
Name	MistralDocumentEmbedder
Type	Document Embedder
Path	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mistral
Position in a Pipeline	Before a `DocumentWriter` in an indexing Pipeline
Inputs	“documents”: a list of Document objects to be embedded
Outputs	“documents”: a list of Document objects (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.

Overview

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")

Usage

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 = embedder.run([doc])
print(result['documents'][0].embedding)
# [-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")

indexing.run(data={"fetcher": {"urls": ["https://mistral.ai/news/la-plateforme/"]}})