landing_page/qdrant-landing/content/documentation/embeddings/aleph-alpha.md

title, short_description, description, aliases

title	short_description	description	aliases
Aleph Alpha	Pair Aleph Alpha multilingual and multimodal embeddings with Qdrant to build cross-language and cross-modal semantic search over text and images.	Use Aleph Alpha embeddings with Qdrant to power multilingual and multimodal semantic search, mapping text and images into a single vector space.	/documentation/examples/aleph-alpha-search/ /documentation/tutorials/aleph-alpha-search/ /documentation/integrations/aleph-alpha/

Using Aleph Alpha Embeddings with Qdrant

Aleph Alpha is a multimodal and multilingual embeddings' provider. Their API allows creating the embeddings for text and images, both in the same latent space. They maintain an official Python client that might be installed with pip:

pip install aleph-alpha-client

There is both synchronous and asynchronous client available. Obtaining the embeddings for an image and storing it into Qdrant might be done in the following way:

import qdrant_client
from qdrant_client.models import Batch

from aleph_alpha_client import (
    Prompt,
    AsyncClient,
    SemanticEmbeddingRequest,
    SemanticRepresentation,
    ImagePrompt
)

aa_token = "<< your_token >>"
model = "luminous-base"

qdrant_client = qdrant_client.QdrantClient()
async with AsyncClient(token=aa_token) as client:
    prompt = ImagePrompt.from_file("./path/to/the/image.jpg")
    prompt = Prompt.from_image(prompt)

    query_params = {
        "prompt": prompt,
        "representation": SemanticRepresentation.Symmetric,
        "compress_to_size": 128,
    }
    query_request = SemanticEmbeddingRequest(**query_params)
    query_response = await client.semantic_embed(
        request=query_request, model=model
    )
    
    qdrant_client.upsert(
        collection_name="MyCollection",
        points=Batch(
            ids=[1],
            vectors=[query_response.embedding],
        )
    )

If we wanted to create text embeddings with the same model, we wouldn't use ImagePrompt.from_file, but simply provide the input text into the Prompt.from_text method.