Nebius integrations

Integrate with Nebius using LangChain Python.

All functionality related to Nebius Token Factory

Nebius Token Factory provides API access to a wide range of state-of-the-art large language models and embedding models for various use cases.

Installation and setup

The Nebius integration can be installed via pip:

pip

pip install langchain-nebius

uv add langchain-nebius

To use Nebius Token Factory, you’ll need an API key which you can obtain from Nebius Token Factory. 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"

Available models

The full list of supported models can be found in the Nebius Token Factory Models Page.

Chat models

ChatNebius

The ChatNebius class allows you to interact with Nebius Token Factory’s chat models.

See a usage example.

from langchain_nebius import ChatNebius

# Initialize the chat model
chat = ChatNebius(
    model="moonshotai/Kimi-K2.5",  # Choose from available models
    temperature=0.6,
    top_p=0.95
)

Embedding models

NebiusEmbeddings

The NebiusEmbeddings class allows you to generate vector embeddings using Nebius Token Factory’s embedding models.

See a usage example.

from langchain_nebius import NebiusEmbeddings

# Initialize embeddings
embeddings = NebiusEmbeddings(
    model="Qwen/Qwen3-Embedding-8B"  # Default embedding model
)

Retrievers

NebiusRetriever

The NebiusRetriever enables efficient similarity search using embeddings from Nebius Token Factory. 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
)

Tools

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"
)

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