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docs-website/docs/pipeline-components/embedders/mistraltextembedder.mdx

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+---
+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 |
+| **Package name** | `mistral-haystack` |
+
+</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.prompt", "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"])
+```