Nebius AI Studio provides API access to high-quality embedding models through a unified interface. The Nebius embedding models convert text into numerical vectors that capture semantic meaning, making them useful for various applications like semantic search, clustering, and recommendations.
OverviewThe NebiusEmbeddings class provides access to Nebius AI Studio's embedding models through LangChain. These embeddings can be used for semantic search, document similarity, and other NLP tasks requiring vector representations of text.
The Nebius integration can be installed via pip:
%pip install --upgrade langchain-nebius
Nebius requires an API key that can be passed as an initialization parameter api_key or set as the environment variable NEBIUS_API_KEY. You can obtain an API key by creating an account on Nebius AI Studio.
import getpass
import os
if "NEBIUS_API_KEY" not in os.environ:
os.environ["NEBIUS_API_KEY"] = getpass.getpass("Enter your Nebius API key: ")
The NebiusEmbeddings class can be instantiated with optional parameters for the API key and model name:
from langchain_nebius import NebiusEmbeddings
embeddings = NebiusEmbeddings(
model="BAAI/bge-en-icl"
)
The list of supported models is available at https://studio.nebius.com/?modality=embedding
Indexing and RetrievalEmbedding models are often used in retrieval-augmented generation (RAG) flows, both for indexing data and later retrieving it. The following example demonstrates how to use NebiusEmbeddings with a vector store for document retrieval.
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
docs = [
Document(
page_content="Machine learning algorithms build mathematical models based on sample data"
),
Document(page_content="Deep learning uses neural networks with many layers"),
Document(page_content="Climate change is a major global environmental challenge"),
Document(
page_content="Neural networks are inspired by the human brain's structure"
),
]
vector_store = FAISS.from_documents(docs, embeddings)
query = "How does the brain influence AI?"
results = vector_store.similarity_search(query, k=2)
print("Search results for query:", query)
for i, doc in enumerate(results):
print(f"Result {i + 1}: {doc.page_content}")
Search results for query: How does the brain influence AI?
Result 1: Neural networks are inspired by the human brain's structure
Result 2: Deep learning uses neural networks with many layers
You can also use the InMemoryVectorStore for lightweight applications:
from langchain_core.vectorstores import InMemoryVectorStore
text = "LangChain is a framework for developing applications powered by language models"
vectorstore = InMemoryVectorStore.from_texts(
[text],
embedding=embeddings,
)
retriever = vectorstore.as_retriever()
docs = retriever.invoke("What is LangChain?")
print(f"Retrieved document: {docs[0].page_content}")
Retrieved document: LangChain is a framework for developing applications powered by language models
You can directly use the NebiusEmbeddings class to generate embeddings for text without using a vector store.
You can use the embed_query method to embed a single piece of text:
query = "What is machine learning?"
query_embedding = embeddings.embed_query(query)
print(f"Embedding dimension: {len(query_embedding)}")
print(f"First few values: {query_embedding[:5]}")
Embedding dimension: 4096
First few values: [0.007419586181640625, 0.002246856689453125, 0.00193023681640625, -0.0066070556640625, -0.0179901123046875]
You can embed multiple texts at once using the embed_documents method:
documents = [
"Machine learning is a branch of artificial intelligence",
"Deep learning is a subfield of machine learning",
"Natural language processing deals with interactions between computers and human language",
]
document_embeddings = embeddings.embed_documents(documents)
print(f"Number of document embeddings: {len(document_embeddings)}")
print(f"Each embedding has {len(document_embeddings[0])} dimensions")
Number of document embeddings: 3
Each embedding has 4096 dimensions
NebiusEmbeddings supports async operations:
import asyncio
async def generate_embeddings_async():
query_result = await embeddings.aembed_query("What is the capital of France?")
print(f"Async query embedding dimension: {len(query_result)}")
docs = [
"Paris is the capital of France",
"Berlin is the capital of Germany",
"Rome is the capital of Italy",
]
docs_result = await embeddings.aembed_documents(docs)
print(f"Async document embeddings count: {len(docs_result)}")
await generate_embeddings_async()
Async query embedding dimension: 4096
Async document embeddings count: 3
import numpy as np
from scipy.spatial.distance import cosine
documents = [
"Machine learning algorithms build mathematical models based on sample data",
"Deep learning uses neural networks with many layers",
"Climate change is a major global environmental challenge",
"Neural networks are inspired by the human brain's structure",
]
embeddings_list = embeddings.embed_documents(documents)
def calculate_similarity(embedding1, embedding2):
return 1 - cosine(embedding1, embedding2)
print("Document Similarity Matrix:")
for i, emb_i in enumerate(embeddings_list):
similarities = []
for j, emb_j in enumerate(embeddings_list):
similarity = calculate_similarity(emb_i, emb_j)
similarities.append(f"{similarity:.4f}")
print(f"Document {i + 1}: {similarities}")
Document Similarity Matrix:
Document 1: ['1.0000', '0.8282', '0.5811', '0.7985']
Document 2: ['0.8282', '1.0000', '0.5897', '0.8315']
Document 3: ['0.5811', '0.5897', '1.0000', '0.5918']
Document 4: ['0.7985', '0.8315', '0.5918', '1.0000']
For more details about the Nebius AI Studio API, visit the Nebius AI Studio Documentation.
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