Update README.md

README.md

@@ -158,9 +158,69 @@ This dual capability makes it an excellent tool for multimodal retrieval-augment
 
 ## Usage
 
-1. The easiest way to start using jina-clip-v2 is via Jina AI's [Embeddings API](https://jina.ai/embeddings/).
-2. Alternatively, you can use the model directly via the transformers/sentence-transformers package.
+<details>
+  <summary>via Jina AI [Embedding API](https://jina.ai/embeddings/)<</summary>
+
+```bash
+curl https://api.jina.ai/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer [JINA_AI_API_TOKEN]" \
+  -d @- <<EOFEOF
+  {
+    "model": "jina-clip-v2",
+    "dimensions": 1024,
+    "task": "retrieval.query",
+    "normalized": true,
+    "embedding_type": "float",
+    "input": [
+        {
+            "text": "غروب جميل على الشاطئ"
+        },
+        {
+            "text": "海滩上美丽的日落"
+        },
+        {
+            "text": "A beautiful sunset over the beach"
+        },
+        {
+            "text": "Un beau coucher de soleil sur la plage"
+        },
+        {
+            "text": "Ein wunderschöner Sonnenuntergang am Strand"
+        },
+        {
+            "text": "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία"
+        },
+        {
+            "text": "समुद्र तट पर एक खूबसूरत सूर्यास्त"
+        },
+        {
+            "text": "Un bellissimo tramonto sulla spiaggia"
+        },
+        {
+            "text": "浜辺に沈む美しい夕日"
+        },
+        {
+            "text": "해변 위로 아름다운 일몰"
+        },
+        {
+            "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
+        },
+        {
+            "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
+        }
+    ]
+  }
+EOFEOF
+```
+
+</details>
+
+
+<details>
+  <summary>via transformers:<</summary>
 
+```python
 ```python
 # !pip install transformers einops timm pillow
 from transformers import AutoModel
@@ -170,20 +230,20 @@ model = AutoModel.from_pretrained("jinaai/jina-clip-v2", trust_remote_code=True)
 
 # Corpus
 sentences = [
-    "طاهٍ يطبخ المعكرونة في المطبخ", # Arabic
-    "厨师在厨房煮意大利面", # Chinese
-    "Un chef qui cuisine des pâtes dans la cuisine", # French
-    "Ein Koch, der in der Küche Pasta kocht", # German
-    "Ένας σεφ μαγειρεύει ζυμαρικά στην κουζίνα", # Greek
-    "एक शेफ रसोई में पास्ता पका रहा है", # Hindi
-    "Uno chef che cucina la pasta in cucina", # Italian
-    "シェフがキッチンでパスタを作っている", # Japanese
-    "셰프가 주방에서 파스타를 요리하고 있다", # Korean
+    "غروب جميل على الشاطئ", # Arabic
+    "海滩上美丽的日落", # Chinese
+    "Un beau coucher de soleil sur la plage", # French
+    "Ein wunderschöner Sonnenuntergang am Strand", # German
+    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία", # Greek
+    "समुद्र तट पर एक खूबसूरत सूर्यास्त", # Hindi
+    "Un bellissimo tramonto sulla spiaggia", # Italian
+    "浜辺に沈む美しい夕日", # Japanese
+    "해변 위로 아름다운 일몰", # Korean
 ]
 
 
 # Public image URLs or Pil
-image_urls = ["https://i.ibb.co/bRGGJxD/DALL-E-2024-11-20-13-44-46-A-highly-realistic-8-K-photographic-image-of-a-chef-cooking-pasta-in-a-mo.webp"]
+image_urls = ["https://i.ibb.co/nQNGqL0/beach1.jpg", "https://i.ibb.co/r5w8hG8/beach2.jpg"]
 # Choose a matryoshka dimension, set to None to get the full 1024-dim vectors
 truncate_dim = 512
 
@@ -194,13 +254,15 @@ image_embeddings = model.encode_image(
 )  # also accepts PIL.image, local filenames, dataURI
 
 # Encode query text
-query = "A chef cooking pasta in the kitchen" # English
+query = "beautiful sunset over the beach" # English
 query_embeddings = model.encode_text(
-    query, task="retrieval.query", truncate_dim=truncate_dim
+    query, task='retrieval.query', truncate_dim=truncate_dim
 )
 
 # text to image
 print("En -> Img: " + str(query_embeddings @ image_embeddings[0].T))
+# image similarity
+print("Img -> Img: " + str(image_embeddings[0] @ image_embeddings[1].T))
 # text to text
 print("En -> Ar: " + str(query_embeddings @ text_embeddings[0].T))
 print("En -> Zh: " + str(query_embeddings @ text_embeddings[1].T))
@@ -212,9 +274,13 @@ print("En -> It: " + str(query_embeddings @ text_embeddings[6].T))
 print("En -> Jp: " + str(query_embeddings @ text_embeddings[7].T))
 print("En -> Ko: " + str(query_embeddings @ text_embeddings[8].T))
 ```
+```
+</details>
 
-or via sentence-transformers:
 
+<details>
+  <summary>via sentence-transformers:<</summary>
+  
 ```python
 # !pip install sentence-transformers einops timm pillow
 from sentence_transformers import SentenceTransformer
@@ -227,26 +293,26 @@ model = SentenceTransformer(
 
 # Corpus
 sentences = [
-    "طاهٍ يطبخ المعكرونة في المطبخ", # Arabic
-    "厨师在厨房煮意大利面", # Chinese
-    "Un chef qui cuisine des pâtes dans la cuisine", # French
-    "Ein Koch, der in der Küche Pasta kocht", # German
-    "Ένας σεφ μαγειρεύει ζυμαρικά στην κουζίνα", # Greek
-    "एक शेफ रसोई में पास्ता पका रहा है", # Hindi
-    "Uno chef che cucina la pasta in cucina", # Italian
-    "シェフがキッチンでパスタを作っている", # Japanese
-    "셰프가 주방에서 파스타를 요리하고 있다", # Korean
+    "غروب جميل على الشاطئ", # Arabic
+    "海滩上美丽的日落", # Chinese
+    "Un beau coucher de soleil sur la plage", # French
+    "Ein wunderschöner Sonnenuntergang am Strand", # German
+    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία", # Greek
+    "समुद्र तट पर एक खूबसूरत सूर्यास्त", # Hindi
+    "Un bellissimo tramonto sulla spiaggia", # Italian
+    "浜辺に沈む美しい夕日", # Japanese
+    "해변 위로 아름다운 일몰", # Korean
 ]
 
 # Public image URLs or Pil
-image_urls = ["https://i.ibb.co/bRGGJxD/DALL-E-2024-11-20-13-44-46-A-highly-realistic-8-K-photographic-image-of-a-chef-cooking-pasta-in-a-mo.webp"]
+image_urls = ["https://i.ibb.co/nQNGqL0/beach1.jpg", "https://i.ibb.co/r5w8hG8/beach2.jpg"]
 
 text_embeddings = model.encode(sentences)
 image_embeddings = model.encode(image_urls)
-query = "A chef cooking pasta in the kitchen" # English
-query_embeddings = model.encode(query)
+query = "beautiful sunset over the beach" # English
+query_embeddings = model.encode(query)  
 ```
-
+</details>
 
 ## Contact