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Using Clarifai Embeddings with Qdrant

Clarifai is a leading provider of visual embeddings, which are particularly strong in image and video analysis. Clarifai offers an API that allows you to create embeddings for various media types, which can be integrated into Qdrant for efficient vector search and retrieval.

You can install the Clarifai Python client with pip:

pip install clarifai-client

Integration Example

import qdrant_client
from qdrant_client.models import Batch
from clarifai.rest import ClarifaiApp

# Initialize Clarifai client
clarifai_app = ClarifaiApp(api_key="<< your_api_key >>")

# Choose the model for embeddings
model = clarifai_app.public_models.general_embedding_model

# Upload and get embeddings for an image
image_path = "./path/to/the/image.jpg"
response = model.predict_by_filename(image_path)

# Extract the embedding from the response
embedding = response['outputs'][0]['data']['embeddings'][0]['vector']

# Initialize Qdrant client
qdrant_client = qdrant_client.QdrantClient()

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

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