Jina CLIP v2 is a state-of-the-art multilingual and multimodal (text-image) embedding model, It excels in both cross-modal (text-to-image, image-to-text) and unimodal (text-to-text) retrieval tasks within a single vector space. It supports 100 languages with a focus on 30 (including English, Spanish, Chinese, Arabic, and more), it supports flexible embedding generation through Matryoshka Representation Learning (MRL) and allows for shortened vector lengths via the dimensions parameter.
Installation
pip install pinecone requests
Create Index
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="API_KEY")
JINA_API_KEY = "" # Replace with your Jina API key
dimension = 1024 # Specify the desired embedding dimension
index_name = "jina-clip-v2"
if not pc.has_index(index_name):
pc.create_index(
name=index_name,
dimension=dimension,
metric="cosine",
spec=ServerlessSpec(
cloud='aws',
region='us-east-1' # Replace with your preferred region
)
)
index = pc.Index(index_name)
Embed & Upsert
from typing import List
import requests
def get_embeddings(
inputs: List[str], # List of text or image URLs
dimensions: 1024,
task: str = None # Set to 'retrieval.query' for text retrieval
):
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {JINA_API_KEY}'
}
data = {
'input': inputs,
'model': 'jina-clip-v2',
'dimensions': dimensions,
}
response = requests.post('https://api.jina.ai/v1/embeddings', headers=headers, json=data)
return response.json()
# Example data with image and text
data = [
{"id": "img1", "modality": "image", "content": "<https://example.com/image1.jpg>"},
{"id": "txt1", "modality": "text", "content": "A red apple on a table."},
{"id": "img2", "modality": "image", "content": "<https://example.com/image2.png>"},
{"id": "txt2", "modality": "text", "content": "A basket of green apples."},
]
vectors = []
for item in data:
embeddings = get_embeddings([item["content"]], dimensions=dimension)
embedding = embeddings["data"][0]["embedding"]
vectors.append({
"id": item['id'],
"values": embedding,
"metadata": {'content': item['content'], 'modality': item['modality']}
})
index.upsert(
vectors=vectors,
namespace="ns1" # optionally specify a namespace
)
Query
query = "Santa With Glasses" # Text query
embeddings = get_embeddings([query], dimensions=dimension, task='retrieval.query')
query_embedding = embeddings["data"][0]["embedding"]
results = index.query(
namespace="ns1",
vector=query_embedding,
top_k=3,
include_values=False,
include_metadata=True
)
print(results)
