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

title, weight

title	weight
Mistral	700

Time: 10 min	Level: Beginner

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

And then we set this up:

from mistralai.client import MistralClient
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, VectorParams, Distance

collection_name = "example_collection"

MISTRAL_API_KEY = "your_mistral_api_key"
client = QdrantClient(":memory:")
mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
texts = [
    "Qdrant is the best vector search engine!",
    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
]

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

result = mistral_client.embeddings(
    model="mistral-embed",
    input=texts,
)

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

Converting this into Qdrant Points

points = [
    PointStruct(
        id=idx,
        vector=response.embedding,
        payload={"text": text},
    )
    for idx, (response, text) in enumerate(zip(result.data, texts))
]

Create a collection and Insert the documents

client.create_collection(collection_name, vectors_config=VectorParams(
        size=1024,
        distance=Distance.COSINE,
    )
)
client.upsert(collection_name, points)

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:

client.search(
    collection_name=collection_name,
    query_vector=mistral_client.embeddings(
        model="mistral-embed", input=["What is the best to use for vector search scaling?"]
    ).data[0].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.

Oversampling		1	1	2	2	3	3
	Rescore	False	True	False	True	False	True
Limit
10		0.53444	0.857778	0.534444	0.918889	0.533333	0.941111
20		0.508333	0.837778	0.508333	0.903889	0.508333	0.927778
50		0.492222	0.834444	0.492222	0.903556	0.492889	0.940889
100		0.499111	0.845444	0.498556	0.918333	0.497667	0.944556

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