The guide to jina-embeddings-v2-small-en

Model
Jina AI / jina-embeddings-v2-small-en

Jina AI / jina-embeddings-v2-small-en

AI Model Milvus Integrated

Task: Embedding

Modality: Text

Similarity Metric: Any (Normalized)

License: Apache 2.0

Dimensions: 512

Max Input Tokens: 8192

Price: Free

Introduction to Jina Embedding v2 Models

Jina Embeddings v2 models are designed to handle long documents with an expanded max input size of 8,192 tokens. As of October 2024, Jina AI Embedding V2 has the following variants, each catering to different embedding needs:

Introduction to jina-embeddings-v2-small-en

jina-embeddings-v2-small-en is an English monolingual embedding model designed for a sequence length of up to 8192 tokens. It is the smallest variant in the Jina Embeddings v2 family, which has been trained with 33 million parameters and generates 512-dimensional embeddings.

Comparing jina-embeddings-v2-small-en with other Jina embedding models.

Model	Parameter Size	Embedding Dimension	Text
jina-embeddings-v3	570M	flexible embedding size (Default: 1024)	multilingual text embeddings; supports 94 language in total
jina-embeddings-v2-small-en	33M	512	English monolingual embeddings
jina-embeddings-v2-base-en	137M	768	English monolingual embeddings
jina-embeddings-v2-base-zh	161M	768	Chinese-English Bilingual embeddings
jina-embeddings-v2-base-de	161M	768	German-English Bilingual embeddings
jina-embeddings-v2-base-code	161M	768	English and programming languages

How to create embeddings with jina-embeddings-v2-small-en

There are two primary ways to generate vector embeddings:

PyMilvus: the Python SDK for Milvus that seamlessly integrates the jina-embeddings-v2-small-en model.
SentenceTransformer library: the Python library sentence-transformer.

Once the vector embeddings are generated, they can be stored in Zilliz Cloud (a fully managed vector database service powered by Milvus) and used for semantic similarity search. Here are four key steps:

Sign up for a Zilliz Cloud account for free.
Set up a serverless cluster and obtain the Public Endpoint and API Key.
Create a vector collection and insert your vector embeddings.
Run a semantic search on the stored embeddings.

Create embeddings via PyMilvus

from pymilvus.model.dense import SentenceTransformerEmbeddingFunction
from pymilvus import MilvusClient

ef = SentenceTransformerEmbeddingFunction("jinaai/jina-embeddings-v2-small-en", trust_remote_code=True)

docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]
# Generate embeddings for documents
docs_embeddings = ef(docs)

queries = ["When was artificial intelligence founded",
          "Where was Alan Turing born?"]
# Generate embeddings for queries
query_embeddings = ef(queries)

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=ef.dim,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])

For more information, refer to our PyMilvus Embedding Model documentation.

Create embeddings via sentence-transformer

from sentence_transformers import SentenceTransformer
from pymilvus import MilvusClient

model = SentenceTransformer("jinaai/jina-embeddings-v2-small-en", trust_remote_code=True)

docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]
# Generate embeddings for documents
docs_embeddings = model.encode(docs, normalize_embeddings=True)

queries = ["query: When was artificial intelligence founded",
           "query: Wo wurde Alan Turing geboren?" ] 
# Generate embeddings for queries
query_embeddings = model.encode(queries, normalize_embeddings=True)

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=512,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])