> ## Documentation Index > Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt > Use this file to discover all available pages before exploring further. # jina-embeddings-v4 > Use the jina-embeddings-v4 embedding or reranking model with Pinecone: specs and index setup. Overview export const ModelPageTabs = ({inferenceEnabled}) => { const showGuides = () => { document.querySelector('.model-page-guides').style.display = 'flex'; document.querySelector('.model-page-playground').style.display = 'none'; document.querySelector('.guides').classList.add('active'); document.querySelector('.playground').classList.remove('active'); }; const showPlayground = () => { document.querySelector('.model-page-guides').style.display = 'none'; document.querySelector('.model-page-playground').style.display = 'block'; document.querySelector('.guides').classList.remove('active'); document.querySelector('.playground').classList.add('active'); }; return
{false && }
; }; export const ModelPageCard = ({image, title, company, inferenceEnabled, metric, dimension, seq, task, price}) => { return
{image && image !== "null" &&
}

{title} {company !== "null" && "| " + company}

{inferenceEnabled === "true" &&
Hosted
}
{!!metric && metric.length > 0 &&
METRIC

{metric.join(", ")}

} {!!dimension && dimension.length > 0 &&
DIMENSION

{dimension.join(", ")}

} {!!seq && seq.length > 0 &&
MAX INPUT TOKENS

{seq}

} {!!task && task.length > 0 &&
TASK

{task}

} {price && price.length > 0 &&
PRICE

${price}

}
; }; export const ModelPageHeader = () => { return
Back to all models
; };
## Overview [`jina-embeddings-v4`](https://huggingface.co/jinaai/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 ```python theme={null} pip install pinecone requests ``` ## Create Index ```python theme={null} 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 ```python theme={null} 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 ```python theme={null} 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) ```