add embeddings

qdrant-landing/content/documentation/embeddings/clip.md

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+
+---
+title: Clip
+weight: 1300
+aliases:
+  - /documentation/examples/clip-search/
+  - /documentation/tutorials/clip-search/
+  - /documentation/integrations/clip/ 
+---
+
+# Using Clip with Qdrant 
+
+CLIP (Contrastive Language-Image Pre-Training) provides advanced AI capabilities including natural language processing and computer vision. CLIP is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install clip-client
+```
+## Integration Example
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+
+# Load the CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Load and process the image
+image = Image.open("path/to/image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Generate embeddings
+with torch.no_grad():
+    embeddings = model.get_image_features(**inputs).numpy().tolist()
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="ImageEmbeddings",
+    points=Batch(
+        ids=[1],
+        vectors=embeddings,
+    )
+)
+
+```
+