatory run variables** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers (vectors)  <br /> <br />`meta`: A dictionary of metadata strings |
| **API reference** | [Mistral](/reference/integrations-mistral) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mistral |

</div>

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

## Overview

`MistralTextEmbedder` transforms a string into a vector that captures its semantics using a Mistral embedding model.

The component currently supports the `mistral-embed` embedding model. The list of all supported models can be found in Mistral’s [embedding models documentation](https://docs.mistral.ai/platform/endpoints/#embedding-models).

To start using this integration with Haystack, install it with:

```shell
pip install mistral-haystack
```

`MistralTextEmbedder` needs a Mistral API key to work. It uses a `MISTRAL_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```python
embedder = MistralTextEmbedder(
    api_key=Secret.from_token("<your-api-key>"),
    model="mistral-embed",
)
```

## Usage

### On its own

Remember to set the`MISTRAL_API_KEY` as an environment variable first or pass it in directly.

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

```python

from haystack_integrations.components.embedders.mistral.text_embedder import (
    MistralTextEmbedder,
)

embedder = MistralTextEmbedder(
    api_key=Secret.from_token("<your-api-key>"),
    model="mistral-embed",
)

result = embedder.run(text="How can I ise the Mistral embedding models with Haystack?")

print(result["embedding"])
## [-0.0015687942504882812, 0.052154541015625, 0.037109375...]
```

### In a pipeline

Below is an example of the `MistralTextEmbedder` in a document search pipeline. We are building this pipeline on top of an `InMemoryDocumentStore` where we index the contents of two URLs.

```python
from haystack import Document, Pipeline
from haystack.utils import Secret
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.mistral.document_embedder import (
    MistralDocumentEmbedder,
)
from haystack_integrations.components.embedders.mistral.text_embedder import (
    MistralTextEmbedder,
)
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

## Initialize document store
document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

## Indexing components
fetcher = LinkContentFetcher()
converter = HTMLToDocument()
embedder = MistralDocumentEmbedder()
writer = DocumentWriter(document_store=document_store)

indexing = Pipeline()
indexing.add_component(name="fetcher", instance=fetcher)
indexing.add_component(name="converter", instance=converter)
indexing.add_component(name="embedder", instance=embedder)
indexing.add_component(name="writer", instance=writer)

indexing.connect("fetcher", "converter")
indexing.connect("converter", "embedder")
indexing.connect("embedder", "writer")

indexing.run(
    data={
        "fetcher": {
            "urls": [
                "https://docs.mistral.ai/self-deployment/cloudflare/",
                "https://docs.mistral.ai/platform/endpoints/",
            ],
        },
    },
)

## Retrieval components
text_embedder = MistralTextEmbedder()
retriever = InMemoryEmbeddingRetriever(document_store=document_store)

## Define prompt template
prompt_template = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user(
        "Given the retrieved documents, answer the question.\nDocuments:\n"
        "{% for document in documents %}{{ document.content }}{% endfor %}\n"
        "Question: {{ query }}\nAnswer:",
    ),
]

prompt_builder = ChatPromptBuilder(
    template=prompt_template,
    required_variables={"query", "documents"},
)
llm = OpenAIChatGenerator(
    model="gpt-4o-mini",
    api_key=Secret.from_token("<your-api-key>"),
)

doc_search = Pipeline()
doc_search.add_component("text_embedder", text_embedder)
doc_search.add_component("retriever", retriever)
doc_search.add_component("prompt_builder", prompt_builder)
doc_search.add_component("llm", llm)

doc_search.connect("text_embedder.embedding", "retriever.query_embedding")
doc_search.connect("retriever.documents", "prompt_builder.documents")
doc_search.connect("prompt_builder.messages", "llm.messages")

query = "How can I deploy Mistral models with Cloudflare?"

result = doc_search.run(
    {
        "text_embedder": {"text": query},
        "retriever": {"top_k": 1},
        "prompt_builder": {"query": query},
    },
)

print(result["llm"]["replies"])
```