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This component computes the embeddings of a list of documents using the Mistral API and models.

Most common position in a pipelineBefore a DocumentWriter in an indexing pipeline
Mandatory input variables“documents”: A list of documents to be embedded
Output variables“documents”: A list of documents (enriched with embeddings)

“meta”: A dictionary of metadata strings

This component should be used to embed a list of Documents. To embed a string, use the MistralTextEmbedder.


MistralDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses the Mistral API and its embedding models.

The component currently supports the mistral-embed embedding model. The list of all supported models can be found in Mistral’s embedding models documentation.

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

pip install mistral-haystack

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

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


On its own

Remember first to set theMISTRAL_API_KEY as an environment variable or pass it in directly.

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

from haystack import Document
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder

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

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

result =[doc])
# [-0.453125, 1.2236328, 2.0058594, 0.67871094...]

In a pipeline

Below is an example of the MistralDocumentEmbedder in an indexing pipeline. We are indexing the contents of a webpage into an InMemoryDocumentStore.

from haystack import Pipeline
from haystack.components.converters import HTMLToDocument
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder

document_store = InMemoryDocumentStore()
fetcher = LinkContentFetcher()
converter = HTMLToDocument()
chunker = DocumentSplitter()
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="chunker", instance=chunker)
indexing.add_component(name="embedder", instance=embedder)
indexing.add_component(name="writer", instance=writer)

indexing.connect("fetcher", "converter")
indexing.connect("converter", "chunker")
indexing.connect("chunker", "embedder")
indexing.connect("embedder", "writer"){"fetcher": {"urls": [""]}})

Related Links

Check out the API reference in the GitHub repo or in our docs: