Jina Embeddings

Qdrant is compatible with Jina AI embeddings. You can get a free trial key from Jina Embeddings to get embeddings.

Qdrant users can receive a 10% discount on Jina AI APIs by using the code QDRANT.

Technical Summary

Model	Dimension	Language	MRL (matryoshka)	Context
jina-clip-v2	1024	Multilingual (100+, focus on 30)	Yes	Text/Image
jina-embeddings-v3	1024	Multilingual (89 languages)	Yes	8192
jina-embeddings-v2-base-en	768	English	No	8192
jina-embeddings-v2-base-de	768	German & English	No	8192
jina-embeddings-v2-base-es	768	Spanish & English	No	8192
jina-embeddings-v2-base-zh	768	Chinese & English	No	8192

Jina recommends using jina-embeddings-v3 for text-only tasks and jina-clip-v2 for multimodal tasks or when enhanced visual retrieval is required.

On top of the backbone, jina-embeddings-v3 has been trained with 5 task-specific adapters for different embedding uses. Include task in your request to optimize your downstream application:

retrieval.query: Used to encode user queries or questions in retrieval tasks.
retrieval.passage: Used to encode large documents in retrieval tasks at indexing time.
classification: Used to encode text for text classification tasks.
text-matching: Used to encode text for similarity matching, such as measuring similarity between two sentences.
separation: Used for clustering or reranking tasks.

jina-embeddings-v3 and jina-clip-v2 support Matryoshka Representation Learning, allowing users to control the embedding dimension with minimal performance loss.
Include dimensions in your request to select the desired dimension.
By default, dimensions is set to 1024, and a number between 256 and 1024 is recommended.
You can reference the table below for hints on dimension vs. performance:

Dimension	32	64	128	256	512	768	1024
Average Retrieval Performance (nDCG@10)	52.54	58.54	61.64	62.72	63.16	63.3	63.35

jina-embeddings-v3 supports Late Chunking, the technique to leverage the model’s long-context capabilities for generating contextual chunk embeddings. Include late_chunking=True in your request to enable contextual chunked representation. When set to true, Jina AI API will concatenate all sentences in the input field and feed them as a single string to the model. Internally, the model embeds this long concatenated string and then performs late chunking, returning a list of embeddings that matches the size of the input list.

Example

Jina Embeddings v3

The code below demonstrates how to use jina-embeddings-v3 with Qdrant:

import requests

import qdrant_client
from qdrant_client.models import Distance, VectorParams, Batch

# Provide Jina API key and choose one of the available models.
JINA_API_KEY = "jina_xxxxxxxxxxx"
MODEL = "jina-embeddings-v3"
DIMENSIONS = 1024 # Or choose your desired output vector dimensionality.
TASK = 'retrieval.passage' # For indexing, or set to retrieval.query for querying

# Get embeddings from the API
url = "https://api.jina.ai/v1/embeddings"

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {JINA_API_KEY}",
}

data = {
    "input": ["Your text string goes here", "You can send multiple texts"],
    "model": MODEL,
    "dimensions": DIMENSIONS,
    "task": TASK,
    "late_chunking": True,
}

response = requests.post(url, headers=headers, json=data)
embeddings = [d["embedding"] for d in response.json()["data"]]


# Index the embeddings into Qdrant
client = qdrant_client.QdrantClient(":memory:")
client.create_collection(
    collection_name="MyCollection",
    vectors_config=VectorParams(size= DIMENSIONS, distance=Distance.DOT),
)


qdrant_client.upsert(
    collection_name="MyCollection",
    points=Batch(
        ids=list(range(len(embeddings))),
        vectors=embeddings,
    ),
)

Jina CLIP v2

The code below demonstrates how to use jina-clip-v2 with Qdrant:

import requests
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

# Provide your Jina API key and choose the model.
JINA_API_KEY = "jina_xxxxxxxxxxx"
MODEL = "jina-clip-v2"
DIMENSIONS = 1024  # Set the desired output vector dimensionality.

# Define the inputs
text_input = "A blue cat"
image_url = "https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg"

# Get embeddings from the Jina API
url = "https://api.jina.ai/v1/embeddings"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {JINA_API_KEY}",
}
data = {
    "input": [
        {"text": text_input},
        {"image": image_url},
    ],
    "model": MODEL,
    "dimensions": DIMENSIONS,
}

response = requests.post(url, headers=headers, json=data)
response_data = response.json()["data"]

# The model doesn't differentiate between images and text, so we extract output based on the input order.
text_embedding = response_data[0]["embedding"]
image_embedding = response_data[1]["embedding"]

# Initialize Qdrant client
client = QdrantClient(url="http://localhost:6333/")

# Create a collection with named vectors
collection_name = "MyCollection"
client.recreate_collection(
    collection_name=collection_name,
    vectors_config={
        "text_vector": VectorParams(size=DIMENSIONS, distance=Distance.DOT),
        "image_vector": VectorParams(size=DIMENSIONS, distance=Distance.DOT),
    },
)

client.upsert(
    collection_name=collection_name,
    points=[
        PointStruct(
            id=0,
            vector={
                "text_vector": text_embedding,
                "image_vector": image_embedding,
            }
        )
    ],
)

# Now let's query the collection
search_query = "A purple cat"

# Get the embedding for the search query from the Jina API
url = "https://api.jina.ai/v1/embeddings"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {JINA_API_KEY}",
}
data = {
    "input": [{"text": search_query}],
    "model": MODEL,
    "dimensions": DIMENSIONS,
    # "task": "retrieval.query" # Uncomment this line for text-to-text retrieval tasks
}

response = requests.post(url, headers=headers, json=data)
query_embedding = response.json()["data"][0]["embedding"]

search_results = client.query_points(
    collection_name=collection_name,
    query=query_embedding,
    using="image_vector",
    limit=5
).points

for result in search_results:
    print(f"ID: {result.id}, Score: {result.score}")

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Jina Embeddings

Technical Summary

Example

Jina Embeddings v3

Jina CLIP v2

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