landing_page/qdrant-landing/content/documentation/embeddings/mistral.md

title, weight

title	weight
Mistral	700

Mistral

Qdrant is compatible with the new released Mistral Embed and its official Python SDK that can be installed as any other package:

Setup

Install the client

pip install mistralai

from mistralai.client import MistralClient

api_key = os.environ["MISTRAL_API_KEY"]
client = MistralClient(api_key=api_key)

Let's see how to use the Embedding Model API to embed a document for retrieval.

The following example shows how to embed a document with the models/embedding-001 with the retrieval_document task type:

Embedding a document

import pathlib
from mistralai.client import MistralClient
import qdrant_client

MISTRAL_API_KEY = "YOUR MISTRAL API KEY"  # add your key here

mistral_client = MistralClient(api_key=MISTRAL_API_KEY)

result = mistral_client.embeddings(
        model="mistral-embed", input=["Qdrant is the best vector search engine to use with Mistral"]
    )

The returned result is a dictionary with a key: embedding. The value of this key is a list of floats representing the embedding of the document.

Searching for documents with Qdrant

Once the documents are indexed, you can search for the most relevant documents using the same model with the retrieval_query task type:

qdrant_client.search(
    collection_name="MistralCollection",
    query=client.embeddings(
        model="mistral-embed", input=["What is the best to use with Mistral?"]
    )["embedding"],
)

Using Mistral Embedding Models with Binary Quantization

You can use Mistral Embedding Models with Binary Quantization - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much.

At an oversampling of 3 and a limit of 100, we've a 95% recall against the exact nearest neighbors with rescore enabled.

That's it! You can now use Mistral Embedding Models with Qdrant!