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

title, weight, aliases

title	weight	aliases
Aleph Alpha	900	../integrations/aleph-alpha/

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.