All functionality related to Nebius AI Studio
Nebius AI Studio provides API access to a wide range of state-of-the-art large language models and embedding models for various use cases.

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Installation and Setup

The Nebius integration can be installed via pip: 
pip install langchain-nebius
To use Nebius AI Studio, you’ll need an API key which you can obtain from Nebius AI Studio. The API key can be passed as an initialization parameter api_key or set as the environment variable NEBIUS_API_KEY. 
import os
os.environ["NEBIUS_API_KEY"] = "YOUR-NEBIUS-API-KEY"

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Available Models

The full list of supported models can be found in the Nebius AI Studio Documentation.

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Chat models

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ChatNebius

The ChatNebius class allows you to interact with Nebius AI Studio’s chat models. See a usage example. 
from langchain_nebius import ChatNebius

# Initialize the chat model
chat = ChatNebius(
    model="Qwen/Qwen3-30B-A3B-fast",  # Choose from available models
    temperature=0.6,
    top_p=0.95
)

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Embedding models

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NebiusEmbeddings

The NebiusEmbeddings class allows you to generate vector embeddings using Nebius AI Studio’s embedding models. See a usage example. 
from langchain_nebius import NebiusEmbeddings

# Initialize embeddings
embeddings = NebiusEmbeddings(
    model="BAAI/bge-en-icl"  # Default embedding model
)

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Retrievers

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NebiusRetriever

The NebiusRetriever enables efficient similarity search using embeddings from Nebius AI Studio. It leverages high-quality embedding models to enable semantic search over documents. See a usage example. 
from langchain_core.documents import Document
from langchain_nebius import NebiusEmbeddings, NebiusRetriever

# Create sample documents
docs = [
    Document(page_content="Paris is the capital of France"),
    Document(page_content="Berlin is the capital of Germany"),
]

# Initialize embeddings
embeddings = NebiusEmbeddings()

# Create retriever
retriever = NebiusRetriever(
    embeddings=embeddings,
    docs=docs,
    k=2  # Number of documents to return
)

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Tools

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NebiusRetrievalTool

The NebiusRetrievalTool allows you to create a tool for agents based on the NebiusRetriever. 
from langchain_nebius import NebiusEmbeddings, NebiusRetriever, NebiusRetrievalTool
from langchain_core.documents import Document

# Create sample documents
docs = [
    Document(page_content="Paris is the capital of France and has the Eiffel Tower"),
    Document(page_content="Berlin is the capital of Germany and has the Brandenburg Gate"),
]

# Create embeddings and retriever
embeddings = NebiusEmbeddings()
retriever = NebiusRetriever(embeddings=embeddings, docs=docs)

# Create retrieval tool
tool = NebiusRetrievalTool(
    retriever=retriever,
    name="nebius_search",
    description="Search for information about European capitals"
)