jina-embeddings-v4 | Jina AI

METRICcosine, dot product
DIMENSION128, 256, 512, 1024, 2048
MAX INPUT TOKENS32768
TASKembedding

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.

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)
Lorem Ipsum