jina-embeddings-v4 | Jina AI

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

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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.

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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.

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Installation

pip install pinecone requests

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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)

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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"
)

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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