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

title, weight

title	weight
Aleph Alpha	900

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 aleph_alpha_client import (
    Prompt,
    AsyncClient,
    SemanticEmbeddingRequest,
    SemanticRepresentation,
    ImagePrompt
)
from qdrant_client.http.models import Batch

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.