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

title, short_description, description

title	short_description	description
MixedBread	Use MixedBread embeddings with Qdrant to build versatile semantic search and RAG systems across multiple domains and content types.	Generate MixedBread embeddings and store them in Qdrant to power smarter, faster semantic search across documents, code, and multi-domain knowledge bases.

Using MixedBread with Qdrant

MixedBread is a unique provider offering embeddings across multiple domains. Their models are versatile for various search tasks when integrated with Qdrant. MixedBread is creating state-of-the-art models and tools that make search smarter, faster, and more relevant. Whether you're building a next-gen search engine or RAG (Retrieval Augmented Generation) systems, or whether you're enhancing your existing search solution, they've got the ingredients to make it happen.

Installation

You can install the required package using the following pip command:

pip install mixedbread

Integration Example

Below is an example of how to obtain embeddings using MixedBread's API and store them in a Qdrant collection:

import qdrant_client
from qdrant_client.models import Batch
from mixedbread import MixedBreadModel

# Initialize MixedBread model
model = MixedBreadModel("mixedbread-variant")

# Generate embeddings
text = "MixedBread provides versatile embeddings for various domains."
embeddings = model.embed(text)

# Initialize Qdrant client
qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)

# Upsert the embedding into Qdrant
qdrant_client.upsert(
    collection_name="VersatileEmbeddings",
    points=Batch(
        ids=[1],
        vectors=[embeddings],
    )
)