Overview
jina-embeddings-v4 is a universal embedding model for multimodal and multilingual retrieval. The model is specially designed for complex document retrieval, including visually rich documents with charts, tables, and illustrations.
Features
jina-embeddings-v4 features:
- Unified embeddings for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval.
- Multilingual support (30+ languages) and compatibility with a wide range of domains, including technical and visually complex documents.
- Task-specific adapters for retrieval, text matching, and code-related tasks, which can be selected at inference time.
- Flexible embedding size: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.
Jina Embeddings v3 is the latest iteration in the Jina AI’s text embedding model series, building upon Jina Embedding v2. Key features include multilingual support for over 30 languages, task-oriented design using Low-Rank Adaptation (LoRa) instruction adapters, and Matryoshka Representation Learning (MRL) for flexible embedding generation. The model is built on a custom backbone based on XLM-Roberta with extended training and Rotary Position Embedding encoding, allowing it to supports up to 8,192 input tokens.
Installation
pip install pinecone requests
Create Index
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="API_KEY")
JINA_API_KEY = ""
dimension = 1024
index_name = "jina-embeddings-v4"
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'
)
)
index = pc.Index(index_name)
Embed & Upsert
from typing import literal, List
import requests
def get_embeddings(
texts: List[str],
dimensions: int,
task: Literal['text-matching', 'separation', 'classification', 'retrieval.query', 'retrieval.passage']):
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {JINA_API_KEY}'
}
data = {
'input': texts,
'model': 'jina-embeddings-v4',
'dimensions': dimensions,
'task': task
}
response = requests.post('https://api.jina.ai/v1/embeddings', headers=headers, json=data)
return response.json()
# Data to index
data = [
{"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
{"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
{"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
{"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
{"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
]
embeddings = get_embeddings([d["text"] for d in data], dimensions=dimension, task='retrieval.passage')
embeddings = [e["embedding"] for e in embeddings["data"]]
vectors = []
for d, e in zip(data, embeddings):
vectors.append({
"id": d['id'],
"values": e,
"metadata": {'text': d['text']}
})
index.upsert(
vectors=vectors,
namespace="ns1"
)
Query
query = "Tell me about the tech company known as Apple"
# Remember to keep query and document embedding to the same dimensions
x = get_embeddings([query], dimensions=dimension, task='retrieval.query')["data"][0]["embedding"]
results = index.query(
namespace="ns1",
vector=x,
top_k=3,
include_values=False,
include_metadata=True
)
print(results)
