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48 lines
1.2 KiB
Markdown
48 lines
1.2 KiB
Markdown
---
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title: Clarifai
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weight: 1200
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---
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# Using Clarifai Embeddings with Qdrant
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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.
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You can install the Clarifai Python client with pip:
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```bash
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pip install clarifai-client
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```
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## Integration Example
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```python
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import qdrant_client
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from qdrant_client.models import Batch
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from clarifai.rest import ClarifaiApp
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# Initialize Clarifai client
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clarifai_app = ClarifaiApp(api_key="<< your_api_key >>")
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# Choose the model for embeddings
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model = clarifai_app.public_models.general_embedding_model
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# Upload and get embeddings for an image
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image_path = "./path/to/the/image.jpg"
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response = model.predict_by_filename(image_path)
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# Extract the embedding from the response
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embedding = response['outputs'][0]['data']['embeddings'][0]['vector']
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# Initialize Qdrant client
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qdrant_client = qdrant_client.QdrantClient()
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# Upsert the embedding into Qdrant
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qdrant_client.upsert(
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collection_name="MyCollection",
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points=Batch(
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ids=[1],
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vectors=[embedding],
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)
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)
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```
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